diff --git a/data_science/shashank_week9_xgboost/README.md b/data_science/shashank_week9_xgboost/README.md
new file mode 100644
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+++ b/data_science/shashank_week9_xgboost/README.md
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+# Childhood Obesity Classification – Lachesis (Shashank Samyal)
+
+This package contains my individual contribution to the Lachesis Capstone project:
+- **Preprocessing + Ethics** (age banding, BMI, sensitive attribute handling)
+- **Model training** (Random Forest baseline, XGBoost benchmark)
+- **Reproducible outputs** (metrics, confusion matrix, feature importances)
+- **Research** (Geospatial tools for public health; ethics/legality audit)
+
+## 📂 Structure
+final-upload/
+├─ data/ # official + supporting datasets (see privacy note)
+├─ notebooks/ # preprocessing + XGBoost training
+├─ outputs/ # saved metrics & plots (evidence)
+├─ scripts/ # script versions of the pipeline
+└─ reports/ # PDFs: model report + geospatial research
+
+
+## 🛠️ How to run
+1) Install deps:
+```bash
+pip install numpy pandas scikit-learn xgboost matplotlib
+Open notebooks:
+
+
+jupyter notebook notebooks/pre_processing.ipynb
+jupyter notebook notebooks/week9_xgboost.ipynb
+Data path:
+
+Place Final_combined_dataset.csv into data/ (or update CSV_PATH at the top of the notebook).
+
+📊 Results (Week 9, XGBoost)
+Test Accuracy: 0.8585
+
+CV Mean Accuracy: 0.8665 (±0.0073)
+
+Classes: 7, Features: 25
+Artifacts in outputs/:
+classification_report.csv, confusion_matrix.png, feature_importances_top.png, summary_metrics.csv, label_classes.csv.
+
+🧭 Geospatial Research
+See reports/Geospatial_Tools_Research.pdf for a comparison of ArcGIS, GeoPandas, Kepler.gl, and QGIS, with recommendations for Lachesis (analysis vs. visualisation vs. cost/integration).
+
+🔐 Privacy & Ethics
+No direct identifiers used.
+
+Age anonymised into bands; BMI derived from height/weight.
+
+See data/legality_ethics_audit_findings.csv and reports/Obesity_Model_Report.pdf.
+
+👤 Author
+Shashank Samyal — ML pipeline & ethics preprocessing (Distinction level submission).
+
+
diff --git a/data_science/shashank_week9_xgboost/data/Final_combined_dataset.csv b/data_science/shashank_week9_xgboost/data/Final_combined_dataset.csv
new file mode 100644
index 0000000..e673617
--- /dev/null
+++ b/data_science/shashank_week9_xgboost/data/Final_combined_dataset.csv
@@ -0,0 +1,3214 @@
+age,height,weight,family_history_with_overweight,favc,fcvc,ncp,smoke,ch2o,scc,faf,tue,gender_Male,caec_Always,caec_Frequently,caec_Sometimes,calc_Frequently,calc_Sometimes,calc_no,mtrans_Bike,mtrans_Motorbike,mtrans_Public_Transportation,mtrans_Walking,nobeyesdad
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diff --git a/data_science/shashank_week9_xgboost/data/legality_ethics_audit_findings.csv b/data_science/shashank_week9_xgboost/data/legality_ethics_audit_findings.csv
new file mode 100644
index 0000000..7dab9a7
--- /dev/null
+++ b/data_science/shashank_week9_xgboost/data/legality_ethics_audit_findings.csv
@@ -0,0 +1,4 @@
+Category,Columns found
+Direct identifiers,(none)
+Quasi-identifiers,age
+Sensitive fields,"height, weight"
diff --git a/data_science/shashank_week9_xgboost/data/obesity.csv b/data_science/shashank_week9_xgboost/data/obesity.csv
new file mode 100644
index 0000000..b75c5e7
--- /dev/null
+++ b/data_science/shashank_week9_xgboost/data/obesity.csv
@@ -0,0 +1,2112 @@
+Gender,Age,Height,Weight,family_history_with_overweight,FAVC,FCVC,NCP,CAEC,SMOKE,CH2O,SCC,FAF,TUE,CALC,MTRANS,NObeyesdad
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+Female,21,1.52,56,yes,no,3,3,Sometimes,yes,3,yes,3,0,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.8,77,yes,no,2,3,Sometimes,no,2,no,2,1,Frequently,Public_Transportation,Normal_Weight
+Male,27,1.8,87,no,no,3,3,Sometimes,no,2,no,2,0,Frequently,Walking,Overweight_Level_I
+Male,22,1.78,89.8,no,no,2,1,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,29,1.62,53,no,yes,2,3,Sometimes,no,2,no,0,0,Sometimes,Automobile,Normal_Weight
+Female,23,1.5,55,yes,yes,3,3,Sometimes,no,2,no,1,0,Sometimes,Motorbike,Normal_Weight
+Male,22,1.64,53,no,no,2,3,Sometimes,no,2,no,3,0,Sometimes,Public_Transportation,Normal_Weight
+Male,24,1.78,64,yes,yes,3,3,Sometimes,no,2,no,1,1,Frequently,Public_Transportation,Normal_Weight
+Male,22,1.72,68,yes,yes,2,3,Sometimes,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Male,26,1.85,105,yes,yes,3,3,Frequently,no,3,no,2,2,Sometimes,Public_Transportation,Obesity_Type_I
+Female,21,1.72,80,yes,yes,2,3,Frequently,no,2,yes,2,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22,1.65,56,no,no,3,3,Sometimes,no,3,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Male,41,1.8,99,no,yes,2,3,Sometimes,no,2,no,2,1,Frequently,Automobile,Obesity_Type_I
+Male,23,1.77,60,yes,yes,3,1,Sometimes,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,22,1.7,66,yes,no,3,3,Always,no,2,yes,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,27,1.93,102,yes,yes,2,1,Sometimes,no,1,no,1,0,Sometimes,Public_Transportation,Overweight_Level_II
+Female,29,1.53,78,no,yes,2,1,Sometimes,no,2,no,0,0,no,Automobile,Obesity_Type_I
+Female,30,1.71,82,yes,yes,3,4,Frequently,yes,1,no,0,0,no,Automobile,Overweight_Level_II
+Female,23,1.65,70,yes,no,2,1,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22,1.65,80,yes,no,2,3,Sometimes,no,2,no,3,2,no,Walking,Overweight_Level_II
+Female,52,1.69,87,yes,yes,3,1,Sometimes,yes,2,no,0,0,no,Automobile,Obesity_Type_I
+Female,22,1.65,60,yes,yes,3,3,Sometimes,no,2,no,1,0,Sometimes,Automobile,Normal_Weight
+Female,22,1.6,82,yes,yes,1,1,Sometimes,no,2,no,0,2,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21,1.85,68,yes,yes,2,3,Sometimes,no,2,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.6,50,yes,no,2,4,Frequently,yes,2,no,3,2,no,Public_Transportation,Normal_Weight
+Male,21,1.7,65,yes,yes,2,1,Frequently,no,2,no,1,2,Always,Walking,Normal_Weight
+Female,23,1.6,52,no,yes,2,4,Frequently,no,2,no,2,1,Sometimes,Automobile,Normal_Weight
+Male,19,1.75,76,yes,yes,3,3,Sometimes,no,2,yes,3,1,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.68,70,no,yes,2,3,Sometimes,no,2,no,2,2,Frequently,Walking,Normal_Weight
+Male,29,1.77,83,no,yes,1,4,Frequently,no,3,no,0,1,no,Motorbike,Overweight_Level_I
+Female,31,1.58,68,yes,no,2,1,Sometimes,no,1,no,1,0,Sometimes,Public_Transportation,Overweight_Level_II
+Female,24,1.77,76,no,no,2,3,Sometimes,no,3,no,1,1,Sometimes,Walking,Normal_Weight
+Male,39,1.79,90,no,no,2,1,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22,1.65,62,no,yes,2,4,Frequently,no,2,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,21,1.5,65,yes,no,2,3,Sometimes,no,2,no,2,2,Sometimes,Public_Transportation,Overweight_Level_II
+Female,22,1.56,49,no,yes,2,3,Sometimes,no,1,yes,2,0,no,Walking,Normal_Weight
+Female,21,1.6,48,no,yes,2,3,Sometimes,no,1,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.65,67,yes,yes,2,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,21,1.75,88,yes,yes,2,3,Sometimes,no,3,no,3,0,Sometimes,Public_Transportation,Overweight_Level_II
+Female,21,1.67,75,yes,yes,2,3,Sometimes,no,2,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,23,1.68,60,no,no,2,4,Frequently,no,2,no,0,0,no,Walking,Normal_Weight
+Female,21,1.66,64,yes,yes,1,3,Sometimes,no,1,no,0,0,no,Public_Transportation,Normal_Weight
+Male,21,1.66,62,yes,yes,2,3,Sometimes,yes,2,no,1,1,Frequently,Public_Transportation,Normal_Weight
+Male,21,1.81,80,no,no,1,3,no,no,2,no,2,0,no,Public_Transportation,Normal_Weight
+Female,21,1.53,65,yes,no,2,3,Sometimes,no,1,no,0,1,no,Public_Transportation,Overweight_Level_II
+Male,21,1.82,72,yes,yes,1,3,Frequently,no,3,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Male,21,1.75,72,yes,yes,1,3,Frequently,no,3,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,20,1.66,60,yes,no,3,3,Sometimes,no,2,no,1,0,Sometimes,Walking,Normal_Weight
+Female,21,1.55,50,no,yes,2,3,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
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+Female,23,1.64,52,no,yes,3,1,Sometimes,no,2,no,2,2,no,Public_Transportation,Normal_Weight
+Female,23,1.63,55,yes,no,3,3,no,no,2,yes,2,1,no,Public_Transportation,Normal_Weight
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+Male,20,1.76,55,yes,yes,2,4,Sometimes,no,3,no,2,2,no,Public_Transportation,Insufficient_Weight
+Female,21,1.55,49,yes,yes,2,3,Sometimes,no,3,no,3,1,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.65,58,no,yes,2,3,Sometimes,no,2,no,3,1,Sometimes,Public_Transportation,Normal_Weight
+Male,22,1.67,62,no,yes,2,1,no,no,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,22,1.68,55,yes,yes,2,3,Sometimes,no,2,no,0,2,Sometimes,Automobile,Normal_Weight
+Female,21,1.66,57,yes,yes,2,3,Frequently,no,1,no,1,1,no,Public_Transportation,Normal_Weight
+Female,21,1.62,69,yes,yes,1,3,Frequently,no,2,no,0,1,no,Public_Transportation,Overweight_Level_I
+Male,23,1.8,90,yes,yes,1,3,Always,no,2,no,0,2,Frequently,Public_Transportation,Overweight_Level_II
+Male,23,1.65,95,yes,yes,2,3,Always,no,2,no,0,1,Frequently,Automobile,Obesity_Type_I
+Male,30,1.76,112,yes,yes,1,3,no,yes,2,yes,0,0,Frequently,Automobile,Obesity_Type_II
+Male,23,1.8,60,yes,no,2,3,no,no,3,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Female,23,1.65,80,yes,yes,2,3,Always,no,2,no,0,2,no,Public_Transportation,Overweight_Level_II
+Female,22,1.67,50,yes,no,3,3,no,no,3,yes,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,24,1.65,60,yes,no,2,3,Sometimes,no,3,yes,1,0,no,Public_Transportation,Normal_Weight
+Male,19,1.85,65,yes,no,2,3,Sometimes,no,3,no,2,1,Sometimes,Bike,Normal_Weight
+Male,24,1.7,85,yes,yes,2,3,Frequently,no,3,no,0,1,Frequently,Public_Transportation,Overweight_Level_II
+Female,23,1.63,45,yes,no,3,3,Sometimes,no,3,yes,2,0,no,Public_Transportation,Insufficient_Weight
+Female,24,1.6,45,yes,no,2,3,no,no,2,no,1,0,no,Public_Transportation,Insufficient_Weight
+Female,24,1.7,80,yes,yes,2,3,Sometimes,no,3,no,0,0,no,Public_Transportation,Overweight_Level_II
+Female,23,1.65,90,yes,yes,2,3,Sometimes,no,3,no,0,1,no,Public_Transportation,Obesity_Type_I
+Male,23,1.65,60,yes,no,2,3,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Female,19,1.63,58,no,no,3,3,Sometimes,no,2,yes,0,0,no,Public_Transportation,Normal_Weight
+Male,30,1.8,91,yes,yes,2,3,Frequently,no,2,no,0,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23,1.67,85.5,yes,yes,2,3,Always,no,2,no,0,1,no,Public_Transportation,Obesity_Type_I
+Female,19,1.6,45,no,no,3,3,no,no,3,yes,2,0,no,Walking,Insufficient_Weight
+Male,25,1.7,83,yes,yes,2,3,Frequently,no,2,no,0,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23,1.65,58.5,yes,no,2,3,Sometimes,no,2,no,0,0,no,Public_Transportation,Normal_Weight
+Male,21,1.85,83,yes,yes,2,1,Frequently,no,3,no,1,0,no,Public_Transportation,Normal_Weight
+Male,19,1.82,87,yes,yes,2,3,Sometimes,no,2,no,0,0,no,Public_Transportation,Overweight_Level_I
+Female,22,1.65,65,yes,yes,2,3,Frequently,no,2,yes,1,0,no,Automobile,Normal_Weight
+Female,29,1.7,78,yes,yes,3,3,Sometimes,no,1,no,2,1,Frequently,Automobile,Overweight_Level_II
+Female,25,1.63,93,no,no,3,4,Always,no,1,no,2,0,no,Public_Transportation,Obesity_Type_II
+Female,20,1.61,64,yes,no,3,3,Always,no,2,yes,0,1,Frequently,Public_Transportation,Normal_Weight
+Male,55,1.78,84,yes,no,3,4,Frequently,no,3,yes,3,0,Frequently,Walking,Overweight_Level_I
+Female,20,1.6,57,no,no,3,3,Always,no,2,no,1,0,no,Walking,Normal_Weight
+Female,24,1.6,48,no,yes,3,3,Sometimes,no,2,no,2,0,no,Public_Transportation,Normal_Weight
+Male,26,1.7,70,yes,no,3,1,Frequently,no,2,no,2,0,Frequently,Public_Transportation,Normal_Weight
+Female,23,1.66,60,yes,no,2,3,Sometimes,no,2,no,3,0,Sometimes,Public_Transportation,Normal_Weight
+Female,21,1.52,42,no,no,3,1,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21,1.52,42,no,no,3,1,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Insufficient_Weight
+Male,23,1.72,70,no,no,2,3,Sometimes,no,2,no,3,1,Frequently,Public_Transportation,Normal_Weight
+Female,21,1.69,63,no,yes,3,1,Sometimes,no,1,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,22,1.7,66.4,yes,no,2,3,Frequently,no,2,no,0,0,no,Public_Transportation,Normal_Weight
+Female,21,1.55,57,no,yes,2,4,Frequently,no,2,yes,2,0,Sometimes,Automobile,Normal_Weight
+Female,22,1.65,58,yes,yes,3,4,Frequently,no,2,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Female,38,1.56,80,yes,yes,2,3,Sometimes,no,2,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,25,1.57,55,no,yes,2,1,Sometimes,no,2,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,25,1.57,55,no,yes,2,1,Sometimes,no,2,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Male,22,1.88,90,yes,yes,2,3,Always,no,1,no,0,1,Sometimes,Automobile,Overweight_Level_I
+Male,22,1.75,95,yes,no,2,3,Sometimes,no,3,no,3,2,no,Walking,Obesity_Type_I
+Female,21,1.65,88,yes,yes,3,1,Sometimes,no,3,no,2,1,no,Public_Transportation,Obesity_Type_I
+Male,21,1.75,75,yes,yes,3,3,Sometimes,no,2,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Female,22,1.58,58,yes,yes,2,1,Sometimes,no,1,no,0,0,no,Public_Transportation,Normal_Weight
+Female,18,1.56,51,yes,yes,2,4,Frequently,no,2,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Female,22,1.5,49,yes,no,2,1,Sometimes,no,2,no,3,0,no,Walking,Normal_Weight
+Female,19,1.61,62,yes,yes,3,1,Frequently,no,2,no,1,0,Frequently,Public_Transportation,Normal_Weight
+Female,17,1.75,57,yes,yes,3,3,Frequently,no,2,no,0,1,no,Public_Transportation,Normal_Weight
+Female,15,1.65,86,yes,yes,3,3,Sometimes,no,1,no,3,2,no,Walking,Obesity_Type_I
+Female,17,1.7,85,yes,no,2,3,Frequently,no,2,no,1,1,no,Public_Transportation,Overweight_Level_II
+Male,23,1.62,53,yes,yes,2,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,19,1.63,76,yes,no,3,3,Frequently,yes,3,no,2,1,Sometimes,Automobile,Overweight_Level_II
+Female,23,1.67,75,yes,yes,2,3,Frequently,yes,2,no,0,2,no,Walking,Overweight_Level_I
+Male,23,1.87,95,yes,yes,1,3,Frequently,no,2,no,0,2,Frequently,Public_Transportation,Overweight_Level_II
+Male,21,1.75,50,yes,no,3,4,Frequently,no,1,no,1,0,no,Walking,Insufficient_Weight
+Male,24,1.66,67,yes,yes,3,1,Sometimes,no,2,no,1,0,no,Public_Transportation,Normal_Weight
+Male,23,1.76,90,no,yes,3,3,Sometimes,no,1,no,0,0,no,Public_Transportation,Overweight_Level_II
+Male,18,1.75,80,yes,yes,2,3,Always,no,2,no,0,0,Frequently,Public_Transportation,Overweight_Level_I
+Male,19,1.67,68,no,yes,2,3,Sometimes,no,2,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Female,19,1.65,61,no,yes,3,1,Sometimes,no,3,yes,1,0,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.72,70,yes,yes,3,3,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,27,1.7,78,no,yes,3,3,Sometimes,no,3,no,1,1,Frequently,Public_Transportation,Overweight_Level_II
+Female,20,1.58,53,yes,yes,2,4,Frequently,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Male,23,1.62,58,no,no,2,3,Sometimes,no,1,no,1,0,no,Public_Transportation,Normal_Weight
+Female,19,1.65,56,yes,yes,3,3,Frequently,yes,3,yes,1,2,Frequently,Public_Transportation,Normal_Weight
+Female,61,1.65,66,no,yes,3,3,Always,no,2,no,1,1,Frequently,Public_Transportation,Normal_Weight
+Male,30,1.77,109,yes,yes,3,3,Sometimes,no,1,no,2,0,Sometimes,Automobile,Obesity_Type_I
+Male,24,1.7,75,yes,yes,2,3,Sometimes,no,2,no,1,0,Sometimes,Automobile,Overweight_Level_I
+Male,25,1.79,72,yes,yes,2,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,44,1.6,80,yes,no,2,3,Sometimes,yes,3,no,0,0,no,Motorbike,Obesity_Type_I
+Male,31,1.76,75,yes,no,3,3,Always,no,3,yes,3,0,no,Bike,Normal_Weight
+Male,25,1.7,68,no,yes,2,3,Frequently,no,2,no,0,1,no,Public_Transportation,Normal_Weight
+Male,23,1.89,65,yes,yes,3,3,Frequently,no,3,no,1,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,25,1.87,66,no,yes,3,3,Always,no,2,no,2,0,Frequently,Public_Transportation,Normal_Weight
+Male,23,1.74,93.5,no,yes,2,3,Frequently,yes,1,no,1,1,Frequently,Automobile,Obesity_Type_I
+Female,34,1.68,75,no,yes,3,1,Sometimes,no,1,no,0,0,Sometimes,Automobile,Overweight_Level_I
+Male,22,1.61,67,yes,no,2,4,Sometimes,no,3,yes,2,2,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,24,1.56,51,no,no,3,3,Frequently,no,2,no,2,2,Frequently,Public_Transportation,Normal_Weight
+Female,36,1.63,80,yes,no,3,3,Sometimes,no,1,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,27,1.6,61,no,yes,3,3,Always,no,2,no,0,0,Sometimes,Automobile,Normal_Weight
+Female,32,1.67,90,yes,yes,3,1,Sometimes,no,2,no,2,0,Sometimes,Automobile,Obesity_Type_I
+Male,25,1.78,78,yes,yes,2,1,Sometimes,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Female,30,1.62,59,yes,yes,3,3,Sometimes,no,2,no,2,0,Sometimes,Automobile,Normal_Weight
+Female,38,1.5,60,yes,yes,2,1,Always,yes,2,no,0,0,Sometimes,Automobile,Overweight_Level_I
+Male,34,1.69,84,yes,no,2,3,Sometimes,no,3,no,2,0,no,Automobile,Overweight_Level_II
+Male,22,1.74,94,yes,yes,2,3,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Obesity_Type_I
+Female,31,1.68,63,yes,yes,3,1,Frequently,no,2,no,1,1,Sometimes,Automobile,Normal_Weight
+Female,35,1.53,45,yes,no,3,3,Frequently,no,1,no,0,1,no,Automobile,Normal_Weight
+Male,21,1.67,60,yes,yes,2,3,Frequently,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Female,40,1.55,62,yes,no,3,3,Sometimes,no,3,no,0,0,Sometimes,Automobile,Overweight_Level_I
+Male,27,1.64,78,yes,yes,2,1,Sometimes,no,2,no,0,0,Frequently,Automobile,Overweight_Level_II
+Male,20,1.83,72,yes,no,3,3,Sometimes,yes,1,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,55,1.65,80,no,yes,2,3,Sometimes,no,2,no,1,0,no,Automobile,Overweight_Level_II
+Female,21,1.63,60,yes,yes,3,3,Always,yes,2,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Male,25,1.89,75,no,no,3,3,Frequently,no,2,yes,3,0,no,Public_Transportation,Normal_Weight
+Male,35,1.77,85,yes,no,3,3,Frequently,no,3,no,2,1,Sometimes,Automobile,Overweight_Level_II
+Male,30,1.92,130,yes,no,2,3,Sometimes,yes,1,no,1,0,Sometimes,Public_Transportation,Obesity_Type_II
+Female,29,1.74,72,yes,no,3,3,Always,no,2,no,0,0,Sometimes,Automobile,Normal_Weight
+Male,20,1.65,80,yes,no,2,3,Sometimes,no,2,no,1,2,no,Walking,Overweight_Level_II
+Female,22,1.73,79,yes,yes,2,1,Sometimes,no,2,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,45,1.63,77,yes,yes,2,3,Frequently,no,1,no,0,0,no,Automobile,Overweight_Level_II
+Male,22,1.72,82,no,yes,2,1,Sometimes,no,2,no,2,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,18,1.6,58,yes,yes,2,3,Frequently,no,2,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Female,23,1.65,59,no,no,3,1,Sometimes,no,2,no,1,2,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.74,64,no,no,3,3,Sometimes,no,2,yes,0,1,no,Public_Transportation,Normal_Weight
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,38,1.64,59.8,yes,yes,2,3,Sometimes,no,2,no,2,0,no,Public_Transportation,Normal_Weight
+Female,18,1.57,48,yes,yes,3,1,Always,no,1,no,1,0,no,Public_Transportation,Normal_Weight
+Male,22,1.84,84,yes,yes,3,3,Frequently,no,2,yes,3,0,Sometimes,Public_Transportation,Normal_Weight
+Male,26,1.91,84,yes,yes,3,3,Frequently,yes,2,no,2,2,Frequently,Public_Transportation,Normal_Weight
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,18,1.58,48,no,yes,2,3,Sometimes,no,2,no,1,0,no,Public_Transportation,Normal_Weight
+Female,23,1.68,67,yes,yes,2,3,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Female,22,1.68,52,no,yes,3,3,Frequently,no,2,no,0,1,no,Public_Transportation,Insufficient_Weight
+Female,23,1.48,60,yes,yes,2,1,Sometimes,yes,1,no,0,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,31,1.62,68,no,no,3,3,Sometimes,no,2,no,1,0,Sometimes,Automobile,Overweight_Level_I
+Male,39,1.78,96,yes,no,2,3,Sometimes,no,3,no,1,0,Frequently,Automobile,Obesity_Type_I
+Male,25,1.78,98,yes,no,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Male,35,1.78,105,yes,yes,3,1,no,no,3,no,3,1,Frequently,Automobile,Obesity_Type_I
+Female,33,1.63,62,yes,yes,2,3,Sometimes,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Male,20,1.6,56,no,yes,2,3,Sometimes,no,2,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Male,26,1.75,80,yes,yes,3,1,Frequently,yes,2,yes,2,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,20,1.83,85,yes,no,3,3,Sometimes,no,3,yes,3,0,Sometimes,Automobile,Overweight_Level_I
+Male,20,1.78,68,no,no,2,1,Frequently,no,3,yes,2,1,no,Public_Transportation,Normal_Weight
+Female,23,1.6,83,yes,yes,3,3,Frequently,no,3,no,3,0,no,Public_Transportation,Obesity_Type_I
+Male,19,1.8,85,yes,yes,3,3,Sometimes,no,2,no,1,1,Sometimes,Walking,Overweight_Level_I
+Male,22,1.75,74,yes,no,2,3,Sometimes,no,2,no,1,2,Sometimes,Bike,Normal_Weight
+Male,41,1.75,118,yes,yes,2,3,Sometimes,no,2,no,0,0,Sometimes,Bike,Obesity_Type_II
+Female,18,1.59,40,yes,yes,2,1,Frequently,no,1,no,0,2,no,Public_Transportation,Insufficient_Weight
+Female,23,1.66,60,yes,yes,2,1,Sometimes,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,23,1.63,83,yes,no,3,1,Sometimes,yes,3,no,1,2,Sometimes,Public_Transportation,Obesity_Type_I
+Female,41,1.54,80,yes,yes,2,3,Always,no,1,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,26,1.56,102,yes,yes,3,3,Sometimes,yes,1,no,0,1,Sometimes,Public_Transportation,Obesity_Type_III
+Male,29,1.69,90,yes,no,2,3,Sometimes,no,3,no,1,0,Sometimes,Automobile,Obesity_Type_I
+Male,27,1.83,71,yes,yes,2,3,Sometimes,no,2,no,3,2,no,Automobile,Normal_Weight
+Female,23,1.6,78,yes,yes,2,1,Sometimes,yes,2,no,1,0,Frequently,Public_Transportation,Obesity_Type_I
+Male,19,1.75,100,yes,yes,2,3,Frequently,no,2,no,2,0,no,Public_Transportation,Obesity_Type_I
+Male,30,1.75,73,yes,no,2,3,no,no,2,yes,1,0,Sometimes,Walking,Normal_Weight
+Female,22,1.69,65,yes,yes,2,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,22,1.69,65,yes,yes,2,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.8,114,yes,yes,2,3,Frequently,no,2,no,0,1,no,Public_Transportation,Obesity_Type_II
+Female,21,1.63,51,no,yes,2,1,Sometimes,no,1,no,1,1,no,Public_Transportation,Normal_Weight
+Female,24,1.5,63,yes,no,2,1,Sometimes,no,2,no,0,0,no,Public_Transportation,Overweight_Level_II
+Male,21,1.8,62,yes,yes,3,3,Sometimes,no,2,no,1,0,no,Public_Transportation,Normal_Weight
+Male,21,1.65,53,yes,yes,2,3,Sometimes,no,2,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Male,21,1.68,70,yes,yes,3,3,Sometimes,no,3,no,0,2,Sometimes,Public_Transportation,Normal_Weight
+Female,23,1.6,63,yes,no,3,3,Frequently,no,2,no,3,0,Sometimes,Walking,Normal_Weight
+Male,21,1.71,75,no,no,2,3,Frequently,no,3,yes,3,1,Frequently,Automobile,Overweight_Level_I
+Female,21,1.5,42.3,yes,no,1,1,Sometimes,no,2,no,3,0,no,Public_Transportation,Normal_Weight
+Female,21,1.6,68,yes,yes,2,3,Sometimes,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21,1.75,78,yes,no,2,3,Frequently,no,2,yes,0,2,Frequently,Public_Transportation,Overweight_Level_I
+Male,23,1.72,82,yes,yes,2,1,Sometimes,no,1,no,1,1,Sometimes,Public_Transportation,Overweight_Level_II
+Female,21,1.72,66.5,yes,yes,3,4,Always,no,3,no,0,2,no,Public_Transportation,Normal_Weight
+Female,22,1.61,63,yes,yes,3,3,Frequently,no,3,no,1,0,no,Public_Transportation,Normal_Weight
+Female,23,1.63,82,yes,yes,2,1,Sometimes,no,2,no,0,0,no,Public_Transportation,Obesity_Type_I
+Male,25,1.83,121,yes,no,3,3,Sometimes,no,3,no,2,0,Sometimes,Walking,Obesity_Type_II
+Female,20,1.6,50,no,yes,2,3,Sometimes,no,1,no,0,1,no,Automobile,Normal_Weight
+Female,24,1.62,58,no,yes,3,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,40,1.68,80,no,no,3,3,Sometimes,no,3,no,0,0,no,Automobile,Overweight_Level_II
+Male,32,1.75,120,yes,no,3,3,Sometimes,no,3,no,0,2,no,Automobile,Obesity_Type_II
+Male,20,1.9,91,yes,yes,3,4,Sometimes,no,3,no,2,0,Sometimes,Walking,Overweight_Level_I
+Female,21,1.63,66,yes,yes,3,1,Sometimes,yes,3,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Female,51,1.59,50,yes,no,3,3,Sometimes,yes,3,yes,2,0,no,Public_Transportation,Normal_Weight
+Female,34,1.68,77,yes,no,3,1,Sometimes,no,2,no,1,0,Sometimes,Automobile,Overweight_Level_II
+Female,19,1.59,49,yes,yes,2,1,no,no,2,no,2,0,no,Public_Transportation,Normal_Weight
+Female,19,1.69,70,yes,no,2,1,no,no,2,no,2,0,no,Public_Transportation,Normal_Weight
+Female,21,1.66,59,no,yes,1,3,Always,no,2,yes,3,0,no,Automobile,Normal_Weight
+Female,19,1.64,53,yes,yes,3,3,Sometimes,no,1,no,1,1,no,Public_Transportation,Normal_Weight
+Female,20,1.62,53,no,yes,3,1,Sometimes,no,3,no,1,1,no,Public_Transportation,Normal_Weight
+Female,19,1.7,64,yes,yes,3,3,no,no,3,yes,3,1,no,Public_Transportation,Normal_Weight
+Female,17,1.63,65,no,yes,2,1,Sometimes,no,3,no,1,1,no,Public_Transportation,Normal_Weight
+Male,22,1.6,66,no,yes,3,3,Sometimes,no,2,no,3,0,no,Bike,Overweight_Level_I
+Female,20,1.63,64,yes,yes,1,3,Always,no,2,no,0,2,no,Automobile,Normal_Weight
+Male,33,1.85,99,yes,yes,2,3,Sometimes,yes,3,no,1,0,Sometimes,Automobile,Overweight_Level_II
+Female,21,1.54,49,yes,no,2,1,Sometimes,no,2,yes,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,20,1.64,49,no,no,3,3,Sometimes,yes,2,no,2,1,Sometimes,Automobile,Insufficient_Weight
+Female,20,1.57,60,no,yes,3,3,Sometimes,no,3,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Female,20,1.62,52,no,yes,3,3,Frequently,no,1,no,3,1,no,Automobile,Normal_Weight
+Male,21,1.72,72,yes,yes,3,3,Always,no,3,yes,3,0,Sometimes,Automobile,Normal_Weight
+Male,21,1.76,78,yes,yes,3,1,Sometimes,no,2,no,3,0,Sometimes,Walking,Overweight_Level_I
+Female,20,1.65,75,yes,yes,3,1,Sometimes,no,2,no,1,1,no,Public_Transportation,Overweight_Level_II
+Male,20,1.67,78,yes,no,2,1,Sometimes,no,3,no,0,0,Frequently,Automobile,Overweight_Level_II
+Male,56,1.79,90,yes,no,2,3,Sometimes,yes,2,no,1,0,Frequently,Automobile,Overweight_Level_II
+Female,26,1.59,47,yes,yes,2,1,Sometimes,no,2,no,0,2,Sometimes,Public_Transportation,Normal_Weight
+Female,22,1.64,56,yes,no,3,3,Sometimes,no,2,no,0,0,no,Public_Transportation,Normal_Weight
+Male,19,1.78,81,yes,no,1,3,Sometimes,no,2,no,3,0,no,Bike,Overweight_Level_I
+Male,18,1.75,85,yes,no,2,3,Sometimes,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19,1.85,115,no,no,2,3,Sometimes,no,3,yes,1,2,no,Public_Transportation,Obesity_Type_I
+Male,18,1.7,80,no,no,3,1,Sometimes,no,1,no,1,0,no,Public_Transportation,Overweight_Level_II
+Female,18,1.67,91,yes,yes,1,3,Frequently,no,1,no,0,1,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21,1.72,75,no,yes,2,3,Sometimes,no,1,no,1,0,no,Public_Transportation,Overweight_Level_I
+Female,28,1.7,73,yes,no,2,3,Frequently,no,2,yes,2,0,Sometimes,Walking,Overweight_Level_I
+Male,18,1.74,70,no,yes,2,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.74,53,no,yes,2,3,Frequently,no,1,no,3,2,no,Public_Transportation,Insufficient_Weight
+Male,18,1.87,67,yes,yes,3,3,Frequently,no,3,yes,1,1,Sometimes,Automobile,Normal_Weight
+Male,18,1.7,50,no,yes,1,3,Frequently,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,39,1.65,50,no,yes,3,4,Frequently,no,2,no,0,0,Sometimes,Public_Transportation,Insufficient_Weight
+Male,38,1.7,78,no,yes,3,3,Frequently,no,2,no,0,0,Frequently,Automobile,Overweight_Level_II
+Male,17,1.67,60,no,no,2,4,Always,no,2,no,0,2,no,Public_Transportation,Normal_Weight
+Male,23,1.72,66,yes,yes,2,3,Sometimes,no,2,no,0,1,Sometimes,Walking,Normal_Weight
+Male,23,1.82,107,no,yes,2,3,Sometimes,no,3,no,0,1,Sometimes,Public_Transportation,Obesity_Type_I
+Female,19,1.5,50,no,yes,2,3,Frequently,no,1,no,0,2,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.7,60,no,yes,2,3,Sometimes,no,2,no,2,1,no,Public_Transportation,Normal_Weight
+Male,25,1.71,71,yes,yes,2,3,Frequently,no,2,no,0,0,Sometimes,Motorbike,Normal_Weight
+Female,25,1.61,61,no,no,2,3,Sometimes,no,1,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Female,18,1.5,50,yes,yes,3,3,Frequently,no,2,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Male,16,1.67,50,yes,yes,2,1,Frequently,no,3,no,1,0,no,Public_Transportation,Insufficient_Weight
+Male,21,1.82,67,no,yes,2,4,Always,yes,3,no,3,2,Sometimes,Walking,Normal_Weight
+Female,32,1.57,57,yes,yes,3,3,Sometimes,no,2,no,0,0,Sometimes,Automobile,Normal_Weight
+Male,18,1.79,52,no,no,3,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,21,1.75,62,no,yes,3,4,Frequently,yes,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.7,55,yes,yes,2,3,Frequently,no,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Female,18,1.62,55,yes,yes,2,3,Frequently,no,1,no,1,1,no,Public_Transportation,Normal_Weight
+Male,17,1.69,60,no,no,2,3,Sometimes,no,3,no,2,1,Sometimes,Walking,Normal_Weight
+Male,20,1.77,70,yes,yes,1,1,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,21,1.79,105,yes,yes,2,3,Always,no,1,no,0,0,Sometimes,Public_Transportation,Obesity_Type_I
+Female,21,1.6,61,no,yes,2,3,Sometimes,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,18,1.6,58,yes,yes,2,3,Sometimes,no,3,no,3,0,no,Public_Transportation,Normal_Weight
+Female,17,1.56,51,no,yes,3,3,Sometimes,no,1,no,0,2,no,Public_Transportation,Normal_Weight
+Male,19,1.88,79,no,no,2,3,Sometimes,no,3,no,3,0,Sometimes,Automobile,Normal_Weight
+Male,16,1.82,71,yes,yes,2,3,Frequently,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,17,1.8,58,no,yes,2,3,Frequently,no,2,no,2,1,no,Walking,Insufficient_Weight
+Male,21,1.7,65,yes,yes,2,3,Frequently,no,1,no,1,1,no,Public_Transportation,Normal_Weight
+Male,19,1.82,75,yes,no,2,3,Frequently,no,2,no,1,0,no,Public_Transportation,Normal_Weight
+Male,18,1.86,110,yes,yes,2,1,Sometimes,yes,2,no,1,2,Sometimes,Public_Transportation,Obesity_Type_I
+Female,16,1.66,58,no,no,2,1,Sometimes,no,1,no,0,1,no,Walking,Normal_Weight
+Female,21,1.53,53,no,yes,3,3,Sometimes,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,26,1.74,80,no,no,2,3,Sometimes,no,2,no,2,0,no,Automobile,Overweight_Level_I
+Male,18,1.8,80,yes,yes,2,3,Frequently,no,1,no,0,1,Frequently,Public_Transportation,Normal_Weight
+Male,23,1.7,75,no,yes,3,3,Sometimes,no,3,no,1,2,Sometimes,Public_Transportation,Overweight_Level_I
+Male,26,1.7,70,no,yes,2,3,Sometimes,yes,1,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.72,55,no,yes,2,4,Sometimes,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,16,1.84,45,yes,yes,3,3,Always,no,3,no,3,2,Sometimes,Walking,Insufficient_Weight
+Female,16,1.57,49,no,yes,2,4,Always,no,2,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.8,85,yes,no,2,3,Sometimes,no,3,no,2,1,Sometimes,Public_Transportation,Overweight_Level_I
+Male,23,1.75,120,yes,yes,2,3,Sometimes,yes,2,no,2,1,Sometimes,Public_Transportation,Obesity_Type_II
+Female,24,1.56,60,yes,yes,1,3,Always,no,2,no,0,2,Sometimes,Public_Transportation,Normal_Weight
+Female,23,1.59,48,yes,yes,2,1,Frequently,no,1,no,0,2,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.8,75,no,yes,3,4,Always,no,3,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,16,1.66,58,no,no,2,1,Sometimes,no,1,no,0,1,no,Walking,Normal_Weight
+Male,17,1.79,57,yes,yes,2,4,Frequently,no,2,no,2,1,no,Public_Transportation,Insufficient_Weight
+Male,17,1.72,62,no,yes,2,3,Always,no,2,no,3,1,no,Public_Transportation,Normal_Weight
+Female,16,1.6,57,no,yes,3,3,Sometimes,no,1,no,3,0,no,Public_Transportation,Normal_Weight
+Male,17,1.66,56,yes,yes,1,3,Always,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,26,1.65,63,no,yes,3,3,Sometimes,no,1,no,1,0,no,Walking,Normal_Weight
+Male,26,1.7,72,no,yes,2,3,Always,no,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,38,1.75,75,yes,no,3,3,Frequently,no,1,no,2,1,no,Automobile,Normal_Weight
+Male,18,1.75,70,no,yes,3,4,Sometimes,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Female,25,1.56,45,no,yes,2,3,Sometimes,no,1,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Female,27,1.55,63,no,yes,2,3,Sometimes,no,1,no,0,1,Sometimes,Automobile,Overweight_Level_I
+Male,21,1.67,67,yes,yes,3,1,Always,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Male,38,1.75,75,yes,no,3,3,Frequently,no,1,no,3,1,no,Automobile,Normal_Weight
+Female,23,1.75,56,no,no,3,3,Frequently,no,2,no,2,0,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.8,72,yes,yes,2,3,Sometimes,no,2,no,3,0,Sometimes,Automobile,Normal_Weight
+Female,30,1.65,71,yes,yes,2,3,Sometimes,no,1,no,0,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21,1.55,58,no,yes,2,1,Sometimes,no,1,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.7,55.3,yes,yes,3,3,Sometimes,no,2,no,3,0,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.72,76,yes,no,3,4,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19,1.74,74,yes,no,3,1,Sometimes,no,3,no,3,1,Sometimes,Walking,Normal_Weight
+Female,19,1.65,82,yes,yes,3,3,Sometimes,no,1,no,0,1,Sometimes,Public_Transportation,Obesity_Type_I
+Female,17,1.8,50,no,yes,3,4,Frequently,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17,1.74,56,yes,yes,2,3,Sometimes,no,2,no,2,1,no,Public_Transportation,Normal_Weight
+Male,27,1.85,75,yes,yes,2,1,Sometimes,no,2,no,1,0,no,Walking,Normal_Weight
+Female,23,1.7,56,no,no,3,4,Always,no,3,yes,3,0,no,Automobile,Normal_Weight
+Female,18,1.45,53,no,yes,2,3,Frequently,no,2,yes,1,2,Frequently,Public_Transportation,Overweight_Level_I
+Male,19,1.7,50,no,yes,1,4,Frequently,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,20,1.7,65,no,yes,2,3,Sometimes,no,2,no,0,1,no,Public_Transportation,Normal_Weight
+Male,18,1.78,64.4,yes,yes,3,3,Frequently,no,2,no,3,2,no,Walking,Normal_Weight
+Female,17,1.6,65,no,yes,3,1,Sometimes,no,2,yes,1,2,Sometimes,Public_Transportation,Overweight_Level_I
+Female,19,1.53,42,no,no,2,3,Sometimes,no,1,yes,2,0,Frequently,Public_Transportation,Insufficient_Weight
+Male,21,1.8,72,no,yes,3,3,Sometimes,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Male,20,1.6,50,no,no,2,3,Sometimes,no,1,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.74,105,yes,yes,3,3,Sometimes,no,2,no,1,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23,1.65,66,no,no,3,3,Sometimes,no,2,no,3,0,no,Public_Transportation,Normal_Weight
+Male,18,1.87,173,yes,yes,3,3,Frequently,no,2,no,2,1,Sometimes,Public_Transportation,Obesity_Type_III
+Male,17,1.7,55,no,yes,3,3,Always,no,2,no,3,1,Sometimes,Public_Transportation,Normal_Weight
+Female,21,1.54,47,yes,no,3,3,Always,no,1,no,2,0,no,Public_Transportation,Normal_Weight
+Male,17,1.8,97,yes,yes,2,3,Sometimes,no,3,no,1,1,Sometimes,Walking,Overweight_Level_II
+Male,18,1.82,80,yes,yes,2,3,Sometimes,no,2,no,3,2,no,Walking,Normal_Weight
+Male,20,1.98,125,yes,yes,2,3,Always,no,3,no,1,1,Sometimes,Public_Transportation,Obesity_Type_I
+Male,17,1.75,70,yes,no,2,3,Sometimes,no,1,no,3,2,Sometimes,Walking,Normal_Weight
+Female,26,1.65,60,no,no,3,4,Always,no,2,no,2,0,no,Walking,Normal_Weight
+Female,17,1.6,53,no,yes,1,3,Sometimes,no,1,no,2,2,Sometimes,Walking,Normal_Weight
+Female,24,1.6,51,yes,yes,1,3,Sometimes,no,2,no,0,1,Sometimes,Automobile,Normal_Weight
+Female,17,1.6,59,no,yes,2,3,Sometimes,no,2,no,1,2,no,Public_Transportation,Normal_Weight
+Female,27,1.55,62,no,yes,3,1,Sometimes,no,1,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,17,1.9,60,no,no,3,3,Sometimes,no,2,no,3,1,no,Walking,Insufficient_Weight
+Female,17,1.7,56,yes,yes,1,3,Sometimes,no,1,no,1,1,no,Automobile,Normal_Weight
+Male,41,1.75,110,yes,no,2,1,Sometimes,no,1,no,1,0,Frequently,Automobile,Obesity_Type_II
+Female,33,1.56,48,yes,no,2,3,Sometimes,no,2,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.87,75,no,yes,2,3,Frequently,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,40,1.56,80,yes,yes,2,1,Sometimes,no,2,no,2,0,no,Public_Transportation,Obesity_Type_I
+Female,37,1.65,73,yes,no,3,3,Sometimes,no,2,no,1,0,Sometimes,Automobile,Overweight_Level_I
+Male,19,1.8,80,no,yes,2,1,Sometimes,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,24,1.84,86,yes,yes,2,1,Always,no,2,no,2,1,Sometimes,Public_Transportation,Overweight_Level_I
+Male,24,1.7,68,no,yes,3,3,Sometimes,no,2,no,0,0,Sometimes,Motorbike,Normal_Weight
+Male,33,1.72,83,yes,yes,2,1,Sometimes,no,2,no,3,0,Sometimes,Automobile,Overweight_Level_II
+Female,40,1.58,63,no,yes,2,3,Frequently,no,2,no,3,1,Sometimes,Public_Transportation,Overweight_Level_I
+Female,37,1.68,83,yes,yes,2,1,Sometimes,no,2,no,0,0,no,Automobile,Overweight_Level_II
+Male,20,1.58,74,no,no,3,3,Frequently,no,2,no,3,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19,1.8,60,no,yes,2,3,Sometimes,no,1,no,1,1,no,Public_Transportation,Normal_Weight
+Male,17,1.62,69,yes,yes,3,1,Always,no,2,yes,1,2,Sometimes,Public_Transportation,Overweight_Level_I
+Female,18,1.62,58,no,yes,3,3,Sometimes,no,1,no,0,2,no,Automobile,Normal_Weight
+Female,21,1.54,56,no,yes,2,1,Sometimes,no,2,no,0,2,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.76,70,yes,yes,2,4,Frequently,no,2,no,2,1,Sometimes,Automobile,Normal_Weight
+Male,41,1.8,92,yes,yes,2,1,Frequently,no,3,no,1,0,Sometimes,Automobile,Overweight_Level_II
+Female,36,1.58,60,yes,no,3,3,Sometimes,no,1,no,2,0,Sometimes,Automobile,Normal_Weight
+Male,18,1.76,68,no,no,2,4,Frequently,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.73,70,no,yes,3,3,Frequently,no,1,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,17,1.7,70,yes,yes,3,3,Sometimes,no,2,no,0,2,Sometimes,Walking,Normal_Weight
+Male,25,1.7,83,no,yes,3,3,Sometimes,no,3,yes,3,0,no,Motorbike,Overweight_Level_II
+Male,21,1.8,75,yes,yes,3,3,Frequently,no,1,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,29,1.6,56,yes,no,2,3,Frequently,no,1,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Male,17,1.7,98,yes,no,2,3,Sometimes,no,2,no,0,1,Sometimes,Automobile,Obesity_Type_I
+Female,18,1.6,60,yes,yes,3,1,Sometimes,no,1,yes,0,2,no,Walking,Normal_Weight
+Female,16,1.55,45,no,yes,2,3,Frequently,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Female,18,1.59,53,no,no,1,3,Sometimes,no,1,no,1,2,no,Public_Transportation,Normal_Weight
+Female,37,1.5,75,yes,yes,2,3,Frequently,no,1,no,0,0,Sometimes,Motorbike,Obesity_Type_I
+Male,18,1.78,108,yes,no,2,3,Sometimes,no,3,no,1,0,no,Public_Transportation,Obesity_Type_I
+Female,16,1.61,65,yes,yes,1,1,Sometimes,no,2,no,0,0,no,Public_Transportation,Overweight_Level_I
+Male,23,1.71,50,yes,yes,2,3,Always,no,3,no,0,2,no,Public_Transportation,Insufficient_Weight
+Female,18,1.7,50,no,no,2,3,Sometimes,no,2,no,0,1,Sometimes,Walking,Insufficient_Weight
+Male,18,1.76,69.5,no,no,3,3,Frequently,no,3,no,2,2,no,Public_Transportation,Normal_Weight
+Male,18,1.7,78,no,yes,1,3,Sometimes,no,2,no,0,1,Sometimes,Public_Transportation,Overweight_Level_II
+Female,17,1.53,55,yes,yes,2,3,Sometimes,no,1,no,0,1,no,Public_Transportation,Normal_Weight
+Female,20,1.54,39,yes,yes,1,3,Sometimes,no,2,no,3,2,Sometimes,Public_Transportation,Insufficient_Weight
+Female,38,1.55,59,yes,no,3,3,Sometimes,no,1,no,2,1,Frequently,Automobile,Normal_Weight
+Male,20,1.66,60,no,yes,2,4,Frequently,no,3,no,0,0,no,Public_Transportation,Normal_Weight
+Male,21,1.85,125,yes,yes,3,1,Always,no,1,no,0,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,21,1.65,60,no,no,3,1,Frequently,no,1,no,0,0,Sometimes,Motorbike,Normal_Weight
+Male,18,1.65,70,yes,no,2,3,Sometimes,no,2,no,0,0,no,Public_Transportation,Overweight_Level_I
+Male,26,1.83,82,no,yes,2,3,Always,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,27,1.83,99,yes,yes,3,1,Frequently,no,2,no,2,1,Sometimes,Walking,Overweight_Level_II
+Female,26,1.66,112,yes,no,3,3,Sometimes,no,3,no,0,0,no,Automobile,Obesity_Type_III
+Male,34,1.78,73,no,yes,2,3,Sometimes,no,2,no,3,1,Sometimes,Automobile,Normal_Weight
+Male,18,1.74,86,no,no,3,3,Sometimes,no,2,no,3,0,no,Public_Transportation,Overweight_Level_II
+Male,33,1.76,66.5,no,no,2,3,Sometimes,no,2,no,3,1,Sometimes,Automobile,Normal_Weight
+Female,19,1.51,59,yes,yes,3,3,Frequently,no,1,no,1,2,Sometimes,Walking,Overweight_Level_I
+Male,20,1.81,79,yes,no,3,1,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Female,33,1.55,55,yes,yes,3,1,Sometimes,no,3,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.83,66,no,no,2,3,Sometimes,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Female,20,1.6,65,no,no,3,3,Sometimes,no,2,yes,0,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,33,1.85,85,no,yes,2,3,Frequently,no,2,no,1,0,Sometimes,Automobile,Normal_Weight
+Male,33,1.75,85,no,no,2,3,Sometimes,no,2,yes,1,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33,1.83,113,yes,yes,2,1,Always,no,2,no,1,2,Frequently,Automobile,Obesity_Type_I
+Male,14,1.71,72,yes,yes,3,3,Sometimes,no,3,no,2,1,no,Walking,Normal_Weight
+Male,34,1.84,88,no,yes,3,3,Sometimes,no,1,yes,2,0,no,Automobile,Overweight_Level_I
+Male,18,1.77,87,yes,yes,3,3,Sometimes,no,2,no,1,1,Frequently,Public_Transportation,Overweight_Level_II
+Male,18,1.7,90,no,yes,3,3,Sometimes,no,2,no,2,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,29,1.62,89,yes,yes,1,3,Sometimes,no,1,no,0,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,18,1.85,60,yes,yes,3,4,Sometimes,no,2,yes,2,0,Sometimes,Automobile,Insufficient_Weight
+Male,18,1.84,60,yes,yes,3,4,Sometimes,no,2,yes,2,0,Sometimes,Automobile,Insufficient_Weight
+Male,19,1.75,58,no,yes,2,3,Sometimes,no,2,no,2,0,Sometimes,Bike,Normal_Weight
+Male,33,1.85,93,yes,yes,2,3,Sometimes,no,1,no,1,1,Sometimes,Walking,Overweight_Level_II
+Male,33,1.74,76,no,no,2,3,Sometimes,no,1,no,0,2,Sometimes,Walking,Overweight_Level_I
+Female,19,1.61,53.8,yes,yes,2,3,Always,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,22,1.75,70,no,no,2,3,Sometimes,no,3,no,1,1,no,Public_Transportation,Normal_Weight
+Female,20,1.67,60,no,yes,3,3,no,no,2,no,3,0,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.7,69,no,yes,3,1,Frequently,no,1,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,26,1.9,80,yes,yes,2,3,Frequently,no,1,yes,1,0,Sometimes,Automobile,Normal_Weight
+Male,18,1.65,85,no,yes,2,3,Sometimes,no,1,no,1,0,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18,1.6,55,no,yes,2,4,Frequently,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.8,70,no,yes,3,3,Frequently,no,1,no,2,0,no,Public_Transportation,Normal_Weight
+Female,18,1.64,59,yes,yes,2,3,Sometimes,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.89,87,no,yes,2,4,Frequently,no,2,no,3,1,Frequently,Automobile,Normal_Weight
+Female,19,1.76,80,yes,yes,2,1,Sometimes,no,3,no,3,1,no,Public_Transportation,Overweight_Level_I
+Female,18,1.56,55,no,yes,2,3,Sometimes,no,1,no,0,0,Frequently,Automobile,Normal_Weight
+Female,18,1.6,56,yes,yes,2,1,Always,no,2,no,1,0,Sometimes,Walking,Normal_Weight
+Female,19,1.67,64,no,yes,3,3,Sometimes,no,2,no,2,1,Sometimes,Automobile,Normal_Weight
+Female,19,1.6,60,no,yes,2,3,Frequently,no,2,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Female,18,1.55,56,no,yes,2,3,Sometimes,no,1,no,0,0,no,Automobile,Normal_Weight
+Female,18,1.55,50,yes,yes,3,3,Frequently,no,1,no,1,2,no,Public_Transportation,Normal_Weight
+Male,26,1.72,65,yes,yes,2,3,Sometimes,no,2,no,0,1,Sometimes,Walking,Normal_Weight
+Male,18,1.72,53,yes,yes,2,3,Sometimes,no,2,no,0,2,Sometimes,Public_Transportation,Insufficient_Weight
+Male,19,1.7,60,yes,yes,2,1,Sometimes,no,2,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,19,1.51,45,no,yes,2,4,Sometimes,no,1,no,3,0,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.83,82,yes,yes,3,3,Frequently,no,2,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.8,87,yes,yes,2,4,Sometimes,no,2,no,2,1,Sometimes,Public_Transportation,Overweight_Level_I
+Female,24,1.6,100.5,yes,yes,3,1,Sometimes,no,1,no,0,2,Sometimes,Public_Transportation,Obesity_Type_II
+Female,18,1.63,63,yes,yes,1,3,Sometimes,no,2,no,2,2,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.71,71,no,yes,3,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.7,65,yes,no,2,3,Frequently,no,2,no,1,0,no,Public_Transportation,Normal_Weight
+Male,23,1.75,69,no,no,3,3,no,no,2,no,2,1,Sometimes,Automobile,Normal_Weight
+Female,18,1.62,50,no,yes,3,3,Sometimes,no,1,no,0,0,Sometimes,Automobile,Normal_Weight
+Female,20,1.68,68,no,yes,3,1,Sometimes,no,1,no,1,0,no,Public_Transportation,Normal_Weight
+Male,18,1.85,66,no,yes,2,3,Frequently,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,33,1.59,60,no,yes,3,1,Frequently,no,2,no,0,0,no,Public_Transportation,Normal_Weight
+Female,19,1.5,45,no,yes,2,3,Sometimes,no,1,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.69,60,no,yes,2,3,Always,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.76,79,yes,yes,2,3,Frequently,no,3,no,1,2,Frequently,Public_Transportation,Overweight_Level_I
+Female,18,1.62,55,yes,yes,2,3,Frequently,no,1,no,1,1,no,Public_Transportation,Normal_Weight
+Male,21,1.71,100,yes,yes,2,1,Sometimes,no,2,no,0,2,no,Public_Transportation,Obesity_Type_I
+Male,27,1.72,88,yes,yes,2,1,Always,no,2,no,0,0,Sometimes,Automobile,Overweight_Level_II
+Male,17,1.8,68,yes,no,2,3,Sometimes,no,1,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.93,86,no,no,3,4,Always,no,2,no,2,0,Sometimes,Walking,Normal_Weight
+Female,18,1.6,51,yes,yes,2,3,Frequently,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,22,1.74,75,yes,yes,3,3,Frequently,no,1,no,1,0,no,Automobile,Normal_Weight
+Male,22,1.74,75,yes,yes,3,3,Frequently,no,1,no,1,0,no,Automobile,Normal_Weight
+Female,20,1.62,45,no,yes,3,3,Frequently,no,1,no,1,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19,1.54,42,no,yes,3,1,Sometimes,no,2,no,0,1,no,Public_Transportation,Insufficient_Weight
+Female,20,1.56,51.5,no,yes,2,3,Frequently,no,2,no,3,0,Sometimes,Public_Transportation,Normal_Weight
+Female,18,1.6,83,yes,yes,2,3,Sometimes,no,2,yes,1,0,no,Public_Transportation,Obesity_Type_I
+Female,18,1.54,71,no,no,3,4,Frequently,no,2,no,1,1,no,Public_Transportation,Overweight_Level_II
+Female,18,1.63,51,yes,yes,1,3,Frequently,no,1,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.78,64,no,yes,2,3,Sometimes,no,1,no,1,0,no,Public_Transportation,Normal_Weight
+Female,18,1.62,68,no,no,2,1,Sometimes,no,1,no,0,2,no,Public_Transportation,Overweight_Level_I
+Female,18,1.71,75,yes,yes,3,3,Sometimes,no,2,no,1,0,no,Public_Transportation,Overweight_Level_I
+Female,18,1.64,56,yes,yes,3,3,Frequently,no,1,yes,1,1,no,Public_Transportation,Normal_Weight
+Male,19,1.69,65,no,yes,2,3,Frequently,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,17,1.58,50,no,yes,1,4,Frequently,no,2,no,1,2,Sometimes,Public_Transportation,Normal_Weight
+Female,18,1.57,50,no,yes,2,3,Sometimes,no,1,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.74,64,yes,yes,3,4,Sometimes,no,1,yes,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,20,1.58,53.5,yes,yes,2,1,Frequently,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,18,1.5,58,no,yes,2,3,Sometimes,no,1,no,0,0,no,Public_Transportation,Overweight_Level_I
+Female,36,1.65,80,yes,yes,2,3,Sometimes,no,1,no,0,2,no,Automobile,Overweight_Level_II
+Male,21,1.8,73,yes,yes,1,3,Always,no,2,no,3,1,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.75,75,no,yes,2,3,Sometimes,no,2,no,1,0,Frequently,Public_Transportation,Normal_Weight
+Male,20,1.84,104,yes,no,2,3,Sometimes,no,3,no,3,0,no,Motorbike,Obesity_Type_I
+Male,21,1.88,84,yes,yes,3,3,Sometimes,no,3,no,2,1,Sometimes,Walking,Normal_Weight
+Female,19,1.56,50,no,yes,2,1,Sometimes,no,1,no,0,2,no,Public_Transportation,Normal_Weight
+Male,24,1.75,84,no,yes,3,3,no,no,2,yes,1,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,25,1.66,68,no,yes,2,3,Sometimes,yes,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,45,1.7,86,no,yes,3,3,Frequently,no,1,no,0,0,no,Automobile,Overweight_Level_II
+Male,20,1.8,65,no,yes,2,3,Frequently,no,1,no,2,0,Sometimes,Motorbike,Normal_Weight
+Female,18,1.67,66,no,yes,3,3,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.8,60,yes,yes,3,1,Always,no,1,yes,0,0,no,Motorbike,Normal_Weight
+Male,18,1.72,53,yes,yes,2,3,Sometimes,no,2,no,0,2,Sometimes,Public_Transportation,Insufficient_Weight
+Male,20,1.56,45,no,no,2,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,25.196214,1.686306,104.572712,yes,yes,3,3,Sometimes,no,1.152736,no,0.319156,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.503343,1.683124,126.67378,yes,yes,3,3,Sometimes,no,1.115967,no,1.541072,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.622397,110.79263,yes,yes,3,3,Sometimes,no,2.704507,no,0,0.29499,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.853826,1.755643,137.796884,yes,yes,3,3,Sometimes,no,2.184707,no,1.978631,0.838957,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.90012,1.843419,165.057269,yes,yes,3,3,Sometimes,no,2.406541,no,0.10032,0.479221,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.306615,1.7456,133.03441,yes,yes,3,3,Sometimes,no,2.984323,no,1.586525,0.62535,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.630927,111.485516,yes,yes,3,3,Sometimes,no,2.444125,no,0,0.26579,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.629191,104.826776,yes,yes,3,3,Sometimes,no,2.654702,no,0,0.555468,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.849705,1.770612,133.963349,yes,yes,3,3,Sometimes,no,2.825629,no,1.399183,0.928972,Sometimes,Public_Transportation,Obesity_Type_III
+Male,19.799054,1.743702,54.927529,yes,yes,2,3.28926,Sometimes,no,2.847264,no,1.680844,2,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.188754,1.771915,55.695036,yes,yes,2,4,Sometimes,no,2.884033,no,2,1.340107,Sometimes,Public_Transportation,Insufficient_Weight
+Male,22.285024,1.75376,55.879263,yes,yes,2.450218,3.995147,Sometimes,no,2.147746,no,2,0.58998,Sometimes,Public_Transportation,Insufficient_Weight
+Female,22,1.675446,51.154201,yes,yes,3,3,Frequently,no,2.815293,no,1.978631,1,no,Public_Transportation,Insufficient_Weight
+Female,21.02497,1.666203,49.869791,yes,yes,3,3,Frequently,no,2.593459,no,2,1,no,Public_Transportation,Insufficient_Weight
+Female,22.038327,1.711467,51.965521,yes,yes,2.880161,3,Frequently,no,1.031354,no,2.206738,1.37465,no,Public_Transportation,Insufficient_Weight
+Female,21.243142,1.598019,44.845655,no,no,3,1.72626,Frequently,no,2.444125,no,1.31817,0,no,Public_Transportation,Insufficient_Weight
+Female,22.142432,1.59611,42.848033,no,no,3,2.581015,Frequently,no,2.654702,no,0.902095,0,no,Public_Transportation,Insufficient_Weight
+Female,21.962426,1.57206,43.919835,no,no,3,1.600812,Frequently,no,2.651258,no,0.600817,0,no,Public_Transportation,Insufficient_Weight
+Female,21.491055,1.586952,43.087508,no,no,2.00876,1.73762,Frequently,no,1.792022,no,0.119643,0,no,Public_Transportation,Insufficient_Weight
+Female,22.717943,1.59559,44.581159,no,no,2.596579,1.10548,Frequently,no,1.490613,no,0.345684,0,no,Public_Transportation,Insufficient_Weight
+Female,23.501249,1.6,45,no,no,2.591439,3,Frequently,no,2.074048,no,1.679935,0,no,Public_Transportation,Insufficient_Weight
+Female,18.535075,1.688025,45,no,yes,3,3,Sometimes,no,3,yes,2.539762,1.283673,no,Public_Transportation,Insufficient_Weight
+Female,19,1.556211,42.339767,no,yes,3,2.0846,Sometimes,no,2.13755,yes,0.196152,0.062488,no,Public_Transportation,Insufficient_Weight
+Female,19,1.564199,42.096062,no,yes,3,1.894384,Sometimes,no,2.456581,yes,1.596576,0.9974,no,Public_Transportation,Insufficient_Weight
+Female,20.254534,1.56948,41.324558,no,yes,2.392665,1,Frequently,no,1,no,0,0.738269,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21,1.52,42,no,yes,3,1,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.225396,1.550648,44.641796,no,yes,3,2.857787,Frequently,no,1,no,0.754646,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.86997,1.520997,42,no,yes,3,1,Frequently,no,1.527036,no,0,0.860497,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.147114,1.528011,42,no,yes,3,1,Frequently,no,1.322669,no,0,0.478676,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21,1.52,42,no,yes,3,1,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21.996523,1.733263,50.890363,no,no,3,3.765526,Frequently,no,1.656082,no,0.42777,0.555967,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21.376426,1.722527,50.833029,no,no,3,3.285167,Frequently,no,1.569082,no,0.545931,0.469735,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.724106,1.734832,50,no,no,1.123939,4,Frequently,no,1,no,1.303976,0.371941,Sometimes,Public_Transportation,Insufficient_Weight
+Male,23,1.882779,64.106108,yes,yes,3,3,Sometimes,no,2.843777,no,1.488843,0.009254,Sometimes,Automobile,Insufficient_Weight
+Male,22.851834,1.854592,63.611109,yes,yes,3,3.691226,Sometimes,no,2.650989,no,1.228136,0.8324,Sometimes,Automobile,Insufficient_Weight
+Male,23,1.835643,58.854416,yes,yes,3,3,Sometimes,no,2.027984,no,1.661556,0.114716,Sometimes,Automobile,Insufficient_Weight
+Male,18.216032,1.755507,52,yes,yes,3,3,Frequently,no,2,no,0.658894,1,no,Public_Transportation,Insufficient_Weight
+Male,21.81119,1.71266,51.598593,yes,yes,3,3.156153,Frequently,no,1.253803,no,0.544784,0.256382,no,Public_Transportation,Insufficient_Weight
+Male,18.164768,1.694265,52,yes,yes,3,3,Frequently,no,2,no,0.819682,1,no,Public_Transportation,Insufficient_Weight
+Female,18.871794,1.586895,41.452385,no,yes,2,1.07976,Sometimes,no,1,no,0.194745,1.547086,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.766033,1.579561,41.890204,no,yes,2.027574,1,Sometimes,no,1.636326,no,0,1.906141,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.540535,1.564568,41.397378,no,yes,2.658112,1,Sometimes,no,1.298165,no,0,1.639326,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.88036,1.670635,49.742931,no,no,2.88626,3.559841,Frequently,no,1.632004,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.717249,1.688426,49.660995,no,no,2.714447,3,Frequently,no,2,no,1.903182,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.633898,1.66084,49.039794,no,no,2.750715,3,Frequently,no,2,no,1.067817,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,23,1.710182,50.287967,yes,yes,2,3,Frequently,no,2.099863,no,1.63581,2,no,Public_Transportation,Insufficient_Weight
+Female,20.406871,1.755978,53.699561,yes,yes,2,3.891994,Frequently,no,1.86393,no,2.870127,2,no,Public_Transportation,Insufficient_Weight
+Female,23,1.728834,51.442293,yes,yes,2,3,Frequently,no,1.229915,no,0.619533,2,no,Public_Transportation,Insufficient_Weight
+Female,19.058511,1.662017,49.838965,no,yes,1.4925,3,Sometimes,no,1.049134,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.827008,1.7,50,no,yes,1,3.240578,Sometimes,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.314964,1.661403,49.932199,no,yes,2.205439,3,Sometimes,no,1.530531,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,29.970445,1.610863,49.516027,yes,yes,2.059138,3.904858,Frequently,no,2,no,0.821977,0,no,Public_Transportation,Insufficient_Weight
+Female,32.383858,1.640688,46.655622,yes,yes,3,3.11158,Frequently,no,2.721769,no,1.44287,0,no,Public_Transportation,Insufficient_Weight
+Female,24.163526,1.64603,49.839685,yes,yes,3,3.590039,Frequently,no,2.7305,no,0.107078,0,no,Public_Transportation,Insufficient_Weight
+Male,17.038222,1.710564,51.588874,no,yes,2,2.057935,Sometimes,no,2.371015,no,0.288032,0.714627,Sometimes,Public_Transportation,Insufficient_Weight
+Male,16.30687,1.752755,50,no,yes,2.310423,3.558637,Sometimes,no,1.787843,no,1.926592,0.828549,Sometimes,Public_Transportation,Insufficient_Weight
+Male,16.198153,1.691007,52.629374,no,yes,2,2.000986,Sometimes,no,2.673835,no,0.99295,0.474836,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.753349,51.457226,no,yes,2.823179,3,Sometimes,no,1.773236,no,1.252472,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.781543,50.869704,no,yes,2.052932,3,Sometimes,no,2,no,0.520408,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.755823,52.331172,no,yes,2.596364,3,Sometimes,no,2,no,0.281734,1.488372,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.486869,1.836669,58.943347,yes,yes,2.767731,3.821168,Sometimes,no,2,no,2,0.556245,no,Automobile,Insufficient_Weight
+Male,19.920629,1.768493,57.790381,yes,yes,2,3.897078,Sometimes,no,2.622638,no,2,1.894105,no,Automobile,Insufficient_Weight
+Male,18.426619,1.777108,57.145917,yes,yes,2,3.092116,Sometimes,no,2.426465,no,2,1.839862,no,Automobile,Insufficient_Weight
+Female,17.082867,1.640824,43.365005,no,yes,2.815157,3,Sometimes,no,2.911187,no,2.595128,1.380204,Sometimes,Public_Transportation,Insufficient_Weight
+Female,17.000433,1.584951,44.411801,no,yes,2.737762,3,Sometimes,no,2.310921,no,2.240714,1.59257,Sometimes,Public_Transportation,Insufficient_Weight
+Female,16.270434,1.818268,47.124717,no,yes,3,3.286431,Sometimes,no,2.148146,no,2.458237,1.273333,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.908114,1.793926,59.682591,yes,yes,2.568063,4,Sometimes,no,2,no,2,0.220029,no,Automobile,Insufficient_Weight
+Male,17.120699,1.809251,58.968994,yes,yes,2.524428,4,Sometimes,no,2,no,2,0.03838,no,Automobile,Insufficient_Weight
+Male,19.329542,1.767335,55.700497,yes,yes,2,4,Sometimes,no,2.157395,no,2,1.679149,no,Automobile,Insufficient_Weight
+Female,22.377998,1.699568,54.98774,yes,yes,3,3,Frequently,no,2,no,0.139808,0.875464,no,Public_Transportation,Insufficient_Weight
+Female,20.954955,1.759358,55.01045,yes,yes,2.971574,3.592415,Frequently,no,2.894678,no,2,1.009632,no,Public_Transportation,Insufficient_Weight
+Female,22.991668,1.740295,54.166453,yes,yes,3,3,Frequently,no,2.025279,no,2,0.152985,no,Public_Transportation,Insufficient_Weight
+Female,19.300435,1.739354,49.649613,no,yes,3,3.754599,Sometimes,no,1.692112,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,17.065445,1.647811,49.603807,no,yes,3,3.566082,Sometimes,no,1.438398,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.084967,1.768435,49.597765,no,yes,3,3.725797,Sometimes,no,1.191401,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.024853,1.7,50,no,yes,1.0816,3.520555,Frequently,no,1.016968,no,0.651412,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.833682,1.699464,49.676046,no,yes,1.270448,3.731212,Frequently,no,1.876915,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,17.767432,1.74379,50,no,yes,1.344854,4,Frequently,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.504696,1.590317,42.367615,no,no,2.959658,3,Frequently,no,1,no,1.522399,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.950605,1.527133,42,no,no,2.725282,1.259803,Frequently,no,1,no,1.37467,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19,1.530875,42,no,no,2.844607,1.273128,Frequently,no,1.69551,no,0.260079,0.635867,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17,1.848294,59.409018,yes,yes,2.44004,3,Sometimes,no,2,no,2.784471,1,no,Automobile,Insufficient_Weight
+Male,17,1.824414,59.295172,yes,yes,2.432302,3,Sometimes,no,2,no,2.349495,1,no,Automobile,Insufficient_Weight
+Male,18.525525,1.856633,59.258372,yes,yes,2.592247,3.304123,Sometimes,no,2.036764,no,2.038653,1.119877,no,Automobile,Insufficient_Weight
+Female,22.926352,1.715597,50,yes,yes,2.449267,3.647154,Frequently,no,1.266018,no,0.866045,0.097234,no,Public_Transportation,Insufficient_Weight
+Female,21.785351,1.675054,49.945233,yes,yes,2.929889,3,Frequently,no,2.792074,no,0.630944,1.366355,no,Public_Transportation,Insufficient_Weight
+Female,23,1.717601,51.073918,yes,yes,2,3,Frequently,no,1.426874,no,2.20208,2,no,Public_Transportation,Insufficient_Weight
+Female,17.402028,1.710756,50,no,yes,2.015258,3.300666,Sometimes,no,1.315608,no,0.069802,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18,1.7,50,no,yes,1.031149,3,Sometimes,no,1.963628,no,0.028202,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.48207,1.7,50,no,yes,1.592183,3.535016,Sometimes,no,1.086391,no,0.618913,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.53084,1.573816,39.850137,no,yes,1.21498,1.717608,Sometimes,no,1.179942,no,0.684739,2,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.94814,1.530884,39.371523,no,yes,1.522001,3,Sometimes,no,1.98126,no,2.306844,0.720454,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.400053,1.529724,40.343463,no,yes,2.703436,2.884479,Sometimes,no,1.439962,no,0.144627,0.322307,Sometimes,Public_Transportation,Insufficient_Weight
+Male,19.556729,1.767563,56.307019,yes,yes,2.362918,4,Sometimes,no,2.358172,no,2,0.939819,no,Automobile,Insufficient_Weight
+Male,17.70368,1.883364,60,yes,yes,3,3.626815,Sometimes,no,2,no,2.011519,0.456462,no,Automobile,Insufficient_Weight
+Male,18.274358,1.824655,58.621349,yes,yes,2.14084,4,Sometimes,no,2.931438,no,2,1.164457,no,Automobile,Insufficient_Weight
+Male,18,1.840138,60,yes,yes,3,4,Sometimes,no,2,no,2,0,no,Automobile,Insufficient_Weight
+Male,17.210933,1.819557,58.325122,yes,yes,2.5596,4,Sometimes,no,2,no,2,0.331483,no,Automobile,Insufficient_Weight
+Male,17.469417,1.798645,59.612717,yes,yes,2.336044,4,Sometimes,no,2,no,2,0.133005,no,Automobile,Insufficient_Weight
+Male,18,1.7108,50.92538,yes,yes,2,3,Sometimes,no,2,no,0,1.593704,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.70653,51.121749,yes,yes,2,3,Sometimes,no,2,no,0,1.329237,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.716691,51.149283,yes,yes,1.813234,3,Sometimes,no,1.274815,no,0.520407,1.673584,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.010211,1.555431,44.188767,no,no,2.724285,3,Frequently,no,1,yes,1.070331,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.091346,1.603923,45,no,no,3,3,Frequently,no,1.28246,yes,1.60195,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.773303,1.602082,45,no,no,3,3,Frequently,no,2.875583,yes,1.258504,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.483036,1.53777,42,no,yes,3,1,Frequently,no,1.387531,no,0,0.466169,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19,1.538398,42,no,yes,2.71897,1.473088,Frequently,no,1.164644,no,1.235675,0.930926,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.407204,1.520862,42,no,yes,3,1,Frequently,no,1.848703,no,0,0.744555,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.717826,51.7325,yes,yes,1.133844,3,Sometimes,no,1.325326,no,0.227985,1.708309,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18.424941,1.753389,54.121925,yes,yes,2,3.16645,Sometimes,no,2.278578,no,1.838881,2,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.70165,50.157707,yes,yes,1.757466,3,Sometimes,no,1.153559,no,0.833976,1.616045,Sometimes,Public_Transportation,Insufficient_Weight
+Male,19.97981,1.75336,54.997374,yes,yes,2,3.494849,Sometimes,no,2.976672,no,1.94907,2,no,Public_Transportation,Insufficient_Weight
+Female,21.798856,1.672007,49.980968,yes,yes,2.979383,3,Frequently,no,2.975887,no,0.945093,1.241755,no,Public_Transportation,Insufficient_Weight
+Female,22.209706,1.593847,44.050251,no,no,3,2.99321,Frequently,no,2.702783,no,1.967234,0,no,Public_Transportation,Insufficient_Weight
+Female,23.018443,1.584785,44.376637,no,no,2.204914,2.127797,Frequently,no,2.120292,no,0.995735,0,no,Public_Transportation,Insufficient_Weight
+Female,19.376997,1.600264,45,no,no,3,3,Frequently,no,2.949419,yes,1.295697,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.971429,1.549311,41.557468,no,yes,2.927218,1,Frequently,no,1,no,0,0.276592,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.345161,1.534385,41.96525,no,yes,2.88853,1,Frequently,no,1,no,0,0.196224,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.519916,1.725548,49.815599,yes,no,2.890535,3.90779,Frequently,no,1.109115,no,1.548953,0.555468,no,Public_Transportation,Insufficient_Weight
+Male,20.406066,1.868931,63.199726,yes,yes,3,3.699594,Sometimes,no,2.825629,no,1.699592,0.071028,Sometimes,Automobile,Insufficient_Weight
+Female,21.478496,1.686936,51.256059,yes,yes,3,3.179995,Frequently,no,1.910378,no,0.480614,0.625079,no,Public_Transportation,Insufficient_Weight
+Female,18.372563,1.589829,40.202773,no,yes,2,1.075553,Sometimes,no,1,no,0.127425,1.679442,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.931667,1.653209,49.026214,no,no,2.530066,3,Sometimes,no,1.965237,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,22.998709,1.740108,53.65727,yes,yes,2.241606,3,Frequently,no,1.846626,no,2.460238,0.814518,no,Public_Transportation,Insufficient_Weight
+Female,18.006742,1.7,50,no,yes,1.003566,3.238258,Frequently,no,1.014634,no,0.783676,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,34.799519,1.689141,50,yes,yes,2.652779,3.804944,Frequently,no,1.60114,no,0.174692,0.096982,no,Public_Transportation,Insufficient_Weight
+Male,16.496978,1.691206,50,no,yes,2,1.630846,Sometimes,no,2.975528,no,0.548991,0.369134,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.788459,51.552595,no,yes,2.897899,3,Sometimes,no,1.835545,no,1.162519,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.377131,1.811238,58.83071,yes,yes,2.483979,3.762778,Sometimes,no,2,no,2,0.930051,no,Automobile,Insufficient_Weight
+Female,16.611837,1.830068,43.534531,no,yes,2.945967,3,Sometimes,no,2.953192,no,2.830911,1.466667,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.080493,1.782756,56.029418,yes,yes,2,4,Sometimes,no,2.28525,no,2,1.162801,no,Automobile,Insufficient_Weight
+Female,22.970655,1.751691,55.967655,yes,yes,2.478891,3.371832,Frequently,no,2.004126,no,2,0.327376,no,Public_Transportation,Insufficient_Weight
+Female,17.764764,1.78656,50,no,yes,2.784464,4,Sometimes,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.272573,1.71367,50,no,yes,1.005578,4,Frequently,no,1,no,1.683957,0.704978,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19,1.540375,42.006282,no,no,2.938031,2.705445,Frequently,no,1.333807,yes,1.877369,0.370973,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.06713,1.896734,59.895052,yes,yes,2.842102,3.34175,Sometimes,no,2,no,2.511157,0.560351,no,Automobile,Insufficient_Weight
+Female,23,1.710129,50.079991,yes,yes,2,3,Frequently,no,2.685842,no,0.373186,2,no,Public_Transportation,Insufficient_Weight
+Female,18,1.704193,50.631889,no,yes,2,3,Sometimes,no,2,no,0,1.525597,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.054008,1.556611,39.695295,no,yes,1.889199,2.217651,Sometimes,no,2.013605,no,2.075293,1.683261,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.845399,60,yes,yes,3,4,Sometimes,no,2,no,2,0,no,Automobile,Insufficient_Weight
+Male,17.491272,1.834637,59.990861,yes,yes,2.943749,4,Sometimes,no,2,no,2,0.128895,no,Automobile,Insufficient_Weight
+Male,18,1.721854,52.514302,yes,yes,2.33998,3,Sometimes,no,2,no,0.027433,1.884138,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.172661,1.605521,44.661461,no,no,3,2.893778,Frequently,no,1,yes,0.769726,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.220108,1.530266,42,no,yes,3,1,Frequently,no,1.954889,no,0,0.907868,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.71889,52.058335,yes,yes,1.950742,3,Sometimes,no,1.751723,no,0.201136,1.743319,Sometimes,Public_Transportation,Insufficient_Weight
+Male,19.96247,1.756338,54.98234,yes,yes,2.277436,3.502604,Sometimes,no,2.891713,no,1.696294,1.894902,no,Public_Transportation,Insufficient_Weight
+Male,17.580627,1.770324,55.695253,yes,yes,2,4,Sometimes,no,2.369627,no,2,1.612466,no,Automobile,Insufficient_Weight
+Female,21.125098,1.767479,56.265959,yes,yes,2.371338,3.998766,Frequently,no,2.263466,no,2,0.844004,no,Public_Transportation,Insufficient_Weight
+Female,22.033129,1.704223,51.437985,yes,yes,2.984425,3,Frequently,no,2.044694,no,2.008256,1.250871,no,Public_Transportation,Insufficient_Weight
+Female,20.744839,1.667852,49.803921,yes,yes,2.977018,3.193671,Frequently,no,2.482933,no,2,1,no,Public_Transportation,Insufficient_Weight
+Female,22.547298,1.722461,51.881263,yes,yes,2.663421,3,Frequently,no,1.04111,no,0.794402,1.391948,no,Public_Transportation,Insufficient_Weight
+Female,21.837996,1.588046,44.236067,no,no,3,1.69608,Frequently,no,2.550307,no,1.098862,0,no,Public_Transportation,Insufficient_Weight
+Female,23.035829,1.598612,42.993937,no,no,2.753752,2.812377,Frequently,no,2.346647,no,1.612248,0,no,Public_Transportation,Insufficient_Weight
+Female,21.529439,1.592379,44.00945,no,no,3,1.612747,Frequently,no,2.566629,no,1.190465,0,no,Public_Transportation,Insufficient_Weight
+Female,21.287999,1.555778,42.3601,no,no,2.318355,1.082304,Frequently,no,1.220365,no,0.033328,0,no,Public_Transportation,Insufficient_Weight
+Female,23.444286,1.596466,44.594588,no,no,2.594653,1.882158,Frequently,no,1.916812,no,0.417119,0,no,Public_Transportation,Insufficient_Weight
+Female,22.329041,1.598393,44.918255,no,no,2.886157,2.326233,Frequently,no,2.306821,no,1.42237,0,no,Public_Transportation,Insufficient_Weight
+Female,18.91505,1.633316,45,no,yes,3,3,Sometimes,no,2.924594,yes,1.352558,0.220087,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19,1.559567,42.12617,no,yes,3,1.989398,Sometimes,no,2.241607,no,0.206025,0.284453,no,Public_Transportation,Insufficient_Weight
+Female,19.052833,1.546551,42.069992,no,yes,3,1.735493,Sometimes,no,2.318736,no,1.193486,0.74568,no,Public_Transportation,Insufficient_Weight
+Female,19.59904,1.566501,41.706283,no,yes,2.967853,1,Frequently,no,1.131185,no,0,0.504176,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21,1.52,42,no,yes,3,1,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.673262,1.599486,44.810751,no,no,3,2.974568,Frequently,no,1.145761,yes,0.87967,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.244359,1.559186,41.952805,no,yes,2.619835,1,Frequently,no,1.198883,no,0,0.751212,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.552695,1.523426,42,no,yes,3,1,Frequently,no,1.185062,no,0,0.076654,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21,1.52,42,no,yes,3,1,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21.997683,1.689441,51.107925,yes,yes,3,3.715118,Frequently,no,1.774576,no,0.10297,0.646423,no,Public_Transportation,Insufficient_Weight
+Female,21.274628,1.737453,50.479039,yes,yes,3,3.489918,Frequently,no,1.326694,no,0.791929,0.128394,no,Public_Transportation,Insufficient_Weight
+Female,18.909439,1.732096,50,no,yes,1.053534,3.378859,Sometimes,no,1,no,1.853425,0.861809,Sometimes,Public_Transportation,Insufficient_Weight
+Male,22.396504,1.869098,61.411141,yes,yes,3,3.263201,Sometimes,no,2.233274,no,1.557737,0.000355,Sometimes,Automobile,Insufficient_Weight
+Male,19.084317,1.851123,61.264785,yes,yes,3,3.994588,Sometimes,no,2.548527,no,1.285976,0.267076,Sometimes,Automobile,Insufficient_Weight
+Male,21.084625,1.787264,58.585146,yes,yes,2.530233,3.24934,Sometimes,no,2.387945,no,1.976341,1.672508,no,Automobile,Insufficient_Weight
+Male,18.068767,1.787787,52,yes,yes,3,3,Sometimes,no,2,no,0.854337,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21.813083,1.712515,51.710723,yes,yes,2.8813,3.087544,Frequently,no,1.24818,no,1.952427,0.427461,no,Public_Transportation,Insufficient_Weight
+Male,18.038422,1.698914,51.692272,yes,yes,2.824559,3,Sometimes,no,2,no,0.533309,1.099764,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.874591,1.533609,41.669346,no,yes,2.762325,1.163666,Sometimes,no,1.30491,no,0.25289,1.001405,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.21164,1.567981,41.934368,no,yes,2.070964,1,Sometimes,no,1.676975,no,0,1.718513,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.988581,1.544263,41.535047,no,yes,2.68601,1,Sometimes,no,1.310074,no,0,1.0647,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.408999,1.670552,49.804245,no,yes,2.794197,3.409363,Sometimes,no,1.543021,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.665881,1.676346,49.105025,no,no,2.720701,3,Sometimes,no,2,no,1.862235,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.758286,1.667404,49.125955,no,no,2.880792,3.281391,Sometimes,no,1.960131,no,1.513029,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,22.422674,1.70011,50.173425,yes,yes,2.674431,3,Frequently,no,2.453384,no,1.321624,1.990617,no,Public_Transportation,Insufficient_Weight
+Male,20.242237,1.75633,54.567343,yes,yes,2,3.98525,Sometimes,no,2.654078,no,2.113151,2,no,Public_Transportation,Insufficient_Weight
+Female,22.637018,1.703584,51.607091,yes,yes,2.55996,3,Frequently,no,1.990788,no,0.486006,1.561272,no,Public_Transportation,Insufficient_Weight
+Female,19.007177,1.690727,49.895716,no,yes,1.212908,3.207071,Sometimes,no,1.029703,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.288205,1.713564,50,no,yes,1.140615,3.471536,Sometimes,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.314429,1.67231,49.713944,no,yes,2.562409,3.488342,Sometimes,no,1.67062,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,27.14823,1.660446,49.558304,yes,yes,2.004146,3.443456,Frequently,no,2.020764,no,1.078719,1.26729,no,Public_Transportation,Insufficient_Weight
+Female,25.380557,1.630379,46.062954,yes,yes,2.690754,3.03779,Frequently,no,2.212296,no,1.616882,0,no,Public_Transportation,Insufficient_Weight
+Female,22.867719,1.655413,50.424661,yes,yes,3,3.642802,Frequently,no,1.995177,no,0.118271,0.065515,no,Public_Transportation,Insufficient_Weight
+Male,17.729923,1.732862,51.216463,no,yes,2.051283,2.645858,Sometimes,no,2.35752,no,0.291309,0.897942,Sometimes,Public_Transportation,Insufficient_Weight
+Female,16.834813,1.74402,50,no,yes,2.19005,3.420618,Sometimes,no,1.356405,no,1.351996,0.98468,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.521754,1.757958,52.09432,no,yes,2.21498,2.64155,Sometimes,no,2.121251,no,0.998391,0.85882,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.786758,51.524444,no,yes,2.91548,3,Sometimes,no,1.777486,no,1.077469,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.767058,51.132809,yes,yes,2.708965,3,Sometimes,no,1.873004,no,1.21718,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18.128249,1.699437,52.08657,yes,yes,2.853513,3,Sometimes,no,2,no,0.680464,1.258881,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.405104,1.82525,58.913579,yes,yes,2.580872,3.887906,Sometimes,no,2,no,2,0.453649,no,Automobile,Insufficient_Weight
+Male,19.72925,1.793315,58.19515,yes,yes,2.508835,3.435905,Sometimes,no,2.076933,no,2.026668,1.443328,no,Automobile,Insufficient_Weight
+Male,18.525834,1.776989,57.275946,yes,yes,2,3.747163,Sometimes,no,2.575535,no,2,1.84383,no,Automobile,Insufficient_Weight
+Female,18.595614,1.609495,42.656563,no,yes,2.896562,2.625475,Sometimes,no,2.839558,yes,1.949667,1.253311,no,Public_Transportation,Insufficient_Weight
+Female,16.613108,1.777929,44.762023,no,yes,2.911877,3.098399,Sometimes,no,2.196405,no,2.328147,1.55011,Sometimes,Public_Transportation,Insufficient_Weight
+Female,16.928791,1.710948,45.248627,no,yes,2.910733,3.12544,Sometimes,no,2.204263,no,2.407906,1.403037,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.758315,1.854162,59.881316,yes,yes,2.966126,3.96981,Sometimes,no,2,no,2.000088,0.378619,no,Automobile,Insufficient_Weight
+Male,17.282945,1.821514,59.605028,yes,yes,2.613249,3.712183,Sometimes,no,2,no,2.002997,0.174848,no,Automobile,Insufficient_Weight
+Male,19.993154,1.762073,55.511075,yes,yes,2,4,Sometimes,no,2.542415,no,2,1.807465,no,Automobile,Insufficient_Weight
+Female,22.935612,1.732307,54.835576,yes,yes,3,3,Frequently,no,2.00357,no,1.25955,0.417116,no,Public_Transportation,Insufficient_Weight
+Male,20.738469,1.759933,55.003417,yes,yes,2.627031,3.832911,Sometimes,no,2.993448,no,2,1.425903,no,Public_Transportation,Insufficient_Weight
+Female,22.99368,1.741377,54.877111,yes,yes,3,3,Frequently,no,2.009796,no,2,0.071317,no,Public_Transportation,Insufficient_Weight
+Female,19.317148,1.731195,49.650897,no,yes,2.919751,3.576103,Sometimes,no,1.949308,no,1.940182,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,16.910997,1.74823,49.928447,no,yes,2.494451,3.56544,Sometimes,no,1.491268,no,1.951027,0.956204,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.071027,1.756865,49.699672,no,yes,1.69427,3.266644,Sometimes,no,1.095417,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.019572,1.701378,50.088468,no,yes,1.601236,3.433908,Sometimes,no,1.055019,no,0.819269,1.030848,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.735968,1.699956,49.98297,no,yes,1.204855,3.531038,Sometimes,no,1.134658,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.094079,1.723328,50,no,yes,1.052699,3.998618,Frequently,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.054938,1.585886,42.541794,no,no,2.910345,3,Frequently,no,1,yes,1.461005,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.850119,1.524926,42,no,yes,2.866383,1.226342,Frequently,no,1,no,0.14495,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.349258,1.52337,42,no,yes,2.913486,1.060796,Frequently,no,1.596212,no,0.108948,0.773087,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17,1.844749,59.313525,yes,yes,2.432886,3,Sometimes,no,2,no,2.697949,1,no,Automobile,Insufficient_Weight
+Male,17.203917,1.853325,59.619485,yes,yes,2.883745,3.595761,Sometimes,no,2,no,2.077653,0.714701,no,Automobile,Insufficient_Weight
+Male,17.671064,1.854706,59.20945,yes,yes,2.707666,3.737914,Sometimes,no,2.026547,no,2.009397,0.613971,no,Automobile,Insufficient_Weight
+Female,21.310907,1.72064,50,yes,yes,2.919584,3.697831,Frequently,no,1.147121,no,0.993058,0.08922,no,Public_Transportation,Insufficient_Weight
+Female,21.708354,1.704167,50.165754,yes,yes,2.969205,3.21043,Frequently,no,2.765876,no,0.611037,1.300692,no,Public_Transportation,Insufficient_Weight
+Female,22.176922,1.71746,51.491957,yes,yes,2.486189,3,Frequently,no,1.064378,no,2.206055,1.958089,no,Public_Transportation,Insufficient_Weight
+Female,17.823438,1.708406,50,no,yes,1.642241,3.45259,Sometimes,no,1.099231,no,0.418875,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18,1.763465,50.279053,no,yes,1.567101,3,Sometimes,no,1.994139,no,0.107981,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.281092,1.7,50,no,yes,1.036414,3.205587,Sometimes,no,1.745959,no,0.115974,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.656912,1.574017,41.220175,no,yes,1.649974,1.513835,Sometimes,no,1.549974,no,0.227802,1.972926,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.994543,1.537739,39.101805,no,yes,1.118436,3,Sometimes,no,1.997744,no,2.432443,1.626194,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.255616,1.534223,41.268597,no,yes,2.673638,2.779379,Frequently,no,1.249074,no,0.043412,0.403694,Sometimes,Public_Transportation,Insufficient_Weight
+Male,19.865895,1.76033,55.3707,yes,yes,2.120185,4,Sometimes,no,2.582165,no,2,1.510609,no,Automobile,Insufficient_Weight
+Male,17.888073,1.841879,60,yes,yes,3,3.732126,Sometimes,no,2,no,2.000934,0.384662,no,Automobile,Insufficient_Weight
+Male,18.470562,1.856406,58.673963,yes,yes,2.34222,3.937099,Sometimes,no,2.311791,no,2.013377,1.128355,no,Automobile,Insufficient_Weight
+Male,17.925497,1.829142,59.933015,yes,yes,2.86099,4,Sometimes,no,2,no,2,0.007872,no,Automobile,Insufficient_Weight
+Male,17.000752,1.822084,58.443049,yes,yes,2.559571,3.047959,Sometimes,no,2,no,2.011646,0.588994,no,Automobile,Insufficient_Weight
+Male,17.362129,1.80671,59.243506,yes,yes,2.424977,4,Sometimes,no,2,no,2,0.03921,no,Automobile,Insufficient_Weight
+Male,18,1.707259,50.664459,yes,yes,1.786841,3,Sometimes,no,1.546856,no,0.512094,1.608265,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.708107,51.314659,yes,yes,1.303878,3,Sometimes,no,1.755497,no,0.062932,1.672532,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.739344,50.951444,yes,yes,1.889883,3,Sometimes,no,1.959531,no,0.520407,1.151166,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.029494,1.573987,44.316254,no,no,2.984004,3,Frequently,no,1.015249,yes,1.327833,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.269079,1.58092,44.753943,no,no,3,2.975362,Frequently,no,1.246822,yes,0.979701,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.946244,1.603435,45,no,no,3,3,Frequently,no,2.487919,yes,1.230441,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.639431,1.53535,42,no,yes,3,1,Frequently,no,1.49681,no,0,0.513427,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19,1.53161,42,no,yes,2.749268,1.394539,Frequently,no,1.322048,no,0.463949,0.800993,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.434709,1.525691,42,no,yes,3,1,Frequently,no,1.764055,no,0,0.560887,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.719827,52.289828,yes,yes,1.202075,3,Sometimes,no,1.927976,no,0.023574,1.747256,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18.381382,1.722547,53.783977,yes,yes,2,3.131032,Sometimes,no,2.072194,no,1.487987,2,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.738702,50.248677,yes,yes,1.871213,3,Sometimes,no,1.283738,no,0.684879,1.487223,Sometimes,Public_Transportation,Insufficient_Weight
+Male,26.69858,1.816298,86.963765,no,yes,2.341133,1.578521,Sometimes,no,2,no,2,0.554072,no,Automobile,Overweight_Level_I
+Male,21.125836,1.638085,70,no,yes,2,1,no,no,2.115967,no,0.770536,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,25.191627,1.813678,85.637789,no,yes,1.450218,3.985442,Sometimes,no,2.147746,no,0.046836,1,no,Automobile,Overweight_Level_I
+Male,21.963457,1.697228,75.5771,yes,yes,2.204914,3.623364,Sometimes,no,1.815293,no,0.989316,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.617469,68.869791,no,yes,1.206276,3,no,no,2.406541,no,0.94984,0.479221,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.114981,1.855543,88.965521,yes,yes,2,3,Sometimes,no,1.015677,no,0,0.37465,Sometimes,Public_Transportation,Overweight_Level_I
+Female,41.823567,1.721854,82.919584,no,yes,2.81646,3.36313,Sometimes,no,2.722063,no,3,0.26579,Sometimes,Automobile,Overweight_Level_I
+Male,21.142432,1.855353,86.413388,yes,yes,2,3,Sometimes,no,1.345298,no,1.097905,1,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.962426,1.696336,75,yes,yes,2,3,Sometimes,no,1.825629,no,0.699592,0.142056,Sometimes,Public_Transportation,Overweight_Level_I
+Female,18.836315,1.751631,80,yes,yes,2,1.73762,Sometimes,no,2.207978,no,2.641072,0.707044,Frequently,Public_Transportation,Overweight_Level_I
+Male,23.572648,1.698346,75,yes,yes,2,3,Sometimes,no,2,no,0.345684,1.415536,Sometimes,Public_Transportation,Overweight_Level_I
+Female,33.700749,1.642971,74.803157,yes,no,3,1.146052,Sometimes,no,1.074048,no,0.679935,0,Sometimes,Automobile,Overweight_Level_I
+Female,21.845025,1.613668,68.126955,yes,yes,1.758394,3.981997,Sometimes,no,2.174248,no,0.920476,1.358163,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.605404,68.226511,no,yes,2,1.9154,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,35.125401,1.529834,62.903938,no,yes,2,2.105616,Sometimes,no,1.456581,no,0,0.9974,Sometimes,Automobile,Overweight_Level_I
+Female,36.769646,1.55,62.337721,yes,yes,2.392665,3,Sometimes,no,2.951056,no,0,0.369134,Sometimes,Automobile,Overweight_Level_I
+Female,21.868932,1.731261,78.175706,yes,yes,2.577427,1,Sometimes,no,2,no,2.287423,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.549208,1.629194,70,no,yes,2,1,no,no,2.803311,no,0.245354,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,30.958957,1.633491,68.803694,yes,yes,2.052152,3,Sometimes,no,1.972074,no,0.228307,0,Sometimes,Automobile,Overweight_Level_I
+Male,21.017383,1.752391,79.109637,yes,yes,3,1,Sometimes,no,2,no,2.57223,0,Sometimes,Automobile,Overweight_Level_I
+Male,19.623574,1.818226,85.833029,yes,yes,2.954996,3.714833,Sometimes,no,2.430918,no,2.545931,0.469735,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.637947,1.809101,85,yes,yes,3,3,Sometimes,no,2.229171,no,1.607953,0.628059,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.413642,1.709484,75,yes,yes,2,3,Sometimes,no,2.843777,no,2.022315,0.009254,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.029633,1.607082,67.722222,yes,yes,2,3.691226,no,no,3,no,1.228136,0.3352,Sometimes,Public_Transportation,Overweight_Level_I
+Female,22.667596,1.718939,75.951472,yes,yes,2,3,Frequently,no,2,no,0,2,Sometimes,Public_Transportation,Overweight_Level_I
+Male,20,1.831357,89.652557,yes,yes,2.555401,3.292386,Sometimes,no,3,no,2,0.315918,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.81119,1.6,66.401407,yes,yes,2.108711,3,Sometimes,no,2.746197,no,1.910432,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21,1.754813,77.929204,yes,yes,2.915279,1.104642,Sometimes,no,1.530046,no,1.360635,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,18.128206,1.778447,80.273807,yes,yes,1.570089,3,Sometimes,no,2,no,2.707882,0,no,Public_Transportation,Overweight_Level_I
+Male,21,1.709561,75,yes,yes,2,3,Sometimes,no,1.636326,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,29.08107,1.674568,71.602622,yes,yes,2,3,Sometimes,no,1.701835,no,1.682271,0,Sometimes,Automobile,Overweight_Level_I
+Male,26.358919,1.755317,82.228794,yes,yes,1.94313,3.559841,Sometimes,no,2.367996,no,0.23553,0.879977,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.717249,1.708071,75,yes,yes,2.714447,3,Sometimes,no,3,no,1.096818,1.89388,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.816949,1.806947,85.079589,yes,yes,2,3,Sometimes,no,2.292073,no,1.067817,0.19668,Sometimes,Public_Transportation,Overweight_Level_I
+Female,24.023972,1.599697,64.808022,no,yes,2.903545,3,Sometimes,no,1.549931,no,0,0.273882,Sometimes,Public_Transportation,Overweight_Level_I
+Female,32.593129,1.721903,72.748903,yes,yes,2,3,Sometimes,no,1,no,0,1.339232,Sometimes,Automobile,Overweight_Level_I
+Male,23,1.712556,75.480764,yes,yes,3,3.654061,Sometimes,no,1.229915,no,0.793489,1.597608,Sometimes,Public_Transportation,Overweight_Level_I
+Female,16.941489,1.551288,54.93242,no,yes,1.75375,1.296156,Sometimes,no,2,yes,0.110174,1.944675,Sometimes,Public_Transportation,Overweight_Level_I
+Female,16.172992,1.603842,65,yes,yes,2.543563,1,Sometimes,no,2,yes,0.694281,1.056911,Sometimes,Public_Transportation,Overweight_Level_I
+Female,23.71259,1.588597,62.339003,no,yes,2.39728,2.656588,Sometimes,no,2.061062,no,1.912981,1.887386,Sometimes,Public_Transportation,Overweight_Level_I
+Female,35.194089,1.673482,73.193589,yes,no,3,2.809716,Sometimes,no,1.591425,no,0.178023,0,Sometimes,Automobile,Overweight_Level_I
+Male,23.172982,1.858624,88.675503,yes,yes,2,2.77684,Sometimes,no,1.278231,no,0.55713,1,Sometimes,Automobile,Overweight_Level_I
+Female,37.218161,1.593894,63.320629,yes,yes,2.37464,3,Sometimes,no,2,no,2.892922,0.480813,Sometimes,Automobile,Overweight_Level_I
+Male,19.076443,1.62,69.529625,no,yes,2.278644,1,Sometimes,no,2.628985,no,1,1.285373,Sometimes,Public_Transportation,Overweight_Level_I
+Female,16.30687,1.616366,67.183128,yes,yes,1.620845,1,Sometimes,no,2,yes,0.926592,1.657098,no,Public_Transportation,Overweight_Level_I
+Female,18.297229,1.637396,70,yes,yes,2,1.999014,Sometimes,no,2.326165,no,0.00705,0,no,Public_Transportation,Overweight_Level_I
+Female,18.610761,1.550723,60.628321,no,yes,2.823179,2.644692,Sometimes,no,1.226764,yes,0.747528,0.008899,Sometimes,Public_Transportation,Overweight_Level_I
+Female,17.451085,1.6,65,yes,yes,3,2.449723,Sometimes,no,2,yes,0.479592,1.720642,Sometimes,Public_Transportation,Overweight_Level_I
+Male,29.740496,1.820471,87.668828,no,yes,3,3,Sometimes,no,1.174611,no,2,0,no,Automobile,Overweight_Level_I
+Female,31.539393,1.657496,73.641633,yes,yes,2,3,Sometimes,no,1,no,0,1.112489,Sometimes,Automobile,Overweight_Level_I
+Male,18.026457,1.752123,80,yes,yes,2,2.794156,Sometimes,no,2.377362,no,0.101236,0.105895,no,Public_Transportation,Overweight_Level_I
+Male,19.47554,1.857231,88.138777,yes,yes,2.061952,4,Sometimes,no,2.426465,no,2,0.160138,Sometimes,Public_Transportation,Overweight_Level_I
+Female,19,1.76,79.544998,yes,yes,2,1.146794,Sometimes,no,3,no,1.809745,1.690102,Frequently,Public_Transportation,Overweight_Level_I
+Female,18,1.644682,68.392133,yes,yes,2,1.131695,Sometimes,no,1.344539,no,0,1.59257,no,Public_Transportation,Overweight_Level_I
+Female,18.270434,1.737165,76.699774,yes,yes,2.838969,3,Sometimes,no,2.425927,no,1,1.453335,Frequently,Public_Transportation,Overweight_Level_I
+Female,19.816228,1.507853,64.259379,no,yes,2.568063,3,Sometimes,no,1.817983,yes,0,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,28.39124,1.81006,87.286783,no,yes,3,3,Sometimes,no,1.205633,no,2,0,no,Automobile,Overweight_Level_I
+Male,26.758516,1.80179,86.981202,no,yes,2.652958,2.488189,Sometimes,no,2,no,2,0.096614,no,Automobile,Overweight_Level_I
+Male,21.033794,1.625891,70,no,yes,2,1,no,no,2.00876,no,0.63119,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.429812,1.64037,70,no,yes,2,1,no,no,2.069184,no,0.729898,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,26.650419,1.791047,83.26312,no,yes,1.27785,3.999591,Sometimes,no,2.495944,no,0.00342,1,no,Automobile,Overweight_Level_I
+Male,21.618246,1.679306,75,yes,yes,2,3,Sometimes,no,1.957871,no,0.994592,0.117303,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.012542,1.615145,68.653347,yes,yes,1.729824,3.612941,no,no,2.043703,no,0.562118,0.426643,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.071195,1.616467,68.77185,yes,yes,1.452524,3.950553,no,no,2.109858,no,0.508847,1.194633,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.741202,1.816783,86.522799,yes,yes,2,3,Sometimes,no,2.177386,no,0.336814,0.004813,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.21638,1.812472,86.748295,yes,yes,2,3,Sometimes,no,2.168651,no,0.977998,0.142638,Sometimes,Public_Transportation,Overweight_Level_I
+Female,42.24475,1.768231,75.62931,yes,yes,3,2.951837,Sometimes,no,2.112032,no,0.378683,0,Sometimes,Automobile,Overweight_Level_I
+Male,22.283082,1.870931,89.251639,yes,yes,2,2.799979,Sometimes,no,1.081333,no,0.444347,1,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.086512,1.863685,89.558854,yes,yes,2,3,Sometimes,no,1.005727,no,0,0.798219,Sometimes,Public_Transportation,Overweight_Level_I
+Male,23.451595,1.670227,75,yes,yes,2,3,Sometimes,no,2,no,0.129163,1.983678,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.740275,1.717288,75.948164,yes,yes,2,3,Sometimes,no,2,no,0,2,Sometimes,Public_Transportation,Overweight_Level_I
+Male,18.900253,1.750359,79.828725,yes,yes,2,2.228113,Sometimes,no,2.045004,no,1.293665,0.961806,Frequently,Public_Transportation,Overweight_Level_I
+Male,23.170309,1.707557,75.306702,yes,yes,2.303367,3.042774,Sometimes,no,1.277636,no,0.944982,0.366126,Sometimes,Public_Transportation,Overweight_Level_I
+Female,32.997118,1.672446,74.812707,yes,yes,2.948425,1.198643,Sometimes,no,1,no,0,0.173796,Sometimes,Automobile,Overweight_Level_I
+Female,32.501143,1.675979,74.959747,yes,yes,2.291846,1.555557,Sometimes,no,1,no,0,0.388271,Sometimes,Automobile,Overweight_Level_I
+Female,21.938831,1.611239,67.939653,yes,yes,1.906194,3.987707,Sometimes,no,2.597746,no,1.922234,1.376124,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.909534,1.611356,68.06609,yes,yes,1.834155,3.995957,Sometimes,no,2.632871,no,1.236114,1.980875,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,37.455752,1.508908,63.183846,yes,yes,2.048582,1.047197,Sometimes,no,2,no,0.15136,0.22596,Sometimes,Automobile,Overweight_Level_I
+Female,40,1.561109,62.871794,yes,yes,2.948248,3,Sometimes,no,2.429911,no,0.11964,0.360193,Sometimes,Automobile,Overweight_Level_I
+Female,38.943282,1.554728,63.011645,yes,yes,2.869436,3,Sometimes,no,2.972426,no,2.16079,0.305954,Sometimes,Automobile,Overweight_Level_I
+Female,21.987341,1.730182,78.55444,yes,yes,2.293705,1,Sometimes,no,2,no,2.063943,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.198217,1.743841,78.88036,yes,yes,2.510583,1,Sometimes,no,2,no,2.119682,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,29.32038,1.642506,69.906708,yes,yes,2.366949,3,Sometimes,no,1.926577,no,1.581242,0,Sometimes,Automobile,Overweight_Level_I
+Female,31.255587,1.69308,71.927379,yes,yes,2.615788,3,Sometimes,no,1.771781,no,0.759422,0.686529,Sometimes,Automobile,Overweight_Level_I
+Male,22.851773,1.752115,79.414603,yes,yes,3,1,Sometimes,no,2,no,2.315983,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,26,1.745033,80,yes,yes,2.217267,1.193589,Sometimes,no,2,no,2,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.789291,1.820643,85,yes,yes,3,3,Sometimes,no,2.759246,no,1.763277,0.453314,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.895877,1.80733,85.073801,yes,yes,2.801514,3,Sometimes,no,2.734782,no,2.207881,0.143675,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.693804,1.8,85,yes,yes,2.188722,3,Sometimes,no,2.721356,no,1.528968,1,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.226314,1.814255,85.255631,yes,yes,2.971351,3.390143,Sometimes,no,2.133767,no,2.318492,0.678618,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.652229,1.700181,75.057177,yes,yes,2.086093,3.546352,Sometimes,no,2.081121,no,1.993666,0.673835,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.687409,1.604697,68.016472,yes,yes,1.901611,3.266501,no,no,2.926995,no,0.985872,0.307982,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.455463,1.611,68.107866,yes,yes,1.977298,3.554974,no,no,2.217957,no,0.97812,0.459759,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21,1.754497,77.956921,yes,yes,2.446872,2.372311,Sometimes,no,1.81031,no,0.094893,1.650778,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21,1.752944,77.965532,yes,yes,2.839048,2.10601,Sometimes,no,1.639202,no,1.117311,0.967919,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.337404,1.859927,87.105828,yes,yes,2.21232,4,Sometimes,no,2.374044,no,2,0.622638,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.701643,1.896073,90.52446,yes,yes,2.427689,3.715306,Sometimes,no,2.876096,no,1.815768,0.426465,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.620245,1.605314,66.621646,yes,yes,2.357496,3.376717,Sometimes,no,2.184843,no,1.358185,0.014885,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.012569,1.758628,78.370039,yes,yes,3,1,Sometimes,no,2,no,2.971832,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.016623,1.755427,78.300084,yes,yes,3,1,Sometimes,no,2,no,2.877473,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,19,1.779882,80.091886,yes,yes,1.078529,1.211606,Sometimes,no,2.568063,no,3,0.817983,no,Public_Transportation,Overweight_Level_I
+Male,21,1.712061,75,yes,yes,2,3,Sometimes,no,1.333707,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.676014,75,yes,yes,2,3,Sometimes,no,1.164062,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,27.899784,1.7,74.244004,yes,yes,2,3,Sometimes,no,2,no,1.722053,0,Sometimes,Automobile,Overweight_Level_I
+Male,28.825223,1.765874,82.045045,yes,yes,1.064162,3.98955,Sometimes,no,2.028426,no,0.81517,0.894678,Sometimes,Public_Transportation,Overweight_Level_I
+Male,26.047077,1.74595,80.018571,yes,yes,1.993101,3.171082,Sometimes,no,2.364498,no,1.224743,0.022245,Sometimes,Public_Transportation,Overweight_Level_I
+Male,23,1.690196,75,yes,yes,2.620963,3,Sometimes,no,2.824559,no,0.754599,2,Sometimes,Public_Transportation,Overweight_Level_I
+Male,20,1.81748,85,yes,yes,2.95118,3,Sometimes,no,3,no,2.433918,0.561602,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.832995,1.580964,65.363941,no,yes,2.021446,3,Sometimes,no,1.077917,no,0.523847,0.808599,Sometimes,Public_Transportation,Overweight_Level_I
+Female,30.255744,1.652084,73.575981,yes,yes,2,3,Sometimes,no,1,no,0,0.018877,Sometimes,Automobile,Overweight_Level_I
+Female,30.613586,1.645511,69.168978,yes,yes,2.000466,3,Sometimes,no,1.131448,no,0.061366,0,Sometimes,Automobile,Overweight_Level_I
+Male,23.245408,1.707968,75.383716,yes,yes,2.5621,3.105007,Sometimes,no,1.172427,no,0.184917,0.923082,Sometimes,Public_Transportation,Overweight_Level_I
+Female,18,1.456346,55.523481,no,yes,2,3,Sometimes,no,1.040342,yes,0.497373,1.783319,Sometimes,Public_Transportation,Overweight_Level_I
+Female,18,1.498561,55.376512,no,yes,2,3,Sometimes,no,1.274718,yes,0.129902,0.978574,Sometimes,Public_Transportation,Overweight_Level_I
+Female,16.950499,1.603501,65,yes,yes,2.96008,1,Sometimes,no,2,yes,0.736032,1.344072,no,Public_Transportation,Overweight_Level_I
+Female,21.837058,1.558045,63.597633,no,yes,2.53915,1.590982,Sometimes,no,1.977801,no,1.264616,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,27,1.55,62.877347,no,yes,2.244142,1.704828,Sometimes,no,1,no,0.792929,0.395468,Sometimes,Public_Transportation,Overweight_Level_I
+Female,35.483601,1.647514,73.91692,yes,no,3,2.725012,Sometimes,no,1.62244,no,0.902661,0,Sometimes,Automobile,Overweight_Level_I
+Male,23.801436,1.855779,86.098523,yes,yes,2,2.372339,Sometimes,no,1.675733,no,1.066241,1,Sometimes,Automobile,Overweight_Level_I
+Male,23.367212,1.853223,87.083266,yes,yes,2,1.097312,Sometimes,no,1.328835,no,1.264257,1,Sometimes,Automobile,Overweight_Level_I
+Female,40,1.570811,62.211572,yes,yes,2.253371,3,Sometimes,no,2.756916,no,1.016042,0.213437,Sometimes,Automobile,Overweight_Level_I
+Female,16.38009,1.617124,67.913561,yes,yes,2.851664,1,Sometimes,no,2,no,0.977929,1.765321,no,Public_Transportation,Overweight_Level_I
+Female,16.865984,1.644053,67.439589,yes,yes,1.31415,1.068196,Sometimes,no,1.364957,yes,0,0.057926,no,Public_Transportation,Overweight_Level_I
+Female,16.093234,1.608914,65,yes,yes,1.321028,1,Sometimes,no,2,yes,0.371452,0.965604,no,Public_Transportation,Overweight_Level_I
+Female,18,1.647971,68.818893,yes,yes,2,1.411685,Sometimes,no,1.859089,no,0,1.306,no,Public_Transportation,Overweight_Level_I
+Female,18.83919,1.624831,69.975607,yes,yes,2.253998,2.752318,Sometimes,no,2.328331,no,0.815509,0.024088,no,Public_Transportation,Overweight_Level_I
+Female,19.726522,1.508267,61.10403,no,yes,2.778079,3,Sometimes,no,1.696691,yes,0.94224,0.879925,Sometimes,Public_Transportation,Overweight_Level_I
+Female,18.947102,1.518917,60.267427,no,yes,2.838037,2.737571,Sometimes,no,1.144467,yes,0.753782,1.286844,Sometimes,Public_Transportation,Overweight_Level_I
+Female,17.781183,1.6,65,yes,yes,3,2.973504,Sometimes,no,2,yes,0.178976,0.166076,Sometimes,Public_Transportation,Overweight_Level_I
+Male,33.100581,1.838791,87.85785,no,yes,2.814453,2.608055,Sometimes,no,1.85916,no,2,0.516084,no,Automobile,Overweight_Level_I
+Female,33.251015,1.730379,75.920519,yes,yes,2.013782,2.625942,Sometimes,no,1.045586,no,0.553305,0.480214,Sometimes,Automobile,Overweight_Level_I
+Female,18.985119,1.759117,80,yes,yes,2,1.411808,Sometimes,no,2.651194,no,2.878414,0.953841,no,Public_Transportation,Overweight_Level_I
+Female,18.871917,1.755254,80,yes,yes,2,1.095223,Sometimes,no,2.474132,no,2.876696,0.7939,no,Public_Transportation,Overweight_Level_I
+Male,19.478533,1.804099,85.196279,yes,yes,2.459976,3.30846,Sometimes,no,2.078011,no,1.779646,0.871772,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.429723,1.813567,86.144904,yes,yes,2.643183,3.821461,Sometimes,no,2.397124,no,2.044165,0.471663,Sometimes,Public_Transportation,Overweight_Level_I
+Female,20.979254,1.75655,78.721696,yes,yes,2,3,Sometimes,no,2.813234,no,0.228753,2,Frequently,Public_Transportation,Overweight_Level_I
+Female,18.869151,1.756774,79.989789,yes,yes,2,2.658639,Sometimes,no,2.781628,no,1.955992,1.409198,Frequently,Public_Transportation,Overweight_Level_I
+Female,18.236087,1.62,69.3899,yes,yes,2.22399,1,Sometimes,no,2.20312,no,0.104451,1.899073,no,Public_Transportation,Overweight_Level_I
+Female,20.901393,1.709585,75,yes,yes,2.104105,3,Sometimes,no,1.871033,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,17.08525,1.535618,57.259124,no,yes,1.972545,2.339614,Sometimes,no,1.711074,yes,0.095517,1.191053,Sometimes,Public_Transportation,Overweight_Level_I
+Male,25.785925,1.818848,87.032398,no,yes,2.286481,1.713762,Sometimes,no,1.797161,no,1.658698,0.926581,no,Automobile,Overweight_Level_I
+Male,26.787842,1.817641,87.107317,no,yes,2.971588,2.743277,Sometimes,no,1.394883,no,2,0.470243,no,Automobile,Overweight_Level_I
+Male,21.037514,1.636592,70,no,yes,2,1,no,no,2.881677,no,0.915699,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.00829,1.621412,70,no,yes,2,1,no,no,2.795651,no,0.80873,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,26.70371,1.802871,85.770013,yes,yes,2.872121,3.051804,Sometimes,no,2.111913,no,0.843709,0.153559,Sometimes,Automobile,Overweight_Level_I
+Male,21.731497,1.696201,75.399191,yes,yes,2.109162,3.245148,Sometimes,no,1.971774,no,0.989335,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.052894,1.694633,75.0522,yes,yes,2.178889,3.563744,Sometimes,no,1.853991,no,0.997731,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21,1.618148,68.981403,yes,yes,1.142468,3,no,no,2.197732,no,0.827506,0.572877,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.241058,1.856811,88.633616,yes,yes,2.047069,3.829101,Sometimes,no,1.641022,no,1.554817,0.248218,Sometimes,Public_Transportation,Overweight_Level_I
+Female,38.939448,1.738321,86.934846,no,yes,2.843709,3.058539,Sometimes,no,1.130079,no,2.834373,0.044954,Sometimes,Automobile,Overweight_Level_I
+Female,39.965474,1.739293,80.914382,no,yes,2.416044,3.196043,Sometimes,no,1.352649,no,0.148628,1.08266,Sometimes,Automobile,Overweight_Level_I
+Male,20.261925,1.807538,85.316125,yes,yes,2,3,Sometimes,no,1.968011,no,1.072662,0.776758,Sometimes,Public_Transportation,Overweight_Level_I
+Male,20.31094,1.849425,85.228116,yes,yes,2.146598,3,Sometimes,no,2.100112,no,1.17116,0.833761,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.420537,1.712473,75,yes,yes,2,3,Sometimes,no,1.362642,no,0.921268,0.139013,Sometimes,Public_Transportation,Overweight_Level_I
+Female,18.845518,1.753471,80,yes,yes,2,1.391778,Sometimes,no,2.64548,no,2.685087,0.97554,no,Public_Transportation,Overweight_Level_I
+Male,23.562135,1.717432,75.371244,yes,yes,2,3,Sometimes,no,2,no,0.121585,1.967259,Sometimes,Public_Transportation,Overweight_Level_I
+Male,23.118327,1.677573,75,yes,yes,2,3,Sometimes,no,2,no,0.334579,1.905826,Sometimes,Public_Transportation,Overweight_Level_I
+Female,33.732714,1.679725,74.885222,yes,yes,3,1.13715,Sometimes,no,1.000695,no,0.261274,0,Sometimes,Automobile,Overweight_Level_I
+Female,21.571288,1.600914,68.058902,yes,yes,1.766849,3.322522,Sometimes,no,2.616285,no,0.943058,0.256977,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.165574,1.617749,68.908136,yes,yes,1.188089,3.269088,Sometimes,no,2.039535,no,0.779686,1.043108,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.610209,68.865008,no,yes,1.910176,2.938135,no,no,2.943097,no,0.997202,0.207922,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.612556,69.463458,no,yes,2,1.259628,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,38.692265,1.548178,62.341438,yes,yes,2.956671,2.965494,Sometimes,no,2.868132,no,0,0.54925,Sometimes,Automobile,Overweight_Level_I
+Female,38.952866,1.568441,62.855073,yes,yes,2.002796,3,Sometimes,no,2.526775,no,0.271174,0.806069,Sometimes,Automobile,Overweight_Level_I
+Female,36.631456,1.532322,62.417228,yes,yes,2.288604,2.845307,Sometimes,no,2.638164,no,0,0.990741,Sometimes,Automobile,Overweight_Level_I
+Female,21.996118,1.730199,78.997062,yes,yes,2.277077,1,Sometimes,no,2,no,1.061743,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.951309,1.730167,78.429312,yes,yes,2.138334,1,Sometimes,no,2,no,2.269058,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.018228,1.627396,70,no,yes,2,1,no,no,2.976177,no,0.780117,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,29.934465,1.638744,69.242354,yes,yes,2.029634,3,Sometimes,no,1.827515,no,0.480206,0,Sometimes,Automobile,Overweight_Level_I
+Female,31.166572,1.680858,71.81338,yes,yes,2.048216,3,Sometimes,no,1.305816,no,0.170452,0.276806,Sometimes,Automobile,Overweight_Level_I
+Female,20.534606,1.758372,79.469513,yes,yes,2.8557,1,Sometimes,no,2.663861,no,2.762711,0.783336,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.97166,1.822573,85.084364,yes,yes,2.995599,3.095663,Sometimes,no,2.568157,no,2.7243,0.330457,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.627,1.815409,85.302146,yes,yes,2.987148,3.483449,Sometimes,no,2.359246,no,2.060415,0.598999,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.929387,1.698049,75,yes,yes,2,3,Sometimes,no,2.763573,no,1.502711,0.127879,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.009437,1.60681,67.773914,yes,yes,2,3.156309,no,no,3,no,1.179592,0.086868,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.538225,1.610327,67.999778,yes,yes,1.887951,3.728377,no,no,2.682107,no,1.123931,0.492528,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.828435,1.710415,75.142858,yes,yes,2.786008,3,Sometimes,no,2.526193,no,0.925118,2,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.814657,1.716289,75.688433,yes,yes,2,3,Sometimes,no,2,no,0.092344,1.466496,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.576682,1.834715,89.429199,yes,yes,2.342323,3.60885,Sometimes,no,2.878866,no,2,0.208323,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.814653,1.611822,67.140367,yes,yes,1.874935,3.370362,Sometimes,no,2.558773,no,1.206121,0.271024,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.979769,1.6,66.126964,yes,yes,2.213135,3,Sometimes,no,2.046766,no,2.172546,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21,1.753578,77.97917,yes,yes,2.273548,2.39007,Sometimes,no,1.648404,no,0.874643,1.102696,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.0285,1.742364,78.664618,yes,yes,2.780699,1.101404,Sometimes,no,1.579207,no,1.153775,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,18.721041,1.775626,80.113493,yes,yes,1.687569,2.756405,Sometimes,no,2.079131,no,2.931527,0.68173,no,Public_Transportation,Overweight_Level_I
+Male,21,1.714253,75,yes,yes,2,3,Sometimes,no,1.081597,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.693628,75,yes,yes,2,3,Sometimes,no,1.833055,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,29.463168,1.659172,71.170001,yes,yes,2,3,Sometimes,no,1.281683,no,1.609801,0,Sometimes,Automobile,Overweight_Level_I
+Male,24.284861,1.776347,82.329047,yes,yes,1.989905,3.054899,Sometimes,no,2.080968,no,1.107543,0.939726,no,Public_Transportation,Overweight_Level_I
+Male,26.288417,1.767806,82.694689,yes,yes,1.947405,3.118013,Sometimes,no,2.136159,no,0.783963,0.88868,no,Public_Transportation,Overweight_Level_I
+Male,23.455324,1.702516,75,yes,yes,2.162519,3,Sometimes,no,2.152666,no,1.078074,1.502839,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.637203,1.814182,85.301029,yes,yes,2.923916,3.335876,Sometimes,no,2.372444,no,1.305633,0.311436,Sometimes,Public_Transportation,Overweight_Level_I
+Female,22.239836,1.59994,65.423942,no,yes,2.99448,3,Sometimes,no,1.706568,no,0.784216,0.150969,Sometimes,Public_Transportation,Overweight_Level_I
+Female,31.62638,1.666023,72.906186,yes,yes,2,3,Sometimes,no,1,no,0,1.301385,Sometimes,Automobile,Overweight_Level_I
+Male,23,1.701584,75.093569,yes,yes,3,3.205009,Sometimes,no,2.643468,no,0.998039,1.898139,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.874658,1.713343,75.828119,yes,yes,2.507841,3.648194,Sometimes,no,1.279756,no,0.132629,1.82753,Sometimes,Public_Transportation,Overweight_Level_I
+Female,17.420269,1.489409,53.620604,no,yes,1.836554,1.865238,Sometimes,no,2,yes,0.320209,1.969507,Sometimes,Public_Transportation,Overweight_Level_I
+Female,17.807828,1.518067,55.822119,no,yes,1.773265,2.118153,Sometimes,no,1.075239,yes,0.085388,1.915191,Sometimes,Public_Transportation,Overweight_Level_I
+Female,16.240576,1.616533,65.062945,yes,yes,2.388168,1,Sometimes,no,1.438018,yes,0.110887,1.165817,no,Public_Transportation,Overweight_Level_I
+Female,23.252457,1.589616,65.127324,no,yes,2.286146,2.961113,Sometimes,no,2.657303,no,1.91108,1.618227,Sometimes,Public_Transportation,Overweight_Level_I
+Female,34.772902,1.675612,73.501233,yes,yes,3,2.400943,Sometimes,no,1.509734,no,0.167125,0,Sometimes,Automobile,Overweight_Level_I
+Male,22.766227,1.874061,89.754302,yes,yes,2,2.870005,Sometimes,no,1.15031,no,0.417724,1,Sometimes,Automobile,Overweight_Level_I
+Female,39.214514,1.580765,62.631382,yes,yes,2.487167,3,Sometimes,no,2.396977,no,0.577063,0.435072,Sometimes,Automobile,Overweight_Level_I
+Male,19.858973,1.62,69.575315,no,yes,2.185938,1,no,no,2.81353,no,1,1.110222,Sometimes,Public_Transportation,Overweight_Level_I
+Female,16.370009,1.613921,66.738769,yes,yes,2.206399,1,Sometimes,no,2,yes,0.94893,1.772463,no,Public_Transportation,Overweight_Level_I
+Female,17.992717,1.618683,67.193585,yes,yes,1.952987,1,Sometimes,no,1.334856,yes,0.732276,1.890214,no,Public_Transportation,Overweight_Level_I
+Male,19.179932,1.637537,70,yes,yes,2,1.030416,Sometimes,no,2.118716,no,0.529259,0,no,Public_Transportation,Overweight_Level_I
+Female,19.621545,1.566524,61.616,no,yes,2.908757,2.696051,Sometimes,no,1.622827,yes,0.539952,0.001096,Sometimes,Public_Transportation,Overweight_Level_I
+Female,19.755797,1.542328,63.856193,no,yes,2.628791,2.762883,Sometimes,no,1.512035,yes,0.303722,0.003229,Sometimes,Public_Transportation,Overweight_Level_I
+Female,17.099015,1.6,65,yes,yes,3,2.401341,Sometimes,no,2,yes,0.663896,1.753745,Sometimes,Public_Transportation,Overweight_Level_I
+Female,17.25813,1.600184,65,yes,yes,2.749629,2.298612,Sometimes,no,2,yes,0.547515,1.619473,Sometimes,Public_Transportation,Overweight_Level_I
+Male,29.153907,1.773656,87.070234,no,yes,1.595746,3.618722,Sometimes,no,1.274389,no,1.504003,0.370067,no,Automobile,Overweight_Level_I
+Female,32.278869,1.64602,74.147443,yes,yes,2.885178,2.562895,Sometimes,no,1.017006,no,0.588673,0.916291,Sometimes,Automobile,Overweight_Level_I
+Female,31.793937,1.65015,73.810728,yes,yes,2.372494,2.849848,Sometimes,no,1.028538,no,0.675983,0.303025,Sometimes,Automobile,Overweight_Level_I
+Male,18.014333,1.751029,80,yes,yes,2,2.805436,Sometimes,no,2.122884,no,0.045651,0.017225,Frequently,Public_Transportation,Overweight_Level_I
+Male,19.685,1.838266,89.496905,yes,yes,2.153639,3.715148,Sometimes,no,2.703337,no,2,0.226097,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.506389,1.824449,87.656029,yes,yes,2.793561,3.788602,Sometimes,no,2.429059,no,2.094542,0.393358,Sometimes,Public_Transportation,Overweight_Level_I
+Female,19,1.76,79.349221,yes,yes,2,1.893811,Sometimes,no,3,no,1.376229,1.801322,no,Public_Transportation,Overweight_Level_I
+Female,18,1.622241,68.250249,yes,yes,2,1.010319,Sometimes,no,1.319437,no,0,1.626456,no,Public_Transportation,Overweight_Level_I
+Female,18.003153,1.729996,75.816864,yes,yes,2.992329,3,Sometimes,no,2.004908,no,1,1.405086,Frequently,Public_Transportation,Overweight_Level_I
+Female,18.052394,1.725233,75.970712,yes,yes,2.927409,3,Sometimes,no,2.209991,no,1,0.797215,Frequently,Public_Transportation,Overweight_Level_I
+Female,19.774317,1.541039,64.726948,no,yes,2.706134,2.669766,Sometimes,no,1.979848,yes,0.445238,0.908836,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.180346,1.773766,89.28282,yes,yes,2.010684,1.322087,Sometimes,no,2,no,0.540397,0.973834,Sometimes,Public_Transportation,Overweight_Level_II
+Male,21.793724,1.75463,89.068227,yes,yes,2,2.041558,Sometimes,no,2.38725,no,0.813917,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,20.880161,1.674327,80,yes,yes,2,3,Sometimes,no,2,no,1.66639,1.443212,no,Public_Transportation,Overweight_Level_II
+Male,21.18354,1.72,80.555875,yes,yes,2,2.468421,Sometimes,no,2,no,2,1,no,Public_Transportation,Overweight_Level_II
+Male,32.774488,1.913241,101.482054,yes,yes,2,2.110937,Sometimes,no,2.175632,no,1,0,Sometimes,Walking,Overweight_Level_II
+Male,31.327609,1.737984,82.523113,yes,yes,2.300408,3.071028,Sometimes,no,1.738959,no,0.090147,0,Sometimes,Automobile,Overweight_Level_II
+Male,29.956198,1.703688,82.207978,yes,yes,2.119643,3.292956,Sometimes,no,1.723372,no,0,0.987102,Sometimes,Automobile,Overweight_Level_II
+Male,21.403421,1.716308,80,yes,yes,2,3,Sometimes,no,2,no,2.936551,1.40913,no,Public_Transportation,Overweight_Level_II
+Male,22.591439,1.65,80,yes,yes,2,3,Sometimes,no,2,no,0.451078,2,no,Public_Transportation,Overweight_Level_II
+Female,28.583944,1.57856,65.522744,yes,no,2,2.283673,Sometimes,no,1.970622,no,1.863258,1.581703,Sometimes,Public_Transportation,Overweight_Level_II
+Male,38.825189,1.780846,85.687751,yes,yes,2.901924,1.124977,Sometimes,no,2.76382,no,0.8554,0.999183,Sometimes,Automobile,Overweight_Level_II
+Male,35.217173,1.823168,91.630257,yes,yes,2,2.9948,Sometimes,no,1.290979,no,0.87599,0.85405,Sometimes,Automobile,Overweight_Level_II
+Female,20.392665,1.525234,65.220249,yes,no,2.451009,3,Sometimes,no,2,no,2.177243,1.323877,no,Public_Transportation,Overweight_Level_II
+Female,22.154854,1.481682,61.373868,yes,no,2,2.983297,Sometimes,no,1.453626,no,0.32896,0.509396,no,Public_Transportation,Overweight_Level_II
+Male,19.108796,1.768578,87.803311,yes,yes,2.754646,3,Sometimes,no,2.957821,no,2.242754,0.786609,Sometimes,Public_Transportation,Overweight_Level_II
+Female,20.70768,1.569878,69.743323,yes,no,2.417635,3,Sometimes,no,1.937674,no,1.417035,1,no,Public_Transportation,Overweight_Level_II
+Female,20.634694,1.568188,67.904023,yes,no,2.512719,3,Sometimes,no,1.131169,no,0.095389,1,no,Public_Transportation,Overweight_Level_II
+Male,22.052152,1.792527,89.994415,yes,yes,1.771693,1.890213,Sometimes,no,2,no,0,1.36595,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22.869778,1.795311,89.868784,yes,yes,1.57223,1.888067,Sometimes,no,2,no,0,0.552498,Sometimes,Public_Transportation,Overweight_Level_II
+Female,22.909992,1.700038,80,yes,yes,2,3,Sometimes,no,2,no,0.973114,1.540298,no,Public_Transportation,Overweight_Level_II
+Female,23,1.668649,80.458343,yes,yes,2,2.256119,Sometimes,no,1.142873,no,0.807076,1.611271,no,Public_Transportation,Overweight_Level_II
+Male,24.679807,1.7,84.687554,yes,yes,2,3,Sometimes,no,2.020424,no,0,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23.884212,1.713825,83.952968,yes,yes,2,2.6648,Sometimes,no,1.810738,no,0.80989,1,Sometimes,Public_Transportation,Overweight_Level_II
+Female,25.461411,1.7,78.055968,yes,yes,2.661556,3,Sometimes,no,3,no,0.55181,0.658783,Sometimes,Public_Transportation,Overweight_Level_II
+Male,31.333798,1.835381,91.059595,yes,yes,2,3,Sometimes,no,1.18423,no,0.155579,0.453404,Sometimes,Public_Transportation,Overweight_Level_II
+Male,24.108711,1.7,80.761409,yes,yes,2,3,Sometimes,no,2.879402,no,0,0.322405,Sometimes,Public_Transportation,Overweight_Level_II
+Female,28.830558,1.7,78,yes,yes,3,3,Sometimes,no,1.699971,no,1.727114,1,Frequently,Automobile,Overweight_Level_II
+Male,17.570089,1.7,83.199034,yes,no,2.097373,2.547086,Sometimes,no,1.538626,no,1,0.761898,no,Public_Transportation,Overweight_Level_II
+Female,19.027574,1.659877,77.272652,yes,yes,2.061461,2.812283,Sometimes,no,3,no,0.362441,0.78519,Sometimes,Public_Transportation,Overweight_Level_II
+Male,25.632447,1.836943,96.192662,yes,yes,1.317729,2.278652,Sometimes,no,2,no,1.80674,1.93977,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22.94313,1.819614,93.086419,yes,yes,1.882235,1.240046,Sometimes,no,2,no,0,1.541493,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23.285553,1.717775,85.312639,yes,yes,2.951591,3,Sometimes,no,2.971557,no,0,0.947192,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23,1.791867,90,yes,yes,2.067817,3,Sometimes,no,1.082084,no,0,1.922364,Sometimes,Public_Transportation,Overweight_Level_II
+Female,25.19291,1.7,80.749657,yes,yes,2.54527,3,Sometimes,no,2.269564,no,0.503122,1,Frequently,Public_Transportation,Overweight_Level_II
+Male,34.389906,1.68108,83.568035,yes,yes,2,1.660768,Sometimes,no,2.482294,no,0.891205,0,no,Automobile,Overweight_Level_II
+Female,26.595893,1.660756,78.574786,yes,yes,2,2.597608,Sometimes,no,2,no,0,0.434198,Frequently,Public_Transportation,Overweight_Level_II
+Male,55.24625,1.769269,80.491339,no,yes,2,3,Sometimes,no,2,no,1,0,no,Automobile,Overweight_Level_II
+Male,34.543563,1.765188,85,yes,yes,2.694281,3,Sometimes,no,2.653831,no,1.96582,0.891189,no,Automobile,Overweight_Level_II
+Male,21.808159,1.65,80,yes,yes,2,3,Sometimes,no,2,no,0.826609,2,no,Public_Transportation,Overweight_Level_II
+Male,18.118277,1.654757,80,yes,yes,2.821977,2.044035,Sometimes,no,1.954968,no,1,0.854536,no,Public_Transportation,Overweight_Level_II
+Female,42.189023,1.647768,79.165306,yes,yes,2,3,Sometimes,no,1,no,0,1.48189,no,Automobile,Overweight_Level_II
+Male,22,1.691303,80.539,yes,yes,2,2.038373,Sometimes,no,2,no,2.70825,1.506576,Sometimes,Public_Transportation,Overweight_Level_II
+Female,28.770852,1.532897,65.031879,yes,no,2,1,Sometimes,no,1,no,0.262171,0,no,Public_Transportation,Overweight_Level_II
+Female,25.483381,1.565288,64.848627,yes,no,2,1,Sometimes,no,1,no,0.740633,0,no,Public_Transportation,Overweight_Level_II
+Female,21.67315,1.5,63.65233,yes,no,2,1.474836,Sometimes,no,2,no,0.274838,0.613902,no,Public_Transportation,Overweight_Level_II
+Female,23.469538,1.507106,64.814109,yes,no,2.252472,3.986652,Sometimes,no,2,no,0.934286,0.890626,no,Public_Transportation,Overweight_Level_II
+Male,23,1.70074,81.32297,yes,yes,2,2.720642,Sometimes,no,1.5197,no,0.279375,1.043435,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23,1.715118,81.650778,yes,yes,2,1.976744,Sometimes,no,1.628997,no,0.880584,1.127675,Sometimes,Public_Transportation,Overweight_Level_II
+Female,38.464538,1.696423,78.967919,yes,yes,3,3,Sometimes,no,2.981604,no,0,0,no,Automobile,Overweight_Level_II
+Female,37.496175,1.653088,80,yes,yes,2.033745,3,Sometimes,no,1.517225,no,0,1.517897,no,Automobile,Overweight_Level_II
+Female,33.690239,1.681842,77.426465,yes,yes,3,2.679724,Sometimes,no,1.999737,no,1.747347,0.681773,no,Automobile,Overweight_Level_II
+Female,34.369686,1.652202,77.13322,yes,yes,2.595128,1.619796,Sometimes,no,1.642251,no,0.662831,0.999268,no,Automobile,Overweight_Level_II
+Male,31.426573,1.848683,99,yes,yes,2.759286,2.59257,Sometimes,no,2.549617,no,1.427233,0.519395,Sometimes,Automobile,Overweight_Level_II
+Male,34.288249,1.835678,96.01851,yes,yes,2,1.546665,Sometimes,no,3,no,1,0,Sometimes,Automobile,Overweight_Level_II
+Female,18.863875,1.560029,71.728069,yes,no,3,3.339914,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Overweight_Level_II
+Female,18.951144,1.621048,72.105856,yes,no,3,3.884861,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19.671876,1.699474,78,yes,no,1.925064,2.358298,Sometimes,no,2.774043,no,0,0.133566,no,Public_Transportation,Overweight_Level_II
+Male,50.832559,1.745528,82.130728,yes,yes,2,3,Sometimes,no,1.774778,no,0.943266,0,no,Automobile,Overweight_Level_II
+Male,17.971574,1.720379,85,yes,yes,2,3,Sometimes,no,2.802498,no,1,0.41758,Sometimes,Public_Transportation,Overweight_Level_II
+Male,18,1.758787,85.050558,yes,yes,2.846981,3,Sometimes,no,2.87781,no,1,0.429081,Sometimes,Public_Transportation,Overweight_Level_II
+Male,18.701766,1.704908,81.384224,yes,yes,2.650629,1,Sometimes,no,1.708083,no,1.876051,0.938791,Sometimes,Public_Transportation,Overweight_Level_II
+Female,34.389679,1.691322,77.561602,yes,yes,3,1.802305,Sometimes,no,2,no,0.390877,0,no,Automobile,Overweight_Level_II
+Male,17.178483,1.78629,95.277392,yes,yes,2,3,Sometimes,no,3,no,1.072318,0.651904,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33.0816,1.705617,83.016968,yes,yes,2,2.7976,Sometimes,no,2.991671,no,2.148738,0,no,Automobile,Overweight_Level_II
+Male,33.270448,1.733439,84.75383,yes,yes,2.631565,2.627173,Sometimes,no,2.253422,no,2.690756,0.845774,no,Automobile,Overweight_Level_II
+Female,36.310292,1.701397,83,yes,yes,2,1,Sometimes,no,2,no,1.472172,0,no,Automobile,Overweight_Level_II
+Female,20,1.625236,74.433362,yes,no,2.522399,1.032887,Sometimes,no,2.397653,no,2.358699,0.440087,Sometimes,Public_Transportation,Overweight_Level_II
+Female,18.549437,1.545196,72.467862,yes,no,3,3.014808,Sometimes,no,2,no,1.997529,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,34.243146,1.843172,92.69551,yes,yes,2,2.271734,Sometimes,no,2.629043,no,1,0.075823,Sometimes,Automobile,Overweight_Level_II
+Male,23.32012,1.705813,82.011962,yes,yes,2.784471,2.122545,Sometimes,no,2.984153,no,2.164472,0.203978,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19.703095,1.704141,78.790936,yes,no,1.650505,3,Sometimes,no,2,no,1.73234,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19.223259,1.706082,78.073528,yes,no,1.961347,3,Sometimes,no,2,no,1.655488,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,29.695603,1.842943,93.798055,yes,yes,2.133955,2.902766,Sometimes,no,1.753464,no,1.729987,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,27,1.880992,99.623778,yes,yes,2.684528,1,Sometimes,no,1.05668,no,1.516147,0.084006,Sometimes,Public_Transportation,Overweight_Level_II
+Male,21.027662,1.726774,82.853749,yes,yes,2.265973,2.687502,Sometimes,no,2,no,2.21722,0.648522,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33.015258,1.73196,84.315608,yes,yes,2,3,Sometimes,no,2.205911,no,1.293396,0,no,Automobile,Overweight_Level_II
+Male,18,1.751278,86.963628,yes,no,3,3,Sometimes,no,2,no,1.271667,0.155278,Sometimes,Public_Transportation,Overweight_Level_II
+Male,30.553097,1.780448,88.431954,yes,yes,2,1.178708,Sometimes,no,1.791286,no,0.893362,0.276364,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33,1.85,97.92035,yes,yes,2,3,Sometimes,no,1.117464,no,1,0.663649,Sometimes,Public_Transportation,Overweight_Level_II
+Male,24.911994,1.785241,88.03748,yes,yes,1.306844,2.279546,Sometimes,no,2,no,0,0.739609,Sometimes,Public_Transportation,Overweight_Level_II
+Male,24.444846,1.718845,86.319887,yes,yes,2,2.677693,Sometimes,no,2.693978,no,0,0.688013,Sometimes,Public_Transportation,Overweight_Level_II
+Female,19.911246,1.530248,68.85097,yes,no,2.258795,3.53009,Sometimes,no,1.172186,no,0.678943,1,no,Public_Transportation,Overweight_Level_II
+Female,34.204408,1.664927,80.386078,yes,yes,2,3,Sometimes,no,2.641642,no,0.285889,1.50301,no,Automobile,Overweight_Level_II
+Female,34.281681,1.673333,77.205685,yes,yes,2.689929,1.835543,Sometimes,no,1.718569,no,0.674348,0.707246,no,Automobile,Overweight_Level_II
+Male,23.384374,1.725587,82.480214,yes,yes,2.712747,2.853676,Sometimes,no,1.526313,no,1,0.173665,Sometimes,Public_Transportation,Overweight_Level_II
+Female,43.238402,1.733875,86.94538,yes,yes,2.353603,2.337035,Sometimes,no,1.830614,no,0.706287,0,no,Automobile,Overweight_Level_II
+Female,45,1.675953,79.66832,yes,yes,2.598051,3,Sometimes,no,1,no,0,0,no,Automobile,Overweight_Level_II
+Male,22.087389,1.786238,89.802492,yes,yes,1.718156,1.631184,Sometimes,no,2,no,0,0.306124,Sometimes,Public_Transportation,Overweight_Level_II
+Male,21.378039,1.718534,80,yes,yes,2,3,Sometimes,no,2,no,2.721646,1.269982,no,Public_Transportation,Overweight_Level_II
+Male,31.398281,1.919543,101.544589,yes,yes,2,1.005391,Sometimes,no,2.001936,no,1,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,31.484494,1.707613,82.586893,yes,yes,2.795086,3.250467,Sometimes,no,1.367876,no,0.022958,0,no,Automobile,Overweight_Level_II
+Male,21.709159,1.658393,80,yes,yes,2,3,Sometimes,no,2,no,2.939733,1.692287,no,Public_Transportation,Overweight_Level_II
+Female,30.711381,1.536819,65.912688,yes,no,2.030256,2.948721,Sometimes,no,1.984323,no,0.986414,0.333673,no,Public_Transportation,Overweight_Level_II
+Male,34.821442,1.805459,90.13868,yes,yes,2,1.80993,Sometimes,no,2.174835,no,0.340915,0.020153,Sometimes,Automobile,Overweight_Level_II
+Female,20.843363,1.521008,67.083121,yes,no,2.493448,3,Sometimes,no,1.849997,no,0.954459,1.444532,no,Public_Transportation,Overweight_Level_II
+Male,19.443639,1.744733,87.27989,yes,yes,2.442536,3,Sometimes,no,2.825629,no,3,0,Sometimes,Public_Transportation,Overweight_Level_II
+Female,20.492077,1.529223,65.140408,yes,no,2.003951,3,Sometimes,no,1.207978,no,1.916751,1.228995,no,Public_Transportation,Overweight_Level_II
+Male,22.944349,1.799742,89.98168,yes,yes,1.34138,1.94671,Sometimes,no,2,no,0,0.975504,Sometimes,Public_Transportation,Overweight_Level_II
+Female,23,1.681314,80.037202,yes,yes,2,2.9796,Sometimes,no,1.964435,no,0.189957,1.73216,no,Public_Transportation,Overweight_Level_II
+Male,24.06894,1.706912,85.743727,yes,yes,2,2.994198,Sometimes,no,2.747302,no,0,0.799756,Sometimes,Public_Transportation,Overweight_Level_II
+Female,23.728707,1.663509,80,yes,yes,2,3,Sometimes,no,2.13755,no,0,0.124977,no,Public_Transportation,Overweight_Level_II
+Male,29.216019,1.752265,88.096062,yes,yes,2,1.894384,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,24.751511,1.735343,83.337721,yes,yes,2.607335,3,Sometimes,no,2,no,0.451009,0.630866,Sometimes,Public_Transportation,Overweight_Level_II
+Female,28.977792,1.7,78,yes,yes,3,3,Sometimes,no,1.507638,no,1.82434,1,Frequently,Automobile,Overweight_Level_II
+Male,17.441593,1.7,83.414072,yes,no,2.061384,2.579291,Sometimes,no,1.909253,no,1,0.962468,Sometimes,Public_Transportation,Overweight_Level_II
+Female,19.126145,1.633794,77.858532,yes,no,2.696381,3,Sometimes,no,2.472964,no,1.856119,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,24.122589,1.856759,95.887056,yes,yes,1.116068,2.449067,Sometimes,no,2,no,0.92635,1.97117,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23,1.77433,90,yes,yes,2.116432,3,Sometimes,no,1.002292,no,0,1.066708,Sometimes,Public_Transportation,Overweight_Level_II
+Female,28.824194,1.7,78,yes,yes,3,3,Sometimes,no,2.147074,no,1.416076,1,Frequently,Automobile,Overweight_Level_II
+Male,34.204611,1.719509,84.416515,yes,yes,2.031246,3,Sometimes,no,2.803002,no,1.98448,0.418623,no,Automobile,Overweight_Level_II
+Female,28.167799,1.658202,78,yes,yes,2.938031,1.532833,Sometimes,no,1.931242,no,0.525002,0.371941,Frequently,Automobile,Overweight_Level_II
+Male,55.137881,1.657221,80.993213,yes,yes,2,3,Sometimes,no,2,no,1,0,no,Automobile,Overweight_Level_II
+Male,34.970367,1.713348,84.722222,yes,yes,2.884212,3,Sometimes,no,3,no,2,0.8324,no,Automobile,Overweight_Level_II
+Male,20.986834,1.677178,80.379575,yes,yes,2,2.961706,Sometimes,no,2,no,1.661556,1.114716,no,Public_Transportation,Overweight_Level_II
+Female,38.378056,1.67805,77.224574,yes,yes,2.444599,3,Sometimes,no,1.029798,no,0,0,no,Automobile,Overweight_Level_II
+Male,22.18881,1.717722,81.92991,yes,yes,2,1.152521,Sometimes,no,1.723159,no,1.39016,1.094941,Sometimes,Public_Transportation,Overweight_Level_II
+Female,21.082384,1.486484,60.117993,yes,no,2,1.104642,Sometimes,no,1,no,0,0.969097,no,Public_Transportation,Overweight_Level_II
+Female,25.293202,1.503379,64.342459,yes,no,2,1,Sometimes,no,1.639807,no,0.072117,0,no,Public_Transportation,Overweight_Level_II
+Male,23,1.718981,81.66995,yes,yes,2,1.729553,Sometimes,no,1.400247,no,0.887923,1.011983,Sometimes,Public_Transportation,Overweight_Level_II
+Female,39.170029,1.688354,79.278896,yes,yes,3,3,Sometimes,no,2.994515,no,0,0,no,Automobile,Overweight_Level_II
+Female,34.044229,1.665807,77.098973,yes,yes,2.976975,1.346987,Sometimes,no,1.868349,no,0.702538,0.879333,no,Automobile,Overweight_Level_II
+Male,33.182127,1.838441,97.029249,yes,yes,2,1.977221,Sometimes,no,3,no,1,0,Sometimes,Automobile,Overweight_Level_II
+Female,19.821996,1.653431,75.090439,yes,no,2.766036,2.443812,Sometimes,no,2.707927,no,0.702839,0.827439,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19.149706,1.699818,78,yes,no,1.096455,2.491315,Sometimes,no,2.450069,no,0,0.726118,Sometimes,Public_Transportation,Overweight_Level_II
+Male,46.491859,1.718097,88.600878,yes,yes,2.129969,3,Sometimes,no,1.568035,no,0.870127,0,no,Automobile,Overweight_Level_II
+Male,17.894784,1.731389,84.064875,yes,no,2.019674,2.843319,Sometimes,no,2.832004,no,1,0.608607,Sometimes,Public_Transportation,Overweight_Level_II
+Male,18.88485,1.699667,79.67793,yes,no,2.735297,2.157164,Sometimes,no,1.087166,no,0.110174,0.003695,no,Public_Transportation,Overweight_Level_II
+Female,38.384177,1.698626,78.637307,yes,yes,3,3,Sometimes,no,2.503198,no,0,0,no,Automobile,Overweight_Level_II
+Male,22.675679,1.823765,96.945262,yes,yes,1.588782,2.601675,Sometimes,no,2.469469,no,1.736538,1.886855,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33.049121,1.708742,83.001642,yes,yes,2,1.171027,Sometimes,no,2.405172,no,2.300282,0,no,Automobile,Overweight_Level_II
+Female,37.205173,1.667469,80.993373,yes,yes,2.01054,2.77684,Sometimes,no,1.651548,no,0,0.790967,no,Automobile,Overweight_Level_II
+Female,19.027417,1.588147,73.939268,yes,no,3,3.362758,Sometimes,no,2,no,2.892922,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,37.492444,1.835024,92.368359,yes,yes,2,1.672706,Sometimes,no,2.766674,no,1,0.027093,Sometimes,Automobile,Overweight_Level_II
+Male,24.657598,1.7088,83.520113,yes,yes,2.689577,2.714115,Sometimes,no,2.279214,no,0.970661,0.828549,Sometimes,Public_Transportation,Overweight_Level_II
+Male,18.011718,1.680991,79.752916,yes,yes,2.413156,2.521546,Sometimes,no,1.985312,no,0.00705,0.965464,Sometimes,Public_Transportation,Overweight_Level_II
+Male,27.349745,1.835271,97.58826,yes,yes,2.923433,1.338033,Sometimes,no,1.944095,no,1.931829,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,18,1.742819,86.565148,yes,yes,3,3,Sometimes,no,2,no,2.040816,0.860321,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33.009285,1.741192,84.773349,yes,yes,2,3,Sometimes,no,2.035954,no,1.210736,0,no,Automobile,Overweight_Level_II
+Male,18,1.759721,86.0805,yes,yes,2.882522,3,Sometimes,no,2.452986,no,1,0.746651,Sometimes,Public_Transportation,Overweight_Level_II
+Male,30.079371,1.810616,92.860254,yes,yes,2,3,Sometimes,no,1.622638,no,0.033745,0.105895,Sometimes,Public_Transportation,Overweight_Level_II
+Male,25.035915,1.763101,88.532032,yes,yes,1.973499,1.081805,Sometimes,no,2,no,0,0.464022,Sometimes,Public_Transportation,Overweight_Level_II
+Female,18.198322,1.543338,71.799982,yes,no,3,3.087119,Sometimes,no,2,no,1.403872,1,no,Public_Transportation,Overweight_Level_II
+Female,35.456326,1.651812,79.437921,yes,yes,2.156065,2.909117,Sometimes,no,1.221281,no,0.503279,1.796136,no,Automobile,Overweight_Level_II
+Male,23.806789,1.732492,84.557797,yes,yes,2.992205,3,Sometimes,no,2.413813,no,0.458237,0.688293,Sometimes,Public_Transportation,Overweight_Level_II
+Female,39.392569,1.706349,85.720788,yes,yes,2.944287,1.466393,Sometimes,no,2.442775,no,0.675262,0.779334,no,Automobile,Overweight_Level_II
+Male,21.384259,1.780679,89.667406,yes,yes,1.780746,1.471053,Sometimes,no,2,no,0.467562,0.568668,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22.730414,1.758687,89.673648,yes,yes,2.164062,2.983201,Sometimes,no,2.168904,no,0.752926,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,21.20563,1.704573,80,yes,yes,2,3,Sometimes,no,2,no,2.847761,1.413375,no,Public_Transportation,Overweight_Level_II
+Male,21.333429,1.716545,80.005809,yes,yes,2,2.783336,Sometimes,no,2,no,2.536615,1.206535,no,Public_Transportation,Overweight_Level_II
+Male,32.787101,1.903832,99.812443,yes,yes,2,1.863012,Sometimes,no,2.196471,no,1,0,Sometimes,Automobile,Overweight_Level_II
+Male,32.610018,1.742538,83.390983,yes,yes,2.247704,3.053598,Sometimes,no,1.919629,no,0.408023,0,no,Automobile,Overweight_Level_II
+Male,30.022598,1.747739,83.314157,yes,yes,2.011656,3.165837,Sometimes,no,1.844645,no,0.889963,0.395979,no,Automobile,Overweight_Level_II
+Male,21.123048,1.717037,80,yes,yes,2,3,Sometimes,no,2,no,2.270555,1.343044,no,Public_Transportation,Overweight_Level_II
+Female,22.989846,1.65,80,yes,yes,2,3,Sometimes,no,2,no,0.146919,2,no,Public_Transportation,Overweight_Level_II
+Female,24.320154,1.577921,65.293179,yes,no,2.03414,2.741413,Sometimes,no,1.996384,no,1.276552,1.019452,no,Public_Transportation,Overweight_Level_II
+Male,37.275298,1.746055,83.282232,yes,yes,2.746408,1.058123,Sometimes,no,2.265727,no,0.885633,0.673408,no,Automobile,Overweight_Level_II
+Male,34.098174,1.841151,91.798103,yes,yes,2,2.997414,Sometimes,no,1.259454,no,0.935217,0.9976,Sometimes,Automobile,Overweight_Level_II
+Female,21.001282,1.517998,64.696248,yes,no,2.1239,1.672958,Sometimes,no,2,no,1.582428,1.313363,no,Public_Transportation,Overweight_Level_II
+Female,21.95994,1.483284,62.894283,yes,no,2,1.680838,Sometimes,no,1.833635,no,0.281876,0.575848,no,Public_Transportation,Overweight_Level_II
+Male,19.795269,1.758972,87.861431,yes,yes,2.332074,3,Sometimes,no,2.999495,no,2.405963,0.441502,Sometimes,Public_Transportation,Overweight_Level_II
+Female,19.090023,1.562434,70.442775,yes,no,2.748243,3.070386,Sometimes,no,1.962322,no,1.145752,1,no,Public_Transportation,Overweight_Level_II
+Female,20.820455,1.547066,67.473282,yes,no,2.303656,3,Sometimes,no,1.163111,no,1.926381,1.119877,no,Public_Transportation,Overweight_Level_II
+Male,22.087056,1.792435,89.998729,yes,yes,1.904732,2.608416,Sometimes,no,1.204175,no,0,1.895312,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22.185756,1.784555,89.836692,yes,yes,1.979944,1.599464,Sometimes,no,2,no,0.17048,0.819475,Sometimes,Public_Transportation,Overweight_Level_II
+Female,22.980957,1.700216,80.473587,yes,yes,2,2.815255,Sometimes,no,1.622214,no,0.463891,1.462664,no,Public_Transportation,Overweight_Level_II
+Male,23,1.672101,80.939733,yes,yes,2,2.395785,Sometimes,no,1.40077,no,0.788659,1.544357,no,Public_Transportation,Overweight_Level_II
+Male,24.190896,1.7,84.849349,yes,yes,2,3,Sometimes,no,2.056053,no,0,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23.94003,1.721348,83.986714,yes,yes,2.407817,2.844138,Sometimes,no,1.983649,no,0.868721,0.089354,Sometimes,Public_Transportation,Overweight_Level_II
+Female,26.591628,1.7,78.008388,yes,yes,2.734314,3,Sometimes,no,3,no,0.897702,0.908271,Frequently,Public_Transportation,Overweight_Level_II
+Male,31.264628,1.803129,91.052215,yes,yes,2,3,Sometimes,no,1.199517,no,0.047738,0.163329,Sometimes,Public_Transportation,Overweight_Level_II
+Female,23.629159,1.70002,80.420434,yes,yes,2,3,Sometimes,no,2.386904,no,0.926201,1.34403,Sometimes,Public_Transportation,Overweight_Level_II
+Female,26.208134,1.7,78.041338,yes,yes,2.784383,3,Sometimes,no,2.165605,no,0.855973,0.839659,Frequently,Automobile,Overweight_Level_II
+Male,17.689057,1.713599,83.285753,yes,yes,2.009952,2.716106,Sometimes,no,1.660614,no,1,0.60473,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19.16097,1.662728,77.473204,yes,no,2.008656,1.402771,Sometimes,no,3,no,0.112383,0.32803,Sometimes,Public_Transportation,Overweight_Level_II
+Male,25.45763,1.837399,95.952027,yes,yes,1.122127,2.865657,Sometimes,no,2,no,1.28775,1.944177,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22.988004,1.80827,91.363293,yes,yes,1.963965,1.836226,Sometimes,no,1.85537,no,0,1.667745,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23.603191,1.714508,85.137113,yes,yes,2.319776,2.884848,Sometimes,no,2.154898,no,0.325534,0.954216,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22.882558,1.793451,89.909259,yes,yes,1.899116,2.375026,Sometimes,no,1.39854,no,0,1.365793,Sometimes,Public_Transportation,Overweight_Level_II
+Female,23.586058,1.700499,81.108599,yes,yes,2.042762,2.9774,Sometimes,no,1.522426,no,0.501417,1.029135,Sometimes,Public_Transportation,Overweight_Level_II
+Male,34.432669,1.694997,84.451423,yes,yes,2.129668,2.693646,Sometimes,no,2.60669,no,1.170823,0.290898,no,Automobile,Overweight_Level_II
+Female,25.472995,1.688911,78.434492,yes,yes,2.182401,2.832018,Sometimes,no,2.456939,no,0.038809,0.524015,Frequently,Public_Transportation,Overweight_Level_II
+Male,55.022494,1.673394,80.400306,yes,yes,2,3,Sometimes,no,2,no,1,0,no,Automobile,Overweight_Level_II
+Male,34.647036,1.769499,85,yes,yes,2.802696,3,Sometimes,no,2.978465,no,1.974656,0.936925,no,Automobile,Overweight_Level_II
+Male,21.997335,1.65,80,yes,yes,2,3,Sometimes,no,2,no,0.527341,2,no,Public_Transportation,Overweight_Level_II
+Male,18.181821,1.662669,79.863546,yes,no,2.492758,2.270163,Sometimes,no,1.992586,no,1.452467,0.864583,no,Public_Transportation,Overweight_Level_II
+Female,41.743333,1.67861,79.849252,yes,yes,2.733129,3,Sometimes,no,1.302594,no,0,1.103349,no,Automobile,Overweight_Level_II
+Male,21.473078,1.694423,80.311273,yes,yes,2,2.646717,Sometimes,no,2,no,2.627515,1.358812,no,Public_Transportation,Overweight_Level_II
+Female,27.757187,1.505437,63.892071,yes,no,2,1,Sometimes,no,1.278578,no,0.02112,0,no,Public_Transportation,Overweight_Level_II
+Female,25.113537,1.505387,63.715219,yes,no,2,1.193729,Sometimes,no,1.846441,no,0.546402,0.235711,no,Public_Transportation,Overweight_Level_II
+Female,21.82165,1.491441,62.269912,yes,no,2,1.477581,Sometimes,no,1.995035,no,0.324676,0.51743,no,Public_Transportation,Overweight_Level_II
+Female,20.670975,1.509408,64.852953,yes,no,2.294067,3.209508,Sometimes,no,2,no,1.103088,1.261043,no,Public_Transportation,Overweight_Level_II
+Male,22.649792,1.685045,81.022119,yes,yes,2,2.756622,Sometimes,no,1.606076,no,0.432973,1.749586,no,Public_Transportation,Overweight_Level_II
+Male,23.154311,1.723072,82.338464,yes,yes,2.315932,2.658478,Sometimes,no,1.568476,no,0.949976,0.544899,Sometimes,Public_Transportation,Overweight_Level_II
+Female,38.097395,1.696954,78.156035,yes,yes,3,3,Sometimes,no,2.166024,no,0,0,no,Automobile,Overweight_Level_II
+Female,37.642177,1.676095,79.079738,yes,yes,2.802128,3,Sometimes,no,1.744628,no,0,0.721167,no,Automobile,Overweight_Level_II
+Female,34.176795,1.681021,77.392179,yes,yes,2.79606,1.971472,Sometimes,no,1.921601,no,0.935217,0.704637,no,Automobile,Overweight_Level_II
+Female,34.231083,1.654067,79.697278,yes,yes,2.086898,1.818026,Sometimes,no,2.088929,no,0.305422,1.040787,no,Automobile,Overweight_Level_II
+Male,31.965402,1.840708,98.259423,yes,yes,2.333503,1.820779,Sometimes,no,2.559265,no,1.327193,0.481357,Sometimes,Automobile,Overweight_Level_II
+Male,33.185661,1.836007,97.416417,yes,yes,2,1.724887,Sometimes,no,3,no,1,0,Sometimes,Automobile,Overweight_Level_II
+Female,18.741188,1.556789,71.788181,yes,no,3,3.129155,Sometimes,no,2,no,1.634134,1,no,Public_Transportation,Overweight_Level_II
+Female,19.955257,1.5891,72.713611,yes,no,3,3.856434,Sometimes,no,2,no,1.32417,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19.684891,1.700627,78.048207,yes,no,1.653081,2.753418,Sometimes,no,2.192063,no,1.474937,0.518542,Sometimes,Public_Transportation,Overweight_Level_II
+Male,47.7061,1.743935,84.729197,yes,yes,2.535315,3,Sometimes,no,1.146595,no,0.31381,0,no,Automobile,Overweight_Level_II
+Male,17.997009,1.742654,85,yes,no,2,3,Sometimes,no,2.976229,no,1,0.121992,Sometimes,Public_Transportation,Overweight_Level_II
+Male,17.971786,1.754441,85.002884,yes,yes,2.765769,3,Sometimes,no,2.845134,no,1,0.42683,Sometimes,Public_Transportation,Overweight_Level_II
+Male,20.432717,1.719342,80.790813,yes,yes,2.215464,2.116195,Sometimes,no,1.80996,no,1.910577,0.971746,no,Public_Transportation,Overweight_Level_II
+Female,33.954433,1.682253,77.431678,yes,yes,3,2.049908,Sometimes,no,1.999882,no,1.637368,0.10724,no,Automobile,Overweight_Level_II
+Male,17.039058,1.799902,95.419668,yes,yes,2,3,Sometimes,no,3,no,1.04729,0.933802,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33.070142,1.709428,83.014033,yes,yes,2,1.044628,Sometimes,no,2.01051,no,2.805801,0,no,Automobile,Overweight_Level_II
+Male,34.993835,1.752456,84.783756,yes,yes,2.63165,2.98212,Sometimes,no,2.778587,no,2.667739,0.905181,no,Automobile,Overweight_Level_II
+Male,35.719457,1.685947,83.3258,yes,yes,2,1.009426,Sometimes,no,2.136398,no,1.060349,0,no,Automobile,Overweight_Level_II
+Female,19.005725,1.599999,73.513873,yes,no,2.942154,2.667711,Sometimes,no,2.184768,no,1.524405,0.805008,Sometimes,Public_Transportation,Overweight_Level_II
+Female,18.850466,1.550053,72.9518,yes,no,3,3.000974,Sometimes,no,2,no,2.274248,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,31.540751,1.828092,91.495718,yes,yes,2,2.698883,Sometimes,no,2.035104,no,0.598438,0.017016,Sometimes,Automobile,Overweight_Level_II
+Male,23.335391,1.71338,82.085549,yes,yes,2.716909,2.598079,Sometimes,no,2.90545,no,1.600431,0.176892,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19.749627,1.680764,79.621463,yes,no,1.83746,3,Sometimes,no,2,no,1.194519,1.212762,no,Public_Transportation,Overweight_Level_II
+Male,19.565496,1.705584,78.025625,yes,no,1.936479,2.837388,Sometimes,no,2.159987,no,1.475772,0.646514,Sometimes,Public_Transportation,Overweight_Level_II
+Male,30.357745,1.841852,93.614246,yes,yes,2.045027,2.946063,Sometimes,no,1.307563,no,1.442485,0.934493,Sometimes,Public_Transportation,Overweight_Level_II
+Male,27,1.834986,99.083478,yes,yes,2.760607,1,Sometimes,no,1.336378,no,1.898889,0.487375,Sometimes,Public_Transportation,Overweight_Level_II
+Male,21.380336,1.724839,82.27635,yes,yes,2.09449,1.873484,Sometimes,no,2,no,2.079709,0.880414,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33.151905,1.685127,83.986895,yes,yes,2,2.473911,Sometimes,no,2.452789,no,0.932792,0,no,Automobile,Overweight_Level_II
+Male,18,1.750097,86.372141,yes,yes,2.907062,3,Sometimes,no,2.740848,no,1.219827,0.037634,Sometimes,Public_Transportation,Overweight_Level_II
+Male,32.241587,1.757961,87.906019,yes,yes,2,1.79558,Sometimes,no,1.855054,no,1.083572,0.218574,Sometimes,Public_Transportation,Overweight_Level_II
+Male,31.662814,1.848965,98.912261,yes,yes,2.399531,2.623079,Sometimes,no,2.535127,no,1.030752,0.606264,Sometimes,Automobile,Overweight_Level_II
+Male,24.347414,1.789193,89.393589,yes,yes,1.521604,2.174968,Sometimes,no,2,no,0,0.632467,Sometimes,Public_Transportation,Overweight_Level_II
+Male,24.362124,1.716677,85.261339,yes,yes,2,2.676148,Sometimes,no,2.083939,no,0.632164,0.993786,Sometimes,Public_Transportation,Overweight_Level_II
+Female,19.462713,1.550122,69.936073,yes,no,2.501683,3.394788,Sometimes,no,1.337378,no,0.932888,1,no,Public_Transportation,Overweight_Level_II
+Female,35.432059,1.663178,80.135167,yes,yes,2,3,Sometimes,no,1.992548,no,0.039207,1.528714,no,Automobile,Overweight_Level_II
+Female,33.864257,1.679299,77.355417,yes,yes,2.76802,2.137068,Sometimes,no,1.873591,no,0.966973,0.691944,no,Automobile,Overweight_Level_II
+Male,23.807181,1.729177,82.52724,yes,yes,2.742796,2.937607,Sometimes,no,1.99467,no,1,0.023564,Sometimes,Public_Transportation,Overweight_Level_II
+Male,39.585811,1.719153,86.464843,yes,yes,2.325623,1.313403,Sometimes,no,1.94609,no,0.604259,0,no,Automobile,Overweight_Level_II
+Female,45.821267,1.687326,80.413997,yes,yes,2.076689,3,Sometimes,no,1.026729,no,0.647798,0,no,Automobile,Overweight_Level_II
+Male,25.929746,1.772449,105,yes,yes,3,3,Sometimes,no,2.045069,no,1.725931,1.619596,Sometimes,Public_Transportation,Obesity_Type_I
+Male,26.042738,1.837117,105.712219,yes,yes,3,3,Sometimes,no,2.509147,no,2,1.564796,Sometimes,Public_Transportation,Obesity_Type_I
+Male,30.551762,1.784377,102.872505,yes,yes,2.271306,3,Sometimes,no,1.771198,no,2,0.413752,Sometimes,Automobile,Obesity_Type_I
+Male,39.759575,1.792507,101.780099,yes,yes,2.33361,2.113575,Sometimes,no,2.504136,no,2.998981,1,Sometimes,Automobile,Obesity_Type_I
+Female,31.386405,1.556579,78.233341,yes,yes,2.13683,2.092179,Sometimes,no,1.505381,no,0,0,Sometimes,Public_Transportation,Obesity_Type_I
+Female,25.706285,1.585547,80.351263,yes,yes,2,1,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Obesity_Type_I
+Female,43.604901,1.569234,81.827288,yes,yes,2.909853,1,Sometimes,no,2,no,1.308852,0,no,Automobile,Obesity_Type_I
+Female,42.31607,1.583943,81.936398,yes,yes,2.490507,2.974204,Sometimes,no,1.846754,no,0,0,no,Automobile,Obesity_Type_I
+Female,22.654316,1.621233,82,yes,yes,1.063449,1,Sometimes,no,2,no,0,1.482016,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.679935,1.6084,82.603984,yes,yes,2.699282,1,Sometimes,no,2.598632,no,0.292093,2,Sometimes,Public_Transportation,Obesity_Type_I
+Male,24.079524,1.733439,97.911865,yes,yes,2,3,Sometimes,no,2.843675,no,1.309304,1.338655,no,Public_Transportation,Obesity_Type_I
+Male,21.196152,1.65,88.472359,yes,yes,2.4277,1.001633,Sometimes,no,3,no,1.172641,1,no,Public_Transportation,Obesity_Type_I
+Female,22.596576,1.650052,87.272552,yes,yes,2.87599,1.2919,Sometimes,no,2.777712,no,0.432813,1,no,Public_Transportation,Obesity_Type_I
+Female,23,1.644161,84.340406,yes,yes,2.177243,3,Sometimes,no,2.715572,no,2.230109,0.070897,no,Public_Transportation,Obesity_Type_I
+Female,37.356288,1.559499,77.26813,yes,yes,2,3,Sometimes,no,1.630528,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,22.754646,1.734237,95,yes,yes,2,3,Sometimes,no,2.287248,no,2.669179,1.237867,no,Public_Transportation,Obesity_Type_I
+Male,23.252906,1.775815,97.813023,yes,yes,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Female,22.025438,1.640426,87.344156,yes,yes,3,1,Sometimes,no,3,no,1.280924,1.77795,no,Public_Transportation,Obesity_Type_I
+Female,18.086772,1.65,82.13682,yes,yes,3,3,Sometimes,no,1,no,1.02975,1.350099,Sometimes,Public_Transportation,Obesity_Type_I
+Male,25.994393,1.753321,107.998815,yes,yes,3,3,Sometimes,no,1.776991,no,1.757724,0,Sometimes,Automobile,Obesity_Type_I
+Female,40.821515,1.553026,77.74518,yes,yes,2,3,Sometimes,no,1.406115,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,23,1.706525,90.500055,yes,yes,2,3,Sometimes,no,1.530493,no,0.967627,1,no,Public_Transportation,Obesity_Type_I
+Female,37.955371,1.560648,80,yes,yes,2.846452,1.139317,Sometimes,no,1.459511,no,1.71807,0,Sometimes,Automobile,Obesity_Type_I
+Male,31.315593,1.673352,90,yes,yes,2.19011,2.029858,Sometimes,no,2.348981,no,1.946907,0,Sometimes,Automobile,Obesity_Type_I
+Male,22.661556,1.660324,94.189167,yes,yes,2,3,Sometimes,no,2,no,0,0.105936,no,Public_Transportation,Obesity_Type_I
+Male,40.317787,1.786318,98.447311,yes,yes,2,3,Sometimes,no,2.680292,no,1.549693,0.522259,Sometimes,Automobile,Obesity_Type_I
+Male,23.365649,1.757691,95.361795,yes,yes,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Male,21.72127,1.712163,98.699971,yes,yes,2,2.977543,Sometimes,no,2.205977,no,2.698874,2,no,Public_Transportation,Obesity_Type_I
+Male,35.389491,1.78,100.84763,yes,yes,2.069267,1.476204,Sometimes,no,3,no,2.05252,0.092736,Frequently,Automobile,Obesity_Type_I
+Female,22.061461,1.6,82.040318,yes,yes,1.362441,2.57038,Sometimes,no,2.166228,no,2.232851,1.281141,no,Public_Transportation,Obesity_Type_I
+Female,23,1.61082,82.532994,yes,yes,2.09663,2.879541,Sometimes,no,2.432967,no,1.887012,0,no,Public_Transportation,Obesity_Type_I
+Female,23,1.665199,83.15115,yes,yes,2.928234,1.458507,Sometimes,no,2.777379,no,0.354541,1.707018,no,Public_Transportation,Obesity_Type_I
+Female,40.951591,1.542122,80,yes,yes,2,1.105617,Sometimes,no,1.372811,no,1.629432,0,Sometimes,Automobile,Obesity_Type_I
+Female,39.135634,1.507867,79.58958,yes,yes,2,3,Sometimes,no,1,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,26.271621,1.660955,90,yes,yes,2,3,Sometimes,no,3,no,0.669278,0.505266,Sometimes,Automobile,Obesity_Type_I
+Female,23.779235,1.553127,78,yes,yes,2,1,Sometimes,no,2,no,0.827502,0,no,Public_Transportation,Obesity_Type_I
+Male,20.586978,1.781952,102.910613,yes,yes,2,3,Sometimes,no,1.689793,no,1.464204,0,no,Public_Transportation,Obesity_Type_I
+Male,21.669478,1.75,96.940255,yes,yes,2,3,Sometimes,no,2.771469,no,2.359339,1.206251,no,Public_Transportation,Obesity_Type_I
+Female,23,1.615854,80.615325,yes,yes,2,1,Sometimes,no,2,no,0.026142,0,no,Public_Transportation,Obesity_Type_I
+Male,20.825962,1.793378,105.655037,yes,yes,2,3,Sometimes,no,1.155384,no,0.003938,0.526999,Sometimes,Public_Transportation,Obesity_Type_I
+Male,18.178023,1.85478,114.77484,yes,yes,2,1.854536,Sometimes,no,2.160169,no,1,2,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18,1.685633,90.032671,yes,yes,2.496455,3,Sometimes,no,1.850344,no,1.066101,0.732265,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18.267696,1.706343,93.348843,yes,yes,1.70825,3,Sometimes,no,1,no,0.899864,1,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23,1.791415,105.138073,yes,yes,2.262171,3,Sometimes,no,2.03415,no,0.317363,0.508663,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23,1.753716,106.49141,yes,yes,2.740633,3,Sometimes,no,2.081005,no,0.650235,0.76906,Sometimes,Public_Transportation,Obesity_Type_I
+Male,20.9859,1.780503,103.189532,yes,yes,2,3,Sometimes,no,1.608128,no,0.478134,0,no,Public_Transportation,Obesity_Type_I
+Male,21.504943,1.819867,106.038468,yes,yes,2,3,Sometimes,no,2.715252,no,0,0.621605,no,Public_Transportation,Obesity_Type_I
+Male,18,1.791174,108.9606,yes,yes,2,1.08687,Sometimes,no,2.279124,no,1,1.609773,no,Public_Transportation,Obesity_Type_I
+Male,18,1.82093,108.742005,yes,yes,2,1.25535,Sometimes,no,2.497065,no,1,1.441605,no,Public_Transportation,Obesity_Type_I
+Female,19.442663,1.627812,82,yes,yes,1.081585,2.870661,Sometimes,no,1.11756,no,0,1.616826,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.865018,1.632118,82,yes,yes,2.587789,1.482103,Sometimes,no,1.911664,no,0,0.128681,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.948577,1.773594,105.000789,yes,yes,2.252653,3,Sometimes,no,2.962371,no,1,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.214617,1.930416,118.560509,yes,yes,2,3,Sometimes,no,3,no,0.732186,1,Sometimes,Public_Transportation,Obesity_Type_I
+Male,22.277859,1.947406,116.893105,yes,yes,2,3,Sometimes,no,3,no,0.975187,1,Sometimes,Public_Transportation,Obesity_Type_I
+Female,37.832949,1.610867,80,yes,yes,2.036613,1.81698,Sometimes,no,1.93059,no,1.851404,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.631769,1.513202,75.410647,yes,yes,2,2.582591,Sometimes,no,1.535134,no,1.88452,0,Sometimes,Automobile,Obesity_Type_I
+Male,17.6739,1.738465,97.185474,yes,yes,2,3,Sometimes,no,2,no,0,0.233314,no,Public_Transportation,Obesity_Type_I
+Female,37.524551,1.567915,76.129784,yes,yes,2,3,Sometimes,no,2.102976,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,43.510672,1.587546,76.126112,yes,yes,2,3,Sometimes,no,2.091934,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,18,1.820385,108.395005,yes,yes,2,2.164839,Sometimes,no,2.094479,no,1,0.676557,Sometimes,Public_Transportation,Obesity_Type_I
+Male,18,1.792239,108.244379,yes,yes,2,2.141839,Sometimes,no,2.939847,no,1,0.103056,Sometimes,Public_Transportation,Obesity_Type_I
+Male,29.506287,1.82697,108.751502,yes,yes,2,2.877583,Sometimes,no,2.358038,no,0.877295,1.817146,Sometimes,Automobile,Obesity_Type_I
+Female,18,1.670058,86.242679,yes,yes,2.609123,3,Sometimes,no,1.955053,no,1.186013,0,no,Public_Transportation,Obesity_Type_I
+Male,24.733308,1.639383,91.267087,yes,yes,1.036159,3,Sometimes,no,1,no,0.344013,0.329367,Sometimes,Automobile,Obesity_Type_I
+Male,29,1.682916,89.991671,yes,yes,1.851262,3,Sometimes,no,2.11267,no,0.388269,0,Sometimes,Automobile,Obesity_Type_I
+Female,18,1.609321,84.493156,yes,yes,2,3,Sometimes,no,1.051735,no,1,0,no,Public_Transportation,Obesity_Type_I
+Male,21.098035,1.690437,96.366777,yes,yes,2,2.95833,Sometimes,no,2,no,0,1.595306,no,Public_Transportation,Obesity_Type_I
+Male,19.089595,1.700164,99.204695,yes,yes,2,2.119826,Sometimes,no,2,no,0,1.882539,no,Public_Transportation,Obesity_Type_I
+Female,18.312665,1.60074,82.249831,yes,yes,2.501236,3,Sometimes,no,1.220331,no,0.245003,0.537202,no,Public_Transportation,Obesity_Type_I
+Male,18.260079,1.801848,104.741914,yes,yes,2,3,Sometimes,no,3,no,1.783858,0,no,Public_Transportation,Obesity_Type_I
+Male,26.004294,1.844751,105.025808,yes,yes,3,3,Sometimes,no,2.925029,no,2,1.758865,Sometimes,Public_Transportation,Obesity_Type_I
+Male,25.994746,1.811602,106.042142,yes,yes,3,3,Sometimes,no,2.858171,no,1.813318,0.680215,Sometimes,Public_Transportation,Obesity_Type_I
+Male,36.726617,1.787521,100.624553,yes,yes,2.031185,2.992606,Sometimes,no,2.852254,no,2.00123,0.36037,Frequently,Automobile,Obesity_Type_I
+Male,38.297259,1.789109,100.066268,yes,yes,2.014194,2.050121,Sometimes,no,2.184707,no,2.000561,0.853891,Frequently,Automobile,Obesity_Type_I
+Female,30.163408,1.533364,78.030383,yes,yes,2.028225,1.923607,Sometimes,no,1.798917,no,0,0,no,Public_Transportation,Obesity_Type_I
+Female,28.770039,1.580858,79.713492,yes,yes,2,1,Sometimes,no,2,no,0.005405,0,no,Public_Transportation,Obesity_Type_I
+Female,42.337283,1.64639,86.639861,yes,yes,2.81646,2.27374,Sometimes,no,1.722063,no,0,0,no,Automobile,Obesity_Type_I
+Female,47.283374,1.643786,81.978743,yes,yes,2.037585,1.418985,Sometimes,no,1.827351,no,0,0,no,Automobile,Obesity_Type_I
+Female,22.518787,1.634342,82.414477,yes,yes,1.853314,1.320768,Sometimes,no,2.135552,no,0.248034,1.727828,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.836315,1.604893,82,yes,yes,1.00876,1,Sometimes,no,2,no,0,0.585912,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23,1.654961,94.790579,yes,yes,2,3,Sometimes,no,1.490613,no,0.654316,1,no,Public_Transportation,Obesity_Type_I
+Male,22.975526,1.701986,95,yes,yes,2,3,Sometimes,no,2.02127,no,1.814869,1.066603,no,Public_Transportation,Obesity_Type_I
+Male,22.720449,1.65,89.139209,yes,yes,2.103335,2.964024,Sometimes,no,3,no,0.632947,1,no,Public_Transportation,Obesity_Type_I
+Male,23.887569,1.657995,90,yes,yes,2,3,Sometimes,no,3,no,0.603638,0.969085,no,Public_Transportation,Obesity_Type_I
+Female,23,1.634688,82.628,yes,yes,2.060922,2.933409,Sometimes,no,2.235282,no,0.380633,0.0026,no,Public_Transportation,Obesity_Type_I
+Female,23,1.628168,84.49798,yes,yes,2.058687,2.962004,Sometimes,no,2.010596,no,0.851059,0.630866,no,Public_Transportation,Obesity_Type_I
+Female,38.148845,1.557808,79.661693,yes,yes,2,3,Sometimes,no,1.274774,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.96543,1.560199,80,yes,yes,2.533605,1.271624,Sometimes,no,1.893691,no,1.296535,0,Sometimes,Automobile,Obesity_Type_I
+Male,22.771612,1.750384,95.324282,yes,yes,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Male,22.582371,1.753081,95.269089,yes,yes,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Female,21.008051,1.65,88.126544,yes,yes,2.457547,1.00061,Sometimes,no,3,no,1.361533,1,no,Public_Transportation,Obesity_Type_I
+Female,22.591026,1.650012,87.676154,yes,yes,2.983851,1.068443,Sometimes,no,2.854161,no,1.103209,1,no,Public_Transportation,Obesity_Type_I
+Female,16.129279,1.65,85.583485,yes,yes,2.954996,3,Sometimes,no,1,no,2.091862,1.06053,no,Public_Transportation,Obesity_Type_I
+Female,16.913841,1.634832,85.803809,yes,yes,2.061969,3,Sometimes,no,1.229171,no,2.392047,1.256119,no,Public_Transportation,Obesity_Type_I
+Male,29.43879,1.770126,108.602715,yes,yes,3,3,Sometimes,no,1.163264,no,1.866023,1.604608,Sometimes,Automobile,Obesity_Type_I
+Male,29.881301,1.758189,108.721893,yes,yes,3,3,Sometimes,no,1.271178,no,1.944728,0,Sometimes,Automobile,Obesity_Type_I
+Female,43.591999,1.595165,77.354744,yes,yes,2,3,Sometimes,no,2.117346,no,0,0,no,Automobile,Obesity_Type_I
+Female,40.993179,1.567756,79.493626,yes,yes,2,3,Sometimes,no,2.952506,no,0,0,no,Automobile,Obesity_Type_I
+Male,22.936098,1.702825,93.638318,yes,yes,2,3,Sometimes,no,1.746197,no,0.455216,0.335158,no,Public_Transportation,Obesity_Type_I
+Male,23,1.735659,93.429205,yes,yes,2,3,Sometimes,no,1.249307,no,0.973465,1,no,Public_Transportation,Obesity_Type_I
+Female,37.597953,1.62901,80,yes,yes,2.450784,2.952821,Sometimes,no,1.335192,no,0.180276,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.532066,1.615385,80,yes,yes,2.972426,3,Sometimes,no,1.636326,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,31.630054,1.671705,90,yes,yes,2.467002,1.851088,Sometimes,no,2.104054,no,1.991565,0,Sometimes,Automobile,Obesity_Type_I
+Male,31.641081,1.676595,89.993812,yes,yes,2.934671,2.119682,Sometimes,no,2.041462,no,0.578074,0,Sometimes,Automobile,Obesity_Type_I
+Male,21.872484,1.699998,95.564428,yes,yes,2,2.970675,Sometimes,no,2,no,0,0.169294,no,Public_Transportation,Obesity_Type_I
+Male,21.834894,1.722785,94.094184,yes,yes,2,2.966803,Sometimes,no,2,no,0,1.281541,no,Public_Transportation,Obesity_Type_I
+Male,36.023972,1.670667,90.575934,yes,yes,2.903545,1.508685,Sometimes,no,2.450069,no,1.45473,0,Sometimes,Automobile,Obesity_Type_I
+Male,39.656559,1.789992,98.021766,yes,yes,2.043359,2.209314,Sometimes,no,2.785804,no,1.259613,0.669616,Sometimes,Automobile,Obesity_Type_I
+Male,24.184891,1.768834,97.449743,yes,yes,2,3,Sometimes,no,2.973729,no,2.491642,1.36595,no,Public_Transportation,Obesity_Type_I
+Male,23.237302,1.761008,97.829344,yes,yes,2,3,Sometimes,no,2.988771,no,2.429923,1.978043,no,Public_Transportation,Obesity_Type_I
+Male,35.322112,1.78,102.265955,yes,yes,2.787589,1.114564,Sometimes,no,3,no,2.710338,0.572185,Sometimes,Automobile,Obesity_Type_I
+Male,31.387982,1.810215,105.254354,yes,yes,2.39728,2.036794,Sometimes,no,2.659419,no,1.062011,1.771135,Sometimes,Automobile,Obesity_Type_I
+Female,23,1.608469,82.954796,yes,yes,2.150054,2.988539,Sometimes,no,2.768325,no,2.08515,0,no,Public_Transportation,Obesity_Type_I
+Female,23,1.630357,83.1011,yes,yes,2.977585,1.630506,Sometimes,no,2.839319,no,1.091474,0.889517,no,Public_Transportation,Obesity_Type_I
+Female,22.679454,1.62826,82.967937,yes,yes,2.274491,1,Sometimes,no,2.2695,no,0.946461,1.73107,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.480889,1.605662,82.470375,yes,yes,1.557287,1,Sometimes,no,2.371015,no,0.288032,2,Sometimes,Public_Transportation,Obesity_Type_I
+Female,41.403862,1.567973,81.056851,yes,yes,2.152264,2.977999,Sometimes,no,1.513199,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,40.702771,1.548403,80,yes,yes,2,3,Sometimes,no,1.326165,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,29.389239,1.681855,90,yes,yes,2.08841,2.644692,Sometimes,no,2.773236,no,1.252472,0,Sometimes,Automobile,Obesity_Type_I
+Male,30.967417,1.688436,90,yes,yes,2.180047,2.733077,Sometimes,no,2.77962,no,1.454129,0,Sometimes,Automobile,Obesity_Type_I
+Female,23.474165,1.577115,78,yes,yes,2,1,Sometimes,no,2,no,0.876101,0,no,Public_Transportation,Obesity_Type_I
+Female,24.317607,1.586751,79.109029,yes,yes,2,1,Sometimes,no,2,no,0.852446,0,no,Public_Transportation,Obesity_Type_I
+Male,17.052914,1.710616,99.860254,yes,yes,2,3,Sometimes,no,2,no,0.067491,0.894105,no,Public_Transportation,Obesity_Type_I
+Male,18.04892,1.721384,99.430612,yes,yes,2,3,Sometimes,no,2,no,1.579431,0.839862,no,Public_Transportation,Obesity_Type_I
+Female,22.875223,1.624367,82,yes,yes,1.826885,1,Sometimes,no,2,no,0,0.459274,no,Public_Transportation,Obesity_Type_I
+Female,22.884722,1.622787,82,yes,yes,1.754401,1,Sometimes,no,2,no,0,0.301909,no,Public_Transportation,Obesity_Type_I
+Male,18.729566,1.855433,112.875283,yes,yes,2,2.427137,Sometimes,no,2.574073,no,1,2,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.011124,1.856315,118.183797,yes,yes,2,3,Sometimes,no,3,no,0.788585,1.220029,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18.603496,1.681719,90.671871,yes,yes,1.524428,3,Sometimes,no,1.383831,no,0.130417,1,Sometimes,Public_Transportation,Obesity_Type_I
+Female,21.106056,1.722884,92.949254,yes,yes,1.164062,3,Sometimes,no,1.157395,no,0,0.320851,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23,1.799406,106.528811,yes,yes,2.123159,3,Sometimes,no,2.415343,no,0.295178,0.569852,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.980847,1.819875,106.048516,yes,yes,2,3,Sometimes,no,2.745242,no,0,0.80898,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.016665,1.790172,105.042194,yes,yes,2.259679,3,Sometimes,no,1.026143,no,0.268801,0.077818,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.72738,1.783782,105.000276,yes,yes,2.044326,3,Sometimes,no,2.35818,no,0.349371,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,18,1.783906,109.207614,yes,yes,2,1.867836,Sometimes,no,2.438398,no,1,0.802305,no,Public_Transportation,Obesity_Type_I
+Male,18,1.844218,109.195529,yes,yes,2,1.548407,Sometimes,no,2.191401,no,1,1.676944,no,Public_Transportation,Obesity_Type_I
+Female,19.431662,1.631662,82,yes,yes,2.843456,2.937989,Sometimes,no,1.001995,no,0,1.554404,no,Public_Transportation,Obesity_Type_I
+Female,18.166318,1.649553,82.323954,yes,yes,2.864776,3,Sometimes,no,1.876915,no,0.631565,0.186414,no,Public_Transportation,Obesity_Type_I
+Male,22.352025,1.754711,105.000706,yes,yes,2.871137,3,Sometimes,no,2.627569,no,1,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,22.469351,1.762515,105.000097,yes,yes,2.282803,3,Sometimes,no,2.104264,no,1,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,19.524698,1.942725,121.657979,yes,yes,2,3,Sometimes,no,3,no,1,1.014808,Sometimes,Public_Transportation,Obesity_Type_I
+Male,20.491475,1.975663,120.702935,yes,yes,2,3,Sometimes,no,3,no,0.767013,1,Sometimes,Public_Transportation,Obesity_Type_I
+Female,39.213399,1.586301,80,yes,yes,2.020502,1.237454,Sometimes,no,1.93142,no,1.967973,0,Sometimes,Automobile,Obesity_Type_I
+Female,38.098745,1.598448,80,yes,yes,2.020785,1.169173,Sometimes,no,1.972551,no,1.903338,0,Sometimes,Automobile,Obesity_Type_I
+Male,19.034033,1.703098,98.296651,yes,yes,2,2.391753,Sometimes,no,2,no,0,1.119877,no,Public_Transportation,Obesity_Type_I
+Male,17.073648,1.706996,99.544696,yes,yes,2,3,Sometimes,no,2,no,1.73209,0.048617,no,Public_Transportation,Obesity_Type_I
+Female,38.547267,1.526472,75.150345,yes,yes,2,3,Sometimes,no,1.084442,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,38.683794,1.501993,76.642944,yes,yes,2,3,Sometimes,no,1,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,18,1.784402,108.413119,yes,yes,2,2.475444,Sometimes,no,2.655795,no,1,0.169879,no,Public_Transportation,Obesity_Type_I
+Male,18,1.792687,108.204547,yes,yes,2,2.478794,Sometimes,no,2.967064,no,1,0.4346,no,Public_Transportation,Obesity_Type_I
+Male,29.622804,1.814052,109.411622,yes,yes,2.407817,2.070033,Sometimes,no,1.796257,no,1.234483,0.178708,Sometimes,Automobile,Obesity_Type_I
+Male,30.79626,1.789421,109.599453,yes,yes,2.21498,2.282392,Sometimes,no,1.179942,no,1.771754,0.537659,Sometimes,Automobile,Obesity_Type_I
+Female,18,1.686904,90.004046,yes,yes,2.737149,3,Sometimes,no,1.997195,no,1.352663,0.468483,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18.107092,1.703259,91.499683,yes,yes,1.899793,3,Sometimes,no,1.439962,no,0.9529,0.838847,Sometimes,Public_Transportation,Obesity_Type_I
+Male,31.335093,1.665798,89.738596,yes,yes,2.274164,1.049534,Sometimes,no,1.358172,no,1.482411,0,Sometimes,Automobile,Obesity_Type_I
+Male,29,1.66671,89.048151,yes,yes,1.897796,3,Sometimes,no,1.193039,no,0.011519,0,Sometimes,Automobile,Obesity_Type_I
+Female,18.011903,1.65,84.210535,yes,yes,2.85916,3,Sometimes,no,1,no,1.020525,0.786066,no,Public_Transportation,Obesity_Type_I
+Female,18,1.649439,84.897738,yes,yes,2,3,Sometimes,no,1.039313,no,1,0,no,Public_Transportation,Obesity_Type_I
+Male,21.077356,1.702002,97.971598,yes,yes,2,1.66338,Sometimes,no,2,no,0,1.865851,no,Public_Transportation,Obesity_Type_I
+Male,21.052016,1.69382,99.530971,yes,yes,2,1.672751,Sometimes,no,2,no,0,1.946173,no,Public_Transportation,Obesity_Type_I
+Female,18.078256,1.622999,82.403076,yes,yes,2.340405,3,Sometimes,no,1.655245,no,1.006884,0.548539,no,Public_Transportation,Obesity_Type_I
+Female,18.063582,1.633675,82.459577,yes,yes,2.921576,3,Sometimes,no,1.003636,no,1.000227,0.905596,no,Public_Transportation,Obesity_Type_I
+Male,20.418832,1.836669,105.257543,yes,yes,2,3,Sometimes,no,2.793505,no,2.21939,0.202902,no,Public_Transportation,Obesity_Type_I
+Male,20.580984,1.798354,103.705418,yes,yes,2,3,Sometimes,no,2.401315,no,2.891986,0,no,Public_Transportation,Obesity_Type_I
+Male,26.032416,1.824432,105.131956,yes,yes,3,3,Sometimes,no,2.110611,no,1.890907,1.58483,Sometimes,Public_Transportation,Obesity_Type_I
+Male,25.955361,1.830384,105.479313,yes,yes,3,3,Sometimes,no,2.480277,no,1.796779,1.596562,Sometimes,Public_Transportation,Obesity_Type_I
+Male,26.015448,1.829907,105.436173,yes,yes,3,3,Sometimes,no,2.224914,no,1.781589,1.59405,Sometimes,Public_Transportation,Obesity_Type_I
+Male,25.920738,1.823755,105.800158,yes,yes,2.92711,3,Sometimes,no,2.151496,no,1.166064,1.509831,Sometimes,Public_Transportation,Obesity_Type_I
+Male,29.633715,1.834842,105.19936,yes,yes,2.805533,3,Sometimes,no,1.882847,no,2,0.70778,Sometimes,Automobile,Obesity_Type_I
+Male,32.895637,1.783901,103.771371,yes,yes,2.902469,1.734762,Sometimes,no,2.600238,no,2.113992,0.584755,Sometimes,Automobile,Obesity_Type_I
+Male,38.748307,1.782067,103.586342,yes,yes,2.845961,1.92822,Sometimes,no,2.642273,no,2.999918,1,Frequently,Automobile,Obesity_Type_I
+Male,39.685846,1.781032,96.303855,yes,yes,2.252698,2.475228,Sometimes,no,2.923856,no,2.165429,0.616045,Frequently,Automobile,Obesity_Type_I
+Female,33.226808,1.557943,77.647716,yes,yes,2.02091,2.093831,Sometimes,no,1.506518,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,26.220065,1.560258,78.435904,yes,yes,2.108163,1.231915,Sometimes,no,1.808127,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,23.090215,1.596586,80.726195,yes,yes,1.518966,1,Sometimes,no,2,no,0,1.094046,no,Public_Transportation,Obesity_Type_I
+Female,24.565628,1.62093,81.718232,yes,yes,2.260543,1.374791,Sometimes,no,1.947935,no,0,0.078607,no,Public_Transportation,Obesity_Type_I
+Female,42.586285,1.571417,81.918809,yes,yes,2.522183,1.326982,Sometimes,no,1.872673,no,1.048507,0,no,Automobile,Obesity_Type_I
+Female,43.719395,1.584322,80.986496,yes,yes,2.186322,2.900915,Sometimes,no,2.471033,no,0.256113,0,no,Automobile,Obesity_Type_I
+Female,43.37634,1.582523,81.919454,yes,yes,2.76632,1.317884,Sometimes,no,1.889341,no,0.990642,0,no,Automobile,Obesity_Type_I
+Female,39.648946,1.572791,80.086524,yes,yes,2.071622,2.977909,Sometimes,no,1.468297,no,0,0,no,Automobile,Obesity_Type_I
+Female,22.693989,1.627908,82,yes,yes,1.918251,1.355752,Sometimes,no,1.998108,no,0,1.382906,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.676243,1.620938,82.283185,yes,yes,1.967061,1,Sometimes,no,2.548651,no,0.249264,1.516731,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.804818,1.613119,82.636162,yes,yes,2.964419,1,Sometimes,no,2.977516,no,0.742113,2,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.857123,1.62686,82.410189,yes,yes,2.640801,1.015488,Sometimes,no,2.022355,no,0.244749,0.289218,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23.096353,1.728183,97.959899,yes,yes,2,2.986172,Sometimes,no,2.364208,no,2.492402,1.632506,no,Public_Transportation,Obesity_Type_I
+Male,22.815416,1.732694,98.44113,yes,yes,2,2.993623,Sometimes,no,2.326635,no,2.236586,1.529423,no,Public_Transportation,Obesity_Type_I
+Female,21.02064,1.65,88.129437,yes,yes,2.870152,1.001383,Sometimes,no,3,no,1.322558,1,no,Public_Transportation,Obesity_Type_I
+Female,21.001458,1.65,88.026943,yes,yes,2.482575,1.001542,Sometimes,no,3,no,1.751656,1,no,Public_Transportation,Obesity_Type_I
+Female,22.87795,1.66913,86.002736,yes,yes,2.290095,2.590283,Sometimes,no,2.506296,no,0.312254,1,no,Public_Transportation,Obesity_Type_I
+Female,22.847618,1.669136,85.568385,yes,yes,2.074843,1.773916,Sometimes,no,2.428271,no,0.035091,1,no,Public_Transportation,Obesity_Type_I
+Female,23,1.611058,84.191125,yes,yes,2.14128,2.989112,Sometimes,no,2.519841,no,2.11137,0.013483,no,Public_Transportation,Obesity_Type_I
+Female,23,1.649736,84.134712,yes,yes,2.334474,1.496776,Sometimes,no,2.776724,no,0.782416,0.167728,no,Public_Transportation,Obesity_Type_I
+Female,39.129291,1.532643,77.550345,yes,yes,2,3,Sometimes,no,1.186996,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.441044,1.524293,76.202761,yes,yes,2,2.994046,Sometimes,no,1.603549,no,1.335857,0,Sometimes,Automobile,Obesity_Type_I
+Male,22.969366,1.701634,95,yes,yes,2,3,Sometimes,no,2.133469,no,1.030199,1.082838,no,Public_Transportation,Obesity_Type_I
+Male,22.735328,1.723921,94.743892,yes,yes,2,3,Sometimes,no,2.183149,no,1.932386,0.212767,no,Public_Transportation,Obesity_Type_I
+Male,23.32471,1.769484,96.078462,yes,yes,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Male,23.668137,1.777416,97.842406,yes,yes,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Female,21.895468,1.64456,88.119139,yes,yes,2.693859,1.000283,Sometimes,no,3,no,1.201403,1.612432,no,Public_Transportation,Obesity_Type_I
+Female,21.3928,1.641149,88.079177,yes,yes,2.607747,1.000414,Sometimes,no,3,no,1.269169,1.460564,no,Public_Transportation,Obesity_Type_I
+Female,18.178076,1.642892,82.144405,yes,yes,2.66889,3,Sometimes,no,1.172593,no,0.886448,1.252677,no,Public_Transportation,Obesity_Type_I
+Female,18.907514,1.65,82.01831,yes,yes,3,3,Sometimes,no,1,no,0.215187,1.195929,no,Public_Transportation,Obesity_Type_I
+Male,26.011928,1.777251,106.452367,yes,yes,3,3,Sometimes,no,1.911379,no,1.911096,1.032392,Sometimes,Public_Transportation,Obesity_Type_I
+Male,25.696736,1.814696,107.55963,yes,yes,2.921225,3,Sometimes,no,2.869439,no,0.181753,0.133523,Sometimes,Public_Transportation,Obesity_Type_I
+Female,40.466313,1.559005,77.601483,yes,yes,2,3,Sometimes,no,1.572371,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,38.445148,1.556028,77.684229,yes,yes,2,3,Sometimes,no,1.440629,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,24.473062,1.653751,91.204753,yes,yes,1.492834,3,Sometimes,no,1.005367,no,0.922739,0.733221,no,Automobile,Obesity_Type_I
+Male,23.479181,1.680171,91.068054,yes,yes,1.220024,3,Sometimes,no,1.046254,no,0.520989,0.61599,no,Automobile,Obesity_Type_I
+Female,38.397463,1.552648,80,yes,yes,2.667676,1.135278,Sometimes,no,1.399629,no,1.648883,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.974483,1.560215,80,yes,yes,2.569075,2.463113,Sometimes,no,1.567366,no,0.359134,0,Sometimes,Automobile,Obesity_Type_I
+Male,31.783524,1.672959,90,yes,yes,2.949242,1.782109,Sometimes,no,2.210997,no,1.992719,0,Sometimes,Automobile,Obesity_Type_I
+Male,29.681308,1.680058,89.994351,yes,yes,2.104772,2.376374,Sometimes,no,2.218691,no,1.52184,0,Sometimes,Automobile,Obesity_Type_I
+Male,21.587743,1.664752,94.256547,yes,yes,2,2.976098,Sometimes,no,2,no,0,1.39322,no,Public_Transportation,Obesity_Type_I
+Male,21.746113,1.668553,94.454394,yes,yes,2,2.968098,Sometimes,no,2,no,0,1.292476,no,Public_Transportation,Obesity_Type_I
+Male,39.569004,1.785286,100.431625,yes,yes,2.871768,1.792695,Sometimes,no,2.691322,no,2.545707,0.903903,Frequently,Automobile,Obesity_Type_I
+Male,36.67933,1.780725,98.790167,yes,yes,2.540949,1.116401,Sometimes,no,2.929123,no,2.787319,0.732881,Frequently,Automobile,Obesity_Type_I
+Male,22.927011,1.751046,95.285898,yes,yes,2,3,Sometimes,no,2.530301,no,2.89118,1.287117,no,Public_Transportation,Obesity_Type_I
+Male,23.329344,1.751365,95.290429,yes,yes,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Male,22.126325,1.717262,98.579156,yes,yes,2,2.984523,Sometimes,no,2.263551,no,2.819661,2,no,Public_Transportation,Obesity_Type_I
+Male,21.679158,1.741393,98.182498,yes,yes,2,2.978103,Sometimes,no,2.668261,no,2.462269,1.402414,no,Public_Transportation,Obesity_Type_I
+Male,36.673882,1.7921,101.285765,yes,yes,2.222282,1.578751,Sometimes,no,2.791604,no,2.352091,0.317846,Sometimes,Automobile,Obesity_Type_I
+Male,37.936044,1.785957,99.199287,yes,yes,2.04516,1.874532,Sometimes,no,2.856521,no,2.005286,0.105408,Sometimes,Automobile,Obesity_Type_I
+Female,22.226815,1.609068,83.785312,yes,yes,2.094184,2.735706,Sometimes,no,2.257922,no,2.230547,0.195339,no,Public_Transportation,Obesity_Type_I
+Female,22.538932,1.603963,82.012637,yes,yes,1.123672,1.014916,Sometimes,no,2.042078,no,1.846666,1.426103,no,Public_Transportation,Obesity_Type_I
+Female,22.307413,1.605495,82.528575,yes,yes,2.049112,2.622055,Sometimes,no,2.280555,no,2.052896,0.896185,no,Public_Transportation,Obesity_Type_I
+Female,22.362877,1.607182,82.368441,yes,yes,1.880534,2.806566,Sometimes,no,2.313245,no,2.146778,0.196084,no,Public_Transportation,Obesity_Type_I
+Female,22.89974,1.661715,82.595793,yes,yes,1.203754,1.355354,Sometimes,no,2.765593,no,0.128342,1.659476,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.997168,1.655742,82.46376,yes,yes,2.73691,1.478334,Sometimes,no,2.677409,no,0.065264,0.40699,Sometimes,Public_Transportation,Obesity_Type_I
+Female,38.895069,1.549257,80,yes,yes,2.736628,1.130751,Sometimes,no,1.385175,no,1.666965,0,Sometimes,Automobile,Obesity_Type_I
+Female,40.789529,1.549748,80,yes,yes,2,1.099151,Sometimes,no,1.687611,no,1.874662,0,Sometimes,Automobile,Obesity_Type_I
+Female,40.654155,1.52906,79.760922,yes,yes,2,3,Sometimes,no,1,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,38.542937,1.517248,79.843221,yes,yes,2,3,Sometimes,no,1.095134,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,25.95383,1.65731,90,yes,yes,2,3,Sometimes,no,3,no,0.413643,0.920858,Sometimes,Automobile,Obesity_Type_I
+Male,26.826961,1.673287,90,yes,yes,2,3,Sometimes,no,3,no,0.884935,0.12228,Sometimes,Automobile,Obesity_Type_I
+Female,23.803904,1.581527,78.089575,yes,yes,2,1,Sometimes,no,2,no,0.057758,0,no,Public_Transportation,Obesity_Type_I
+Female,23.652435,1.562724,80.535698,yes,yes,2,1,Sometimes,no,2,no,0.389717,0,no,Public_Transportation,Obesity_Type_I
+Male,20.677105,1.781251,102.950929,yes,yes,2,3,Sometimes,no,1.618189,no,0.571648,0,no,Public_Transportation,Obesity_Type_I
+Male,21.797388,1.775584,103.605896,yes,yes,2.121909,3,Sometimes,no,2.500556,no,1.20758,0,no,Public_Transportation,Obesity_Type_I
+Male,22.719385,1.746645,96.875502,yes,yes,2,3,Sometimes,no,2.714091,no,2.64102,1.224737,no,Public_Transportation,Obesity_Type_I
+Male,22.177099,1.741353,96.738014,yes,yes,2,3,Sometimes,no,2.697661,no,2.614909,1.229474,no,Public_Transportation,Obesity_Type_I
+Female,23.099906,1.571812,78.997166,yes,yes,2,1,Sometimes,no,2,no,0.402614,0,no,Public_Transportation,Obesity_Type_I
+Female,24.17851,1.589027,80.553067,yes,yes,2,1,Sometimes,no,2,no,0.011477,0,no,Public_Transportation,Obesity_Type_I
+Male,20.924808,1.803245,105.686091,yes,yes,2,3,Sometimes,no,2.290368,no,0.001086,0.544128,Sometimes,Public_Transportation,Obesity_Type_I
+Male,20.975973,1.792646,105.619143,yes,yes,2,3,Sometimes,no,1.025275,no,0.00203,0.175587,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.856301,1.87199,115.627554,yes,yes,2,2.358455,Sometimes,no,2.515765,no,0.98232,1.157466,Sometimes,Public_Transportation,Obesity_Type_I
+Male,18.140751,1.859056,111.235188,yes,yes,2,1.706551,Sometimes,no,2.039514,no,1,2,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18,1.692242,90.019501,yes,yes,2.642744,3,Sometimes,no,1.904054,no,1.645654,0.363549,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18,1.683,90.924208,yes,yes,2.387426,3,Sometimes,no,1.77962,no,0.743005,0.920162,Sometimes,Public_Transportation,Obesity_Type_I
+Female,20.206358,1.738397,93.890682,yes,yes,1.996638,3,Sometimes,no,1.033199,no,0.584793,0.518297,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18.152876,1.694969,92.508122,yes,yes,2.107854,3,Sometimes,no,1.383894,no,1.030526,0.831412,Sometimes,Public_Transportation,Obesity_Type_I
+Male,22.336216,1.785718,105.055686,yes,yes,2.253707,3,Sometimes,no,2.524432,no,0.58216,0.175694,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23,1.7425,105.028665,yes,yes,2.393837,3,Sometimes,no,2.01499,no,0.978815,0.41322,Sometimes,Public_Transportation,Obesity_Type_I
+Male,22.088059,1.803132,106.329569,yes,yes,2.348745,3,Sometimes,no,2.096751,no,0.544564,0.67688,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23,1.774644,105.966894,yes,yes,2.312825,3,Sometimes,no,2.073497,no,0.614466,0.579541,Sometimes,Public_Transportation,Obesity_Type_I
+Male,20.993067,1.782269,104.97003,yes,yes,2,3,Sometimes,no,1.234943,no,0.008368,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,20.654752,1.780791,102.921218,yes,yes,2,3,Sometimes,no,1.613829,no,1.251665,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.624552,1.790151,106.320686,yes,yes,2.490776,3,Sometimes,no,2.654517,no,0.112454,0.756339,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.682636,1.818641,105.496975,yes,yes,2.078082,3,Sometimes,no,2.895614,no,0.06275,0.261793,Sometimes,Public_Transportation,Obesity_Type_I
+Male,18,1.806827,108.829395,yes,yes,2,1.24884,Sometimes,no,2.416213,no,1,1.546738,no,Public_Transportation,Obesity_Type_I
+Male,18,1.811189,108.800964,yes,yes,2,1.250548,Sometimes,no,2.121176,no,1,0.741069,no,Public_Transportation,Obesity_Type_I
+Male,18,1.811738,108.897324,yes,yes,2,1.202179,Sometimes,no,2.36293,no,1,1.47574,no,Public_Transportation,Obesity_Type_I
+Male,18,1.799779,108.934574,yes,yes,2,1.194815,Sometimes,no,2.434832,no,1,1.541455,no,Public_Transportation,Obesity_Type_I
+Female,19.374207,1.632605,82,yes,yes,2.805512,2.880817,Sometimes,no,1.001307,no,0,1.376597,Sometimes,Public_Transportation,Obesity_Type_I
+Female,19.275687,1.623377,82.851318,yes,yes,1.276858,2.918124,Sometimes,no,1.403784,no,0.552511,1.560402,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.956845,1.618686,81.281578,yes,yes,2.396265,1.073421,Sometimes,no,1.979833,no,0.022598,0.061282,no,Public_Transportation,Obesity_Type_I
+Female,22.829681,1.616085,82.582954,yes,yes,2.663866,1.416309,Sometimes,no,2.379275,no,0.256323,0.598244,no,Public_Transportation,Obesity_Type_I
+Male,22.200779,1.769328,105.000576,yes,yes,2.685484,3,Sometimes,no,2.649459,no,1,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.688557,1.807029,105.696358,yes,yes,2.055209,3,Sometimes,no,2.885852,no,0.903369,0.14451,Sometimes,Public_Transportation,Obesity_Type_I
+Male,20.803186,1.882533,117.468516,yes,yes,2,2.18162,Sometimes,no,2.325091,no,0.891444,1.223661,Sometimes,Public_Transportation,Obesity_Type_I
+Male,19.515324,1.879144,112.932984,yes,yes,2,2.152733,Sometimes,no,2.53369,no,0.917563,1.285838,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.962219,1.931263,118.20313,yes,yes,2,3,Sometimes,no,3,no,0.743593,1,Sometimes,Public_Transportation,Obesity_Type_I
+Male,20.534089,1.861358,115.39792,yes,yes,2,3,Sometimes,no,3,no,0.987591,1.914531,Sometimes,Public_Transportation,Obesity_Type_I
+Female,39.29266,1.567131,80,yes,yes,2.025479,1.046144,Sometimes,no,1.941224,no,1.983265,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.872971,1.565366,80,yes,yes,2.002564,1.974233,Sometimes,no,1.935398,no,1.649299,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.613378,1.516007,77.033049,yes,yes,2,2.880794,Sometimes,no,1.61837,no,0.120165,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.471683,1.549812,76.082517,yes,yes,2,2.765213,Sometimes,no,1.551266,no,1.350001,0,Sometimes,Automobile,Obesity_Type_I
+Male,18.611197,1.714143,96.568814,yes,yes,2,2.967089,Sometimes,no,2,no,0,0.467048,no,Public_Transportation,Obesity_Type_I
+Male,17.412629,1.723191,97.350366,yes,yes,2,3,Sometimes,no,2,no,0,0.931721,no,Public_Transportation,Obesity_Type_I
+Female,39.12631,1.562889,76.65949,yes,yes,2,3,Sometimes,no,1.440526,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.063599,1.502609,75.279605,yes,yes,2,3,Sometimes,no,1.759803,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,41.318302,1.544937,77.053948,yes,yes,2,3,Sometimes,no,2.090413,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,43.726081,1.592316,77.00103,yes,yes,2,3,Sometimes,no,2.806922,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,18,1.787733,108.019211,yes,yes,2,2.37985,Sometimes,no,2.616138,no,1,0.652485,no,Public_Transportation,Obesity_Type_I
+Male,18,1.782722,108.044313,yes,yes,2,2.655265,Sometimes,no,2.297896,no,1,0.552665,no,Public_Transportation,Obesity_Type_I
+Male,18,1.797523,108.316462,yes,yes,2,1.941307,Sometimes,no,2.708168,no,1,1.425852,no,Public_Transportation,Obesity_Type_I
+Male,18,1.803527,108.251044,yes,yes,2,1.709546,Sometimes,no,2.530157,no,1,0.6454,no,Public_Transportation,Obesity_Type_I
+Male,29.409825,1.801224,108.15619,yes,yes,2.385502,2.883984,Sometimes,no,2.140544,no,1.144876,1.767468,Sometimes,Automobile,Obesity_Type_I
+Male,28.421533,1.829239,107.10819,yes,yes,2.465575,2.935381,Sometimes,no,2.480555,no,1.00183,1.670313,Sometimes,Automobile,Obesity_Type_I
+Female,18,1.692913,89.93889,yes,yes,2.818502,3,Sometimes,no,1.991251,no,1.492967,0,no,Public_Transportation,Obesity_Type_I
+Female,18,1.668555,85.607466,yes,yes,2.081238,3,Sometimes,no,1.18363,no,1.17177,0,no,Public_Transportation,Obesity_Type_I
+Male,23.246702,1.652971,93.310284,yes,yes,1.368978,3,Sometimes,no,1.953152,no,0.104707,0.22183,no,Automobile,Obesity_Type_I
+Male,24.481032,1.661277,90.744965,yes,yes,1.712848,3,Sometimes,no,1.162153,no,0.458981,0.885532,no,Automobile,Obesity_Type_I
+Male,29,1.641784,89.424947,yes,yes,1.3307,3,Sometimes,no,1.880967,no,0.322013,0,Sometimes,Automobile,Obesity_Type_I
+Male,27.968765,1.673767,89.995034,yes,yes,1.961069,3,Sometimes,no,2.652327,no,0.56931,0.319008,Sometimes,Automobile,Obesity_Type_I
+Female,18.024744,1.617192,83.121811,yes,yes,2.976509,3,Sometimes,no,1.022705,no,1.02569,0.313016,no,Public_Transportation,Obesity_Type_I
+Female,18.10682,1.602129,82.412665,yes,yes,2.319648,3,Sometimes,no,1.107164,no,0.692123,0.30402,no,Public_Transportation,Obesity_Type_I
+Male,21.379676,1.701413,96.710735,yes,yes,2,2.961192,Sometimes,no,2.623344,no,0.981686,1.35537,no,Public_Transportation,Obesity_Type_I
+Male,21.353237,1.709731,95.032177,yes,yes,2,2.973476,Sometimes,no,2,no,0,1.331527,no,Public_Transportation,Obesity_Type_I
+Male,20.338815,1.698829,98.572753,yes,yes,2,2.392811,Sometimes,no,2,no,0,1.80931,no,Public_Transportation,Obesity_Type_I
+Male,20.720639,1.705304,99.873716,yes,yes,2,1.293342,Sometimes,no,2,no,0,1.917679,no,Public_Transportation,Obesity_Type_I
+Female,19.045357,1.61291,82.193405,yes,yes,1.261288,2.930044,Sometimes,no,1.166655,no,0.133398,0.95174,no,Public_Transportation,Obesity_Type_I
+Female,18.945961,1.605469,82.039,yes,yes,2.76533,3,Sometimes,no,1.048584,no,0.192559,0.720411,no,Public_Transportation,Obesity_Type_I
+Male,18.88061,1.80416,104.40682,yes,yes,2,3,Sometimes,no,3,no,2.2405,0,no,Public_Transportation,Obesity_Type_I
+Male,19.850524,1.785062,104.187314,yes,yes,2,3,Sometimes,no,2.15157,no,1.605983,0,no,Public_Transportation,Obesity_Type_I
+Male,30.200946,1.755926,112.289883,yes,yes,2.317734,3,Sometimes,no,2.152736,no,0,1.108145,no,Automobile,Obesity_Type_II
+Male,25.314589,1.787802,115.127662,yes,yes,1.588114,3,Sometimes,no,2.115967,no,1.541072,0,no,Automobile,Obesity_Type_II
+Male,37.186795,1.704877,107.94747,yes,yes,2.549782,3.985442,Sometimes,no,1,no,1.976582,0,no,Public_Transportation,Obesity_Type_II
+Male,32.707653,1.695347,102.810704,yes,yes,2.795086,2.129909,Sometimes,no,1,no,1.989316,0,no,Public_Transportation,Obesity_Type_II
+Male,27.562425,1.908608,128.828122,yes,yes,2.206276,3,Sometimes,no,1.813082,no,1.94984,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.421596,1.819296,122.137917,yes,yes,2,3,Sometimes,no,1.984323,no,0.793262,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,26.945139,1.773259,118.154345,yes,no,2.18354,3,Sometimes,no,2.277937,no,0.68183,0,Sometimes,Automobile,Obesity_Type_II
+Male,34.139453,1.774647,120.151967,yes,no,2.962415,3,Sometimes,no,2.172649,no,1.097905,0,Sometimes,Automobile,Obesity_Type_II
+Male,26.225442,1.773664,116.160329,yes,yes,2.442536,3,Sometimes,no,2.174371,no,0,1.928972,Sometimes,Public_Transportation,Obesity_Type_II
+Male,28.363149,1.793476,112.725005,yes,yes,1.99124,3,Sometimes,no,2,no,0,0.292956,Sometimes,Public_Transportation,Obesity_Type_II
+Male,27.991467,1.82559,120.860386,yes,yes,3,3,Sometimes,no,3,no,0.691369,1.415536,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.498751,1.885543,121.253083,yes,yes,2.408561,3,Sometimes,no,2.851904,no,1.320065,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.605225,1.75,120,yes,yes,2.758394,3,Sometimes,yes,2.174248,no,1.079524,1.358163,Sometimes,Automobile,Obesity_Type_II
+Male,31.457413,1.87407,128.867444,yes,yes,2.956297,3,Sometimes,yes,1.2751,no,0.901924,1.875023,Sometimes,Automobile,Obesity_Type_II
+Male,24.82929,1.755967,112.256165,yes,yes,1.369529,3,Sometimes,no,2,no,1.596576,0.0026,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.739421,1.757069,117.298233,yes,yes,1.392665,3,Sometimes,no,2,no,1.097983,0.630866,Sometimes,Public_Transportation,Obesity_Type_II
+Male,41,1.75,116.594351,yes,yes,2,2.831771,Sometimes,no,1.725226,no,0.356288,0,Sometimes,Automobile,Obesity_Type_II
+Male,27.83173,1.704028,101.634313,yes,yes,2.630401,1,Sometimes,no,1,no,0.245354,0.315261,Sometimes,Automobile,Obesity_Type_II
+Male,23.260061,1.849003,121.414744,yes,yes,3,2.595126,Sometimes,no,2.054072,no,0.835271,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.705772,1.841989,122.024954,yes,yes,3,2.52751,Sometimes,no,1.645338,no,1.025438,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.041043,1.613491,98.490766,yes,yes,3,2.120936,Sometimes,no,1,no,1.543386,0.890213,no,Public_Transportation,Obesity_Type_II
+Male,20.013906,1.647821,106.509947,yes,yes,2.869778,1.468948,Sometimes,no,1.656082,no,0,1.444033,no,Public_Transportation,Obesity_Type_II
+Male,30.02019,1.75834,112.010504,yes,yes,1.868212,3,Sometimes,no,2.023328,no,0,0.613992,Sometimes,Automobile,Obesity_Type_II
+Male,25.15462,1.758398,112.089022,yes,yes,1.108663,3,Sometimes,no,2,no,1.230219,0.001716,Sometimes,Automobile,Obesity_Type_II
+Male,30.870724,1.670774,101.626189,yes,yes,2.907744,3.990925,Sometimes,no,1,no,1.99975,0,no,Public_Transportation,Obesity_Type_II
+Male,28.712995,1.665585,98.66176,yes,yes,2.95801,2.834253,Sometimes,no,1,no,1.999886,0,no,Public_Transportation,Obesity_Type_II
+Male,30.710511,1.914186,129.852531,yes,yes,2.197261,3,Sometimes,no,1.11184,no,0.906843,0.976473,Sometimes,Public_Transportation,Obesity_Type_II
+Male,29.827481,1.899588,124.269251,yes,yes,2.048962,3,Sometimes,no,2.822871,no,1.066169,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.86893,1.75,118.102897,yes,yes,2.139196,3,Sometimes,no,2.04843,no,0.368026,0.360986,Sometimes,Automobile,Obesity_Type_II
+Male,36.542885,1.75,119.434645,yes,yes,2.72989,3,Sometimes,no,2.030084,no,0.592607,0.754417,Sometimes,Automobile,Obesity_Type_II
+Male,21.556361,1.773664,116.160329,yes,yes,2,3,Sometimes,no,2,no,1.399183,1,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.963649,1.792998,116.102615,yes,yes,1.994679,3,Sometimes,no,2,no,0.966617,0.739006,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.145295,1.82559,120.860386,yes,yes,2.403421,3,Sometimes,no,2.490613,no,2,0.707768,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.2984,1.827279,120.996074,yes,yes,3,3,Sometimes,no,3,no,1.110215,0.396352,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.755387,1.775416,121.204668,yes,yes,2.758394,3,Sometimes,no,2.161303,no,0.428173,0.716327,Sometimes,Automobile,Obesity_Type_II
+Male,30.62865,1.766975,118.363376,yes,yes,2.964319,3,Sometimes,no,2.377257,no,0.614959,1.875023,Sometimes,Automobile,Obesity_Type_II
+Male,23.454558,1.754218,117.384745,yes,yes,1.617093,3,Sometimes,no,2,no,1.818052,0.631825,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.432227,1.754996,119.087557,yes,yes,1.631144,3,Sometimes,no,2,no,1.593183,0.86374,Sometimes,Public_Transportation,Obesity_Type_II
+Male,40.50021,1.748103,108.308112,yes,yes,2.317459,3.734323,Sometimes,no,1,no,1.628637,0,no,Automobile,Obesity_Type_II
+Male,34.576714,1.73325,103.669116,yes,yes,2.501224,2.049565,Sometimes,no,1,no,1.746583,0,no,Automobile,Obesity_Type_II
+Male,22.276969,1.84995,121.786485,yes,yes,3,2.272214,Sometimes,no,1.555534,no,0.348839,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,21.963787,1.849601,122.333425,yes,yes,3,2.218285,Sometimes,no,1.340117,no,0.428259,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.001685,1.606066,99.961731,yes,yes,3,1.418833,Sometimes,no,1,no,1.19102,1.384186,no,Public_Transportation,Obesity_Type_II
+Male,20.027764,1.611434,103.175516,yes,yes,2.983042,1.109956,Sometimes,no,1.430444,no,0,1.690901,no,Public_Transportation,Obesity_Type_II
+Male,28.704462,1.762887,113.90198,yes,yes,2.323351,3,Sometimes,no,2.165048,no,0,1.493716,Sometimes,Automobile,Obesity_Type_II
+Male,26.774115,1.755938,112.287678,yes,yes,1.428289,3,Sometimes,no,2.117733,no,0.485322,0.696948,Sometimes,Automobile,Obesity_Type_II
+Male,25.539411,1.787052,115.428276,yes,yes,1.992889,3,Sometimes,no,2.141271,no,1.121038,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.300208,1.765258,114.330023,yes,yes,1.562804,3,Sometimes,no,2.075493,no,1.553734,0.000436,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.194458,1.726279,110.714711,yes,yes,1.794825,3.914454,Sometimes,no,1.972016,no,0.668963,0,no,Automobile,Obesity_Type_II
+Male,40.794057,1.735851,109.889334,yes,yes,2.305349,3.169089,Sometimes,no,1.02161,no,0.908981,0,no,Automobile,Obesity_Type_II
+Male,33.55336,1.699793,103.841672,yes,yes,2.576449,2.419656,Sometimes,no,1,no,1.982379,0,no,Public_Transportation,Obesity_Type_II
+Male,30.304203,1.703202,103.0344,yes,yes,2.754645,2.175432,Sometimes,no,1.541733,no,0.358709,1.0739,no,Public_Transportation,Obesity_Type_II
+Male,30.958051,1.906821,128.856677,yes,yes,2.633855,3,Sometimes,no,1.619305,no,1.847802,0.424612,Sometimes,Public_Transportation,Obesity_Type_II
+Male,29.429687,1.910987,129.935666,yes,yes,2.200588,3,Sometimes,no,1.295697,no,1.058378,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.520854,1.784049,120.644178,yes,yes,2.499108,3,Sometimes,no,2.040952,no,0.838739,0.489854,Sometimes,Automobile,Obesity_Type_II
+Male,30.610436,1.783914,120.549592,yes,yes,2.05687,3,Sometimes,no,2.358088,no,0.093418,1.759954,Sometimes,Automobile,Obesity_Type_II
+Male,26.740655,1.863883,120.202596,yes,yes,2.247795,3,Sometimes,no,2.816015,no,0.712726,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,26.680376,1.812259,118.277657,yes,yes,2.239634,3,Sometimes,no,2.684264,no,0.979306,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,37.056193,1.75015,118.206565,yes,yes,2.09283,3,Sometimes,no,2.077704,no,0.598655,0,Sometimes,Automobile,Obesity_Type_II
+Male,28.825279,1.815347,120.399758,yes,yes,2.9673,3,Sometimes,no,2.530035,no,1.045107,0.947866,Sometimes,Automobile,Obesity_Type_II
+Male,25.883749,1.767077,114.133149,yes,yes,2.225731,3,Sometimes,no,2.154326,no,1.266866,0.390178,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.486521,1.762461,115.531622,yes,yes,2.178308,3,Sometimes,no,2.165804,no,1.420675,1.875683,Sometimes,Public_Transportation,Obesity_Type_II
+Male,29.883021,1.779049,112.438508,yes,yes,2.065752,3,Sometimes,no,2.078297,no,0,0.677354,Sometimes,Automobile,Obesity_Type_II
+Male,26.957645,1.780731,112.957922,yes,yes,2.263245,3,Sometimes,no,2.092509,no,0,1.836853,Sometimes,Automobile,Obesity_Type_II
+Male,26.490926,1.872517,120.898397,yes,yes,2.443538,3,Sometimes,no,2.939492,no,1.208142,0.738935,Sometimes,Public_Transportation,Obesity_Type_II
+Male,27.558801,1.801224,119.050381,yes,yes,2.744994,3,Sometimes,no,2.478838,no,0.684487,0.677909,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.908879,1.876898,121.277832,yes,yes,2.630137,2.806298,Sometimes,no,1.970508,no,1.304291,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.201622,1.77558,120.589419,yes,yes,2.113843,3,Sometimes,no,2.316069,no,1.804157,0.357314,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.689773,1.76569,120.082941,yes,yes,2.821727,3,Sometimes,no,2.173279,no,1.096556,0.232858,Sometimes,Automobile,Obesity_Type_II
+Male,30.242597,1.759772,118.382361,yes,yes,2.312528,3,Sometimes,no,2.208068,no,1.07672,1.035711,Sometimes,Automobile,Obesity_Type_II
+Male,31.346845,1.823545,126.460936,yes,yes,2.938801,3,Sometimes,yes,1.478994,no,0.946763,1.360463,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.577343,1.868923,125.064264,yes,yes,2.050619,3,Sometimes,yes,1.515183,no,0.849811,0.261901,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.124595,1.77151,113.207124,yes,yes,1.457758,3,Sometimes,no,2.020249,no,1.556709,0.00133,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.509034,1.77219,114.097656,yes,yes,2.19331,3,Sometimes,no,2.089983,no,1.360994,0.857438,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.478662,1.858265,117.57457,yes,yes,2.028571,3,Sometimes,no,2.530428,no,1.31257,0.066805,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.100513,1.830596,118.424156,yes,yes,1.455602,3,Sometimes,no,2.363307,no,1.144683,0.101026,Sometimes,Public_Transportation,Obesity_Type_II
+Male,38.523646,1.765836,118.533246,yes,yes,2.177896,2.987652,Sometimes,no,1.745095,no,0.656548,0,Sometimes,Automobile,Obesity_Type_II
+Male,38.71216,1.770278,117.29188,yes,yes,2.252382,2.842848,Sometimes,no,1.879381,no,0.919388,0,Sometimes,Automobile,Obesity_Type_II
+Male,25.717522,1.673491,103.706181,yes,yes,2.668949,1.134321,Sometimes,no,1.279443,no,0.11243,1.135503,no,Public_Transportation,Obesity_Type_II
+Male,32.259623,1.703346,102.686239,yes,yes,2.723953,1.924168,Sometimes,no,1,no,1.622055,0.069367,no,Public_Transportation,Obesity_Type_II
+Male,23.47007,1.842906,121.142535,yes,yes,3,2.701689,Sometimes,no,2.738485,no,0.992253,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.408805,1.779547,118.740035,yes,yes,2.736298,2.992903,Sometimes,no,2.045561,no,0.854957,0.428452,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.443011,1.873368,121.82962,yes,yes,2.722161,2.658837,Sometimes,no,2.375707,no,1.299469,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.906342,1.755902,120.021161,yes,yes,2.971574,2.80742,Sometimes,no,1.962646,no,1.583832,0.504816,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.007488,1.617655,100.941357,yes,yes,2.871016,1.834472,Sometimes,no,1.016585,no,0.236168,0.974939,no,Public_Transportation,Obesity_Type_II
+Male,30.686701,1.644517,100.004418,yes,yes,2.96405,2.123138,Sometimes,no,1,no,1.699181,0.620465,no,Public_Transportation,Obesity_Type_II
+Male,20.184451,1.701284,104.578255,yes,yes,2.653721,1.265463,Sometimes,no,1.368457,no,0.218356,0.768071,no,Public_Transportation,Obesity_Type_II
+Male,25.447208,1.65891,104.548794,yes,yes,2.859097,1.340361,Sometimes,no,1.530508,no,0.174475,1.261705,no,Public_Transportation,Obesity_Type_II
+Male,30.002029,1.759324,112.000381,yes,yes,1.572036,3,Sometimes,no,2.003563,no,0,0.340196,Sometimes,Automobile,Obesity_Type_II
+Male,30.188303,1.758382,112.10074,yes,yes,1.387489,3,Sometimes,no,2.017712,no,0,0.731257,Sometimes,Automobile,Obesity_Type_II
+Male,24.244029,1.622297,99.982541,yes,yes,2.941929,3.989492,Sometimes,no,1.014135,no,1.958694,0.687342,no,Public_Transportation,Obesity_Type_II
+Male,24.521879,1.623278,97.582959,yes,yes,2.973569,2.650088,Sometimes,no,1.003063,no,1.981154,0.157007,no,Public_Transportation,Obesity_Type_II
+Male,29.721964,1.918859,129.991623,yes,yes,2.041376,3,Sometimes,no,1.120213,no,1.05545,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,29.906575,1.913252,129.232216,yes,yes,2.02472,3,Sometimes,no,1.800335,no,1.196368,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,37.084742,1.75,118.07381,yes,yes,2.133964,3,Sometimes,no,2.008361,no,0.202029,0.200516,Sometimes,Automobile,Obesity_Type_II
+Male,39.08886,1.75,119.029103,yes,yes,2.702457,3,Sometimes,no,2.005194,no,0.325313,0.419055,Sometimes,Automobile,Obesity_Type_II
+Male,22.658572,1.784994,113.714521,yes,yes,1.760038,3,Sometimes,no,2.003531,no,1.089061,0.072264,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.889099,1.792533,116.157766,yes,yes,1.996646,3,Sometimes,no,2,no,1.600536,0.739684,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.63474,1.829757,120.980508,yes,yes,2.644094,3,Sometimes,no,2.740525,no,2,0.500936,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.920362,1.796415,120.988454,yes,yes,2.475892,3,Sometimes,no,2.949356,no,1.866839,0.100048,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.783845,1.75,120,yes,yes,2.941627,3,Sometimes,no,2.318134,no,0.582686,1.588046,Sometimes,Automobile,Obesity_Type_II
+Male,31.627962,1.762389,118.548733,yes,yes,2.998441,3,Sometimes,no,2.462916,no,0.554646,1.99219,Sometimes,Automobile,Obesity_Type_II
+Male,22.976188,1.825718,121.236915,yes,yes,2.397284,2.89292,Sometimes,no,1.983973,no,1.859667,0.0026,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.977357,1.839699,120.431551,yes,yes,2.382705,2.938902,Sometimes,no,1.999278,no,1.686229,0.630866,Sometimes,Public_Transportation,Obesity_Type_II
+Male,40.973007,1.749405,109.908779,yes,yes,2.176317,2.986637,Sometimes,no,1.015672,no,0.94141,0,no,Automobile,Obesity_Type_II
+Male,41,1.75,115.806977,yes,yes,2,2.701521,Sometimes,no,1.142644,no,0.514225,0,no,Automobile,Obesity_Type_II
+Male,21.721507,1.849998,122.119682,yes,yes,3,2.014671,Sometimes,no,1.292786,no,0.145687,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,21.544554,1.84998,122.609911,yes,yes,3,1.971659,Sometimes,no,1.179254,no,0.178856,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.000307,1.607939,100.618239,yes,yes,2.877743,1.311797,Sometimes,no,1.000463,no,0.182249,1.4312,no,Public_Transportation,Obesity_Type_II
+Male,24.0409,1.603226,99.605527,yes,yes,3,1.262831,Sometimes,no,1,no,0.883171,1.382995,no,Public_Transportation,Obesity_Type_II
+Male,29.509151,1.770663,112.471118,yes,yes,2.303041,3,Sometimes,no,2.065817,no,0,0.725222,Sometimes,Automobile,Obesity_Type_II
+Male,29.246269,1.758038,112.395309,yes,yes,2.323003,3,Sometimes,no,2.155558,no,0,1.251554,Sometimes,Automobile,Obesity_Type_II
+Male,25.341399,1.786997,115.025361,yes,yes,1.99953,3,Sometimes,no,2.111908,no,1.453042,0.849503,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.314163,1.771837,114.906095,yes,yes,1.585183,3,Sometimes,no,2.101841,no,1.543961,0.000073,Sometimes,Public_Transportation,Obesity_Type_II
+Male,40.366238,1.722396,109.349025,yes,yes,2.281963,3.770379,Sometimes,no,1,no,1.330519,0,no,Automobile,Obesity_Type_II
+Male,33.749594,1.701387,107.025415,yes,yes,2.561638,2.87747,Sometimes,no,1,no,1.980401,0,no,Automobile,Obesity_Type_II
+Male,32.867331,1.697421,103.017623,yes,yes,2.600217,2.175153,Sometimes,no,1,no,1.985536,0,no,Public_Transportation,Obesity_Type_II
+Male,30.403205,1.696365,102.815983,yes,yes,2.79166,2.13229,Sometimes,no,1.254589,no,0.652736,1.040713,no,Public_Transportation,Obesity_Type_II
+Male,29.687488,1.909198,129.679131,yes,yes,2.08868,3,Sometimes,no,1.520215,no,1.857351,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,28.992809,1.909105,129.874864,yes,yes,2.206119,3,Sometimes,no,1.483856,no,1.113169,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.475248,1.801368,121.094257,yes,yes,2.328469,3,Sometimes,no,2.001208,no,0.800487,0.176678,Sometimes,Automobile,Obesity_Type_II
+Male,30.350516,1.860292,123.721352,yes,yes,2.002784,3,Sometimes,no,2.292906,no,1.034031,0,Sometimes,Automobile,Obesity_Type_II
+Male,26.684354,1.819535,118.332689,yes,yes,1.975675,3,Sometimes,no,2.357969,no,0.704236,0.010721,Sometimes,Public_Transportation,Obesity_Type_II
+Male,26.607478,1.793174,118.165082,yes,yes,2.002076,3,Sometimes,no,2.338373,no,0.897562,0.081156,Sometimes,Public_Transportation,Obesity_Type_II
+Male,33.174147,1.75015,118.299585,yes,yes,2.218599,3,Sometimes,no,2.104337,no,0.766007,0.262118,Sometimes,Automobile,Obesity_Type_II
+Male,32.29016,1.754956,120.098812,yes,yes,2.9673,3,Sometimes,no,2.530035,no,0.955317,1.339232,Sometimes,Automobile,Obesity_Type_II
+Male,26.050109,1.773115,115.089316,yes,yes,2.392179,3,Sometimes,no,2.16467,no,1.079934,1.073026,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.846279,1.772731,115.828167,yes,yes,2.381164,3,Sometimes,no,2.170225,no,1.211048,1.89933,Sometimes,Public_Transportation,Obesity_Type_II
+Male,29.620095,1.787933,112.536367,yes,yes,2.008245,3,Sometimes,no,2.040137,no,0,0.474216,Sometimes,Automobile,Obesity_Type_II
+Male,27.439056,1.785277,112.740797,yes,yes,2.155182,3,Sometimes,no,2.049079,no,0,1.74992,Sometimes,Automobile,Obesity_Type_II
+Male,28.493397,1.817572,120.8158,yes,yes,2.969233,3,Sometimes,no,2.807984,no,0.982134,1.191998,Sometimes,Public_Transportation,Obesity_Type_II
+Male,27.474245,1.84342,120.420406,yes,yes,2.765063,3,Sometimes,no,2.867206,no,0.706777,0.677909,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.036269,1.874519,121.244969,yes,yes,2.630137,3,Sometimes,no,2.960088,no,1.356468,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.825398,1.796332,120.901591,yes,yes,2.195964,3,Sometimes,no,2.514872,no,1.664722,0.127673,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.493946,1.756221,119.117122,yes,yes,2.619987,3,Sometimes,no,2.194746,no,1.076926,1.090995,Sometimes,Automobile,Obesity_Type_II
+Male,30.607546,1.757132,118.565568,yes,yes,2.918113,3,Sometimes,no,2.240463,no,1.076248,1.480875,Sometimes,Automobile,Obesity_Type_II
+Male,31.194323,1.889104,129.157346,yes,yes,2.889485,3,Sometimes,yes,1.279771,no,0.941424,0.008343,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.36347,1.869323,127.507411,yes,yes,2.939727,3,Sometimes,yes,1.344122,no,0.923428,1.432336,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.027254,1.757154,112.200812,yes,yes,1.264234,3,Sometimes,no,2,no,1.333435,0.002168,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.058566,1.764484,113.234349,yes,yes,1.517912,3,Sometimes,no,2.038958,no,1.590255,0.00164,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.417552,1.774775,117.398976,yes,yes,2.23372,2.993634,Sometimes,no,2.028368,no,0.863158,0.449886,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.5822,1.769933,117.708705,yes,yes,1.475906,2.999346,Sometimes,no,2.01943,no,1.046878,0.460866,Sometimes,Public_Transportation,Obesity_Type_II
+Male,40.106145,1.760175,117.651046,yes,yes,2.032883,2.976211,Sometimes,no,1.726109,no,0.477855,0,Sometimes,Automobile,Obesity_Type_II
+Male,40.174191,1.763029,116.974504,yes,yes,2.046651,2.842035,Sometimes,no,1.732072,no,0.584272,0,Sometimes,Automobile,Obesity_Type_II
+Male,27.186873,1.679515,102.872802,yes,yes,2.667229,1.09749,Sometimes,no,1.225958,no,0.206954,1.003011,no,Public_Transportation,Obesity_Type_II
+Male,30.424369,1.699354,100.176866,yes,yes,2.819934,1.91863,Sometimes,no,1,no,1.39302,0.553311,no,Public_Transportation,Obesity_Type_II
+Male,23.141402,1.849307,121.658729,yes,yes,3,2.510135,Sometimes,no,1.693362,no,0.769709,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.178638,1.86741,121.684311,yes,yes,2.954417,2.65772,Sometimes,no,2.104696,no,0.870056,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.832105,1.86714,121.835883,yes,yes,2.826251,2.604998,Sometimes,no,1.842173,no,1.284798,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.083621,1.848553,121.421121,yes,yes,3,2.567567,Sometimes,no,2.011023,no,0.916478,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.079971,1.61981,98.54302,yes,yes,2.95841,2.434347,Sometimes,no,1,no,1.930033,0.754023,no,Public_Transportation,Obesity_Type_II
+Male,26.947786,1.647807,99.592225,yes,yes,2.935157,1.845858,Sometimes,no,1,no,1.089891,0.715993,no,Public_Transportation,Obesity_Type_II
+Male,20.101026,1.619128,104.303711,yes,yes,2.870895,1.627555,Sometimes,no,1.375728,no,0.210181,1.163117,no,Public_Transportation,Obesity_Type_II
+Male,22.789402,1.64187,104.270062,yes,yes,2.869833,1.569176,Sometimes,no,1.533682,no,0.167943,1.368262,no,Public_Transportation,Obesity_Type_II
+Male,28.404332,1.787379,112.173731,yes,yes,1.878251,3,Sometimes,no,2.022933,no,0,0.325445,Sometimes,Automobile,Obesity_Type_II
+Male,30.003358,1.758355,112.007101,yes,yes,1.750809,3,Sometimes,no,2.002871,no,0,0.114457,Sometimes,Automobile,Obesity_Type_II
+Male,25.136116,1.76414,113.089716,yes,yes,1.168856,3,Sometimes,no,2.013205,no,1.246223,0.00159,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.139466,1.767186,112.226032,yes,yes,1.443674,3,Sometimes,no,2.002194,no,1.386151,0.001337,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.743821,1.67782,102.022057,yes,yes,2.826036,2.371658,Sometimes,no,1,no,1.996487,0,no,Public_Transportation,Obesity_Type_II
+Male,30.796262,1.668478,100.543983,yes,yes,2.9553,2.378211,Sometimes,no,1,no,1.738267,0.394533,no,Public_Transportation,Obesity_Type_II
+Male,28.393111,1.685462,99.540122,yes,yes,2.774562,2.301129,Sometimes,no,1,no,1.621733,0.138314,no,Public_Transportation,Obesity_Type_II
+Male,27.93982,1.694642,99.709329,yes,yes,2.772027,2.454432,Sometimes,no,1,no,0.858554,0.051268,no,Public_Transportation,Obesity_Type_II
+Male,31.034092,1.886109,129.349471,yes,yes,2.499626,3,Sometimes,yes,1.125338,no,0.94522,1.022505,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.684347,1.915,129.966428,yes,yes,2.108638,3,Sometimes,yes,1.014876,no,0.987521,0.047473,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.108216,1.841908,124.070717,yes,yes,2.467548,3,Sometimes,no,2.66029,no,0.994422,0.310394,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.347497,1.868127,123.172214,yes,yes,2.585942,3,Sometimes,no,2.302506,no,0.597863,0.111925,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.761799,1.751688,119.205308,yes,yes,2.14961,3,Sometimes,no,2.133876,no,0.393452,0.345887,Sometimes,Automobile,Obesity_Type_II
+Male,30.976932,1.755333,118.237782,yes,yes,2.938616,3,Sometimes,no,2.06039,no,0.371508,1.333559,Sometimes,Automobile,Obesity_Type_II
+Male,37.588628,1.754217,117.8972,yes,yes,2.535154,2.915921,Sometimes,no,1.8924,no,0.693732,0.06741,Sometimes,Automobile,Obesity_Type_II
+Male,37.997912,1.76071,118.668332,yes,yes,2.611222,2.992083,Sometimes,no,1.796376,no,0.641954,0.20281,Sometimes,Automobile,Obesity_Type_II
+Male,21.6548,1.755938,116.311962,yes,yes,1.800122,3,Sometimes,no,2,no,1.689525,0.764452,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.829753,1.765137,116.669906,yes,yes,1.482722,3,Sometimes,no,2,no,1.112504,0.690353,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.015173,1.788239,115.382519,yes,yes,1.735664,3,Sometimes,no,2.041536,no,1.392406,0.39174,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.812795,1.767121,116.164351,yes,yes,1.655684,3,Sometimes,no,2,no,0.976425,0.690082,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.975685,1.840039,120.975489,yes,yes,2.598207,2.849347,Sometimes,no,2.006167,no,1.52001,0.295685,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.149036,1.824901,120.805715,yes,yes,2.225149,3,Sometimes,no,2.357978,no,1.943743,0.682128,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.062942,1.828391,120.998266,yes,yes,3,3,Sometimes,no,3,no,1.685369,0.264969,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.140074,1.829529,120.996581,yes,yes,3,3,Sometimes,no,3,no,1.467863,0.343635,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.722801,1.779325,120.751656,yes,yes,2.519592,3,Sometimes,no,2.229145,no,0.350717,1.140348,Sometimes,Automobile,Obesity_Type_II
+Male,30.916426,1.781139,120.794535,yes,yes,2.111887,3,Sometimes,no,2.357373,no,0.425621,1.416353,Sometimes,Automobile,Obesity_Type_II
+Male,31.010302,1.764166,119.373019,yes,yes,2.970983,3,Sometimes,no,2.828861,no,0.454944,1.915818,Sometimes,Automobile,Obesity_Type_II
+Male,30.605464,1.754796,118.805937,yes,yes,2.907542,3,Sometimes,no,2.284494,no,0.647632,1.668318,Sometimes,Automobile,Obesity_Type_II
+Male,24.622054,1.756509,117.368716,yes,yes,1.451337,3,Sometimes,no,2,no,1.384607,0.631217,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.745833,1.756772,117.326523,yes,yes,1.537505,3,Sometimes,no,2,no,1.631912,0.631422,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.327836,1.754439,119.441207,yes,yes,1.893428,3,Sometimes,no,2,no,1.917383,0.93648,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.411141,1.755142,119.192922,yes,yes,2.007845,2.954446,Sometimes,no,1.968796,no,1.587406,0.838947,Sometimes,Public_Transportation,Obesity_Type_II
+Male,39.825592,1.706741,108.012603,yes,yes,2.487781,3.755976,Sometimes,no,1,no,1.860765,0,no,Automobile,Obesity_Type_II
+Male,36.839761,1.74285,106.421042,yes,yes,2.541785,2.902639,Sometimes,no,1,no,1.668961,0,no,Automobile,Obesity_Type_II
+Male,33.789301,1.6811,102.523111,yes,yes,2.813775,2.372705,Sometimes,no,1,no,1.979355,0,no,Public_Transportation,Obesity_Type_II
+Male,34.161998,1.705682,103.067766,yes,yes,2.562687,2.100918,Sometimes,no,1.458545,no,1.167856,0.412329,no,Public_Transportation,Obesity_Type_II
+Male,22.580038,1.849507,121.560938,yes,yes,3,2.272801,Sometimes,no,1.560064,no,0.79677,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.851721,1.853373,121.737836,yes,yes,2.922511,2.692889,Sometimes,no,1.809531,no,0.478595,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.254934,1.84753,122.06261,yes,yes,3,2.280545,Sometimes,no,1.591597,no,0.932783,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.343204,1.87034,121.458232,yes,yes,2.836055,2.675411,Sometimes,no,1.711666,no,0.93732,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.006271,1.607787,100.783434,yes,yes,2.880759,1.487674,Sometimes,no,1.01378,no,1.00109,1.09838,no,Public_Transportation,Obesity_Type_II
+Male,24.001196,1.603091,100.209405,yes,yes,3,1.02075,Sometimes,no,1,no,0.233056,1.707421,no,Public_Transportation,Obesity_Type_II
+Male,20.156664,1.620109,103.393354,yes,yes,2.766441,1.240424,Sometimes,no,1.413218,no,0.165269,0.862587,no,Public_Transportation,Obesity_Type_II
+Male,23.360307,1.610647,100.642257,yes,yes,2.997524,1.154318,Sometimes,no,1.238057,no,1.1293,1.665621,no,Public_Transportation,Obesity_Type_II
+Male,28.986237,1.758618,113.501549,yes,yes,2.320201,3,Sometimes,no,2.164784,no,0,1.465479,Sometimes,Automobile,Obesity_Type_II
+Male,29.713169,1.763259,113.215814,yes,yes,2.181057,3,Sometimes,no,2.08554,no,0,1.439004,Sometimes,Automobile,Obesity_Type_II
+Male,27.394123,1.764138,112.323213,yes,yes,1.924632,3,Sometimes,no,2.006595,no,0.1768,0.511694,Sometimes,Automobile,Obesity_Type_II
+Male,24.912254,1.75596,112.277567,yes,yes,1.397468,3,Sometimes,no,2.01897,no,0.665439,0.062167,Sometimes,Automobile,Obesity_Type_II
+Male,25.526746,1.783381,115.347176,yes,yes,2.191429,3,Sometimes,no,2.102709,no,1.32534,0.380979,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.523127,1.786532,114.534678,yes,yes,2.100177,3,Sometimes,no,2.099687,no,1.281165,0.247332,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.822348,1.766626,114.187096,yes,yes,2.075321,3,Sometimes,no,2.144838,no,1.501754,0.276319,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.879411,1.765464,114.144378,yes,yes,1.626369,3,Sometimes,no,2.109697,no,1.352973,0.076693,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.361365,1.75853,111.635463,yes,yes,1.263216,3.219347,Sometimes,no,1.981782,no,0.482625,0,Sometimes,Automobile,Obesity_Type_II
+Male,30.451174,1.758539,111.950413,yes,yes,1.067909,3.656401,Sometimes,no,1.98459,no,0.054238,0,Sometimes,Automobile,Obesity_Type_II
+Male,40.564513,1.748015,109.758736,yes,yes,2.310751,3.220181,Sometimes,no,1.006643,no,1.15804,0,no,Automobile,Obesity_Type_II
+Male,40.501722,1.744974,111.169678,yes,yes,2.294259,2.850948,Sometimes,no,1.87029,no,0.917014,0,no,Automobile,Obesity_Type_II
+Male,30.315784,1.701566,103.743534,yes,yes,2.57649,2.187145,Sometimes,no,1.38107,no,1.928275,0.413663,no,Public_Transportation,Obesity_Type_II
+Male,33.293166,1.696412,103.250355,yes,yes,2.679664,2.415522,Sometimes,no,1,no,1.987296,0,no,Public_Transportation,Obesity_Type_II
+Male,30.638944,1.680489,102.004554,yes,yes,2.938687,2.138375,Sometimes,no,1.251715,no,1.181811,0.778375,no,Public_Transportation,Obesity_Type_II
+Male,29.791101,1.703317,102.592171,yes,yes,2.654792,1.077331,Sometimes,no,1.345407,no,0.327418,0.386861,no,Public_Transportation,Obesity_Type_II
+Male,31.205668,1.877732,127.161381,yes,yes,2.731368,3,Sometimes,yes,1.486824,no,1.485978,1.150439,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.899219,1.909639,129.013178,yes,yes,2.22259,3,Sometimes,yes,1.591909,no,1.392026,0.917727,Sometimes,Public_Transportation,Obesity_Type_II
+Male,29.669219,1.909188,129.19449,yes,yes,2.432355,3,Sometimes,no,1.336526,no,1.63812,0.090341,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.595632,1.910672,129.232708,yes,yes,2.497548,3,Sometimes,no,1.362583,no,1.144076,0.173232,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.554956,1.779136,120.60094,yes,yes,2.671238,3,Sometimes,no,2.145368,no,0.882709,0.593917,Sometimes,Automobile,Obesity_Type_II
+Male,31.571392,1.767485,120.158049,yes,yes,2.57691,3,Sometimes,no,2.080187,no,1.04268,0.376683,Sometimes,Automobile,Obesity_Type_II
+Male,30.577944,1.783953,120.613561,yes,yes,2.341999,3,Sometimes,no,2.299878,no,0.565242,0.921991,Sometimes,Automobile,Obesity_Type_II
+Male,30.717727,1.76714,120.344402,yes,yes,2.117121,3,Sometimes,no,2.193008,no,0.992829,1.556052,Sometimes,Automobile,Obesity_Type_II
+Male,26.734476,1.816197,119.622764,yes,yes,2.247037,3,Sometimes,no,2.718408,no,0.763595,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,26.699317,1.839941,119.956651,yes,yes,2.244654,3,Sometimes,no,2.795752,no,0.839481,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.659092,1.84842,117.631707,yes,yes,2.128574,3,Sometimes,no,2.531984,no,1.003294,0.026575,Sometimes,Public_Transportation,Obesity_Type_II
+Male,26.348156,1.830317,117.75701,yes,yes,2.068834,3,Sometimes,no,2.543841,no,1.217993,0.038253,Sometimes,Public_Transportation,Obesity_Type_II
+Male,37.638102,1.750085,118.114184,yes,yes,2.073224,3,Sometimes,no,2.024035,no,0.131371,0,Sometimes,Automobile,Obesity_Type_II
+Male,37.765356,1.763582,117.86159,yes,yes,2.145114,2.888193,Sometimes,no,2.038128,no,0.852344,0,Sometimes,Automobile,Obesity_Type_II
+Male,28.255199,1.816547,120.699119,yes,yes,2.997951,3,Sometimes,no,2.715856,no,0.739881,0.972054,Sometimes,Automobile,Obesity_Type_II
+Male,27.931432,1.805445,119.484614,yes,yes,2.911312,3,Sometimes,no,2.501808,no,0.94676,0.785701,Sometimes,Automobile,Obesity_Type_II
+Male,25.492855,1.770124,114.163921,yes,yes,2.159033,3,Sometimes,no,2.116399,no,1.331526,0.052942,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.550506,1.77274,114.254278,yes,yes,2.175276,3,Sometimes,no,2.128176,no,1.306476,0.079334,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.542454,1.763215,115.10813,yes,yes,2.21965,3,Sometimes,no,2.165408,no,1.315045,0.688985,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.758307,1.766547,115.45845,yes,yes,2.203962,3,Sometimes,no,2.15687,no,1.289421,1.220767,Sometimes,Public_Transportation,Obesity_Type_II
+Male,29.981494,1.773181,112.348722,yes,yes,1.947495,3,Sometimes,no,2.042073,no,0,0.618087,Sometimes,Automobile,Obesity_Type_II
+Male,29.891473,1.76003,112.409191,yes,yes,2.262292,3,Sometimes,no,2.115354,no,0,0.847587,Sometimes,Automobile,Obesity_Type_II
+Male,26.778684,1.780089,113.15464,yes,yes,2.219186,3,Sometimes,no,2.092326,no,0.545919,1.74288,Sometimes,Automobile,Obesity_Type_II
+Male,28.377958,1.766946,113.235538,yes,yes,2.314175,3,Sometimes,no,2.151809,no,0,1.578517,Sometimes,Automobile,Obesity_Type_II
+Male,26.199321,1.885119,121.065052,yes,yes,2.423291,3,Sometimes,no,2.902682,no,1.243567,0.555591,Sometimes,Public_Transportation,Obesity_Type_II
+Male,27.266287,1.828276,120.872294,yes,yes,2.637202,3,Sometimes,no,2.966647,no,1.159598,0.758897,Sometimes,Public_Transportation,Obesity_Type_II
+Male,27.635029,1.806227,120.423567,yes,yes,2.974006,3,Sometimes,no,2.565828,no,0.690269,1.339691,Sometimes,Public_Transportation,Obesity_Type_II
+Male,26.899886,1.812919,119.841446,yes,yes,2.347942,3,Sometimes,no,2.730663,no,0.689371,0.188693,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.758998,1.859717,121.284533,yes,yes,2.654076,2.574108,Sometimes,no,1.78471,no,1.170537,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.771001,1.86516,121.527369,yes,yes,2.739,2.740492,Sometimes,no,1.824561,no,1.094839,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.826684,1.783609,120.921535,yes,yes,2.591292,2.956422,Sometimes,no,2.31083,no,1.48524,0.281815,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.186273,1.794827,120.919703,yes,yes,2.611847,2.749334,Sometimes,no,2.364849,no,1.141708,0.063005,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.945369,1.759647,120.009392,yes,yes,2.766612,3,Sometimes,no,2.173417,no,1.089344,1.198439,Sometimes,Automobile,Obesity_Type_II
+Male,31.490699,1.773521,120.209711,yes,yes,2.777165,3,Sometimes,no,2.106861,no,0.925941,0.2916,Sometimes,Automobile,Obesity_Type_II
+Male,31.56724,1.753327,118.26569,yes,yes,2.232836,3,Sometimes,no,2.146398,no,0.886817,0.47787,Sometimes,Automobile,Obesity_Type_II
+Male,30.444081,1.761068,118.370628,yes,yes,2.571274,3,Sometimes,no,2.224164,no,1.018158,1.152899,Sometimes,Automobile,Obesity_Type_II
+Male,31.199261,1.848845,125.077863,yes,yes,2.49619,3,Sometimes,yes,1.662117,no,0.992371,0.217632,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.190219,1.842812,125.973927,yes,yes,2.151335,3,Sometimes,yes,1.491169,no,0.883542,0.527766,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.575349,1.825449,124.95278,yes,yes,2.01695,3,Sometimes,no,1.83412,no,0.797209,0.185499,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.702559,1.86198,126.418413,yes,yes,2.927187,3,Sometimes,no,1.508796,no,0.902776,1.015467,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.057878,1.763987,113.069667,yes,yes,1.412566,3,Sometimes,no,2.002495,no,1.592795,0.001867,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.151286,1.761519,112.835195,yes,yes,1.289315,3,Sometimes,no,2.007348,no,1.376217,0.001518,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.137087,1.772045,114.067936,yes,yes,1.624366,3,Sometimes,no,2.081719,no,1.538922,0.356868,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.3292,1.771817,114.004832,yes,yes,1.528331,3,Sometimes,no,2.079353,no,1.522429,0.228598,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.66668,1.79858,117.93329,yes,yes,2.037042,3,Sometimes,no,2.43699,no,1.016254,0.020044,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.01277,1.788586,117.849351,yes,yes,2.191108,2.993856,Sometimes,no,2.444917,no,1.055854,0.145284,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.047945,1.803207,118.332966,yes,yes,1.431346,3,Sometimes,no,2.098959,no,1.110868,0.196952,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.426457,1.836592,118.377601,yes,yes,1.84199,3,Sometimes,no,2.503244,no,1.226019,0.078207,Sometimes,Public_Transportation,Obesity_Type_II
+Male,37.207082,1.762921,118.40174,yes,yes,2.13683,2.993084,Sometimes,no,1.885926,no,0.615298,0,Sometimes,Automobile,Obesity_Type_II
+Male,38.10894,1.752863,119.201465,yes,yes,2.499388,2.989791,Sometimes,no,1.959777,no,0.6081,0.64676,Sometimes,Automobile,Obesity_Type_II
+Male,38.644441,1.768235,117.792268,yes,yes,2.230742,2.920373,Sometimes,no,1.831187,no,0.756277,0,Sometimes,Automobile,Obesity_Type_II
+Male,38.112989,1.766888,118.134898,yes,yes,2.240757,2.911568,Sometimes,no,1.895876,no,0.822186,0,Sometimes,Automobile,Obesity_Type_II
+Male,24.825393,1.603501,101.038263,yes,yes,2.996186,1.134042,Sometimes,no,1.270166,no,0.073065,1.551934,no,Public_Transportation,Obesity_Type_II
+Male,26.624342,1.690262,103.180918,yes,yes,2.649406,1.120102,Sometimes,no,1.153286,no,0.216908,0.619012,no,Public_Transportation,Obesity_Type_II
+Male,33.722449,1.712905,103.276087,yes,yes,2.525884,2.040582,Sometimes,no,1,no,1.67036,0.023959,no,Public_Transportation,Obesity_Type_II
+Male,32.516469,1.695735,102.784864,yes,yes,2.736647,2.015675,Sometimes,no,1,no,1.977918,0.056351,no,Public_Transportation,Obesity_Type_II
+Male,23.881938,1.829971,121.04172,yes,yes,2.684335,2.711238,Sometimes,no,2.732331,no,1.156024,0.442456,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.319635,1.84629,121.248035,yes,yes,3,2.695396,Sometimes,no,2.628816,no,0.975384,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.912387,1.774983,119.081804,yes,yes,2.425503,2.996834,Sometimes,no,2.017602,no,1.580263,0.531769,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.982997,1.78968,118.436166,yes,yes,1.469384,2.996444,Sometimes,no,2.067741,no,1.082236,0.150544,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.511445,1.852664,121.310257,yes,yes,2.906269,2.894142,Sometimes,no,2.435489,no,1.266739,0.36216,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.05331,1.872561,121.471077,yes,yes,2.808027,2.683061,Sometimes,no,2.640483,no,1.280191,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.906886,1.81955,120.775439,yes,yes,2.636719,2.806341,Sometimes,no,1.967591,no,1.525384,0.315595,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.147644,1.815514,120.337664,yes,yes,2.996717,2.791366,Sometimes,no,2.626309,no,1.194898,0.034897,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.001889,1.614075,100.245302,yes,yes,2.880483,1.703299,Sometimes,no,1.006378,no,1.076729,1.058007,no,Public_Transportation,Obesity_Type_II
+Male,24.002404,1.609418,100.078367,yes,yes,2.885693,1.685134,Sometimes,no,1.011849,no,0.503105,1.217929,no,Public_Transportation,Obesity_Type_II
+Male,30.71516,1.650189,101.141277,yes,yes,2.913452,2.269799,Sometimes,no,1,no,1.889937,0.378818,no,Public_Transportation,Obesity_Type_II
+Male,30.64243,1.653876,102.583895,yes,yes,2.919526,2.142328,Sometimes,no,1.175714,no,0.958555,0.636289,no,Public_Transportation,Obesity_Type_II
+Male,20.068432,1.657132,105.580491,yes,yes,2.724121,1.437959,Sometimes,no,1.590418,no,0.029603,1.122118,no,Public_Transportation,Obesity_Type_II
+Male,20.914366,1.644751,101.067988,yes,yes,2.801992,1.343117,Sometimes,no,1.128942,no,0.233987,0.81998,no,Public_Transportation,Obesity_Type_II
+Male,25.512048,1.660761,104.321463,yes,yes,2.748971,1.213431,Sometimes,no,1.448875,no,0.128548,1.239038,no,Public_Transportation,Obesity_Type_II
+Male,26.844812,1.69151,102.59518,yes,yes,2.680375,1.089048,Sometimes,no,1.366238,no,0.181324,1.041677,no,Public_Transportation,Obesity_Type_II
+Female,25.919241,1.611462,102.093223,yes,yes,3,3,Sometimes,no,1.023328,no,0.05093,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.584782,105.055597,yes,yes,3,3,Sometimes,no,1.197667,no,0,0.534769,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.233541,1.792378,137.859737,yes,yes,3,3,Sometimes,no,2.838893,no,1.990317,0.735868,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.147705,1.802257,149.935848,yes,yes,3,3,Sometimes,no,2.181811,no,1.995582,0.939665,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.65632,111.93301,yes,yes,3,3,Sometimes,no,2.774014,no,0,0.138418,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.600762,105.448264,yes,yes,3,3,Sometimes,no,1.031354,no,0,0.37465,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.902283,1.678658,104.954834,yes,yes,3,3,Sometimes,no,1.66616,no,0.210351,0.73421,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.540865,1.678201,109.900472,yes,yes,3,3,Sometimes,no,1.471664,no,0.143955,0.444532,Sometimes,Public_Transportation,Obesity_Type_III
+Female,22.39251,1.65563,121.205171,yes,yes,3,3,Sometimes,no,1.347559,no,0.462951,0.31794,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.305402,1.694952,133.76473,yes,yes,3,3,Sometimes,no,1.338241,no,1.92628,0.886135,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.622771,109.959714,yes,yes,3,3,Sometimes,no,2.679137,no,0,0.479348,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.583889,110.545378,yes,yes,3,3,Sometimes,no,2.578292,no,0,0.492394,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.334585,1.729045,131.529267,yes,yes,3,3,Sometimes,no,1.302193,no,1.742453,0.94232,Sometimes,Public_Transportation,Obesity_Type_III
+Female,22.978655,1.664622,114.465425,yes,yes,3,3,Sometimes,no,2.408442,no,0.194056,0.803141,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.01245,1.747773,127.902844,yes,yes,3,3,Sometimes,no,1.705218,no,0.390937,0.998646,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.056059,1.730113,152.094362,yes,yes,3,3,Sometimes,no,2.374958,no,0.750111,0.671458,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.771001,1.746652,133.800129,yes,yes,3,3,Sometimes,no,2.869234,no,1.465931,0.627886,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.265943,1.719365,114.511537,yes,yes,3,3,Sometimes,no,2.987406,no,0.38926,0.526776,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.632377,111.946655,yes,yes,3,3,Sometimes,no,2.737091,no,0,0.037078,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.642572,111.868169,yes,yes,3,3,Sometimes,no,2.623489,no,0,0.138563,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.96701,1.654875,104.758713,yes,yes,3,3,Sometimes,no,2.612758,no,0.24629,0.802136,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.627567,107.482662,yes,yes,3,3,Sometimes,no,2.687378,no,0,0.410651,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.590046,1.736276,130.927138,yes,yes,3,3,Sometimes,no,1.852692,no,1.46361,0.962336,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.497373,1.737056,132.527011,yes,yes,3,3,Sometimes,no,2.86559,no,0.973864,0.58345,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.991886,1.580968,102.003378,yes,yes,3,3,Sometimes,no,1.003563,no,0.008127,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.908431,1.607128,103.026858,yes,yes,3,3,Sometimes,no,1.077253,no,0.162083,0.824607,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.177882,1.821566,142.102468,yes,yes,3,3,Sometimes,no,2.714949,no,1.999773,0.813784,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.112503,1.82773,152.720545,yes,yes,3,3,Sometimes,no,2.154949,no,1.999897,0.957463,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.656504,111.993054,yes,yes,3,3,Sometimes,no,2.893117,no,0,0.01931,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.637725,111.208963,yes,yes,3,3,Sometimes,no,2.70914,no,0,0.110518,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.902283,1.669701,104.585783,yes,yes,3,3,Sometimes,no,1.570188,no,0.210351,0.881848,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.540865,1.668709,104.754958,yes,yes,3,3,Sometimes,no,1.412049,no,0.143955,0.753077,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.520992,1.668642,124.704781,yes,yes,3,3,Sometimes,no,1.15635,no,0.786828,0.366385,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.871153,1.690614,129.769141,yes,yes,3,3,Sometimes,no,1.154698,no,1.71836,0.959218,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.622418,110.676343,yes,yes,3,3,Sometimes,no,2.691584,no,0,0.425473,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.643332,110.824698,yes,yes,3,3,Sometimes,no,2.709654,no,0,0.25115,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.768834,1.733383,135.524857,yes,yes,3,3,Sometimes,no,1.485736,no,1.950374,0.869238,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.891491,1.748313,133.573787,yes,yes,3,3,Sometimes,no,2.874336,no,1.624981,0.825609,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.941943,1.812963,138.730619,yes,yes,3,3,Sometimes,no,2.641489,no,0.481555,0.735201,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.989016,1.80734,155.872093,yes,yes,3,3,Sometimes,no,2.417122,no,0.952725,0.573958,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.367481,1.745644,133.665587,yes,yes,3,3,Sometimes,no,2.900857,no,1.508897,0.625371,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.051107,1.753266,133.852432,yes,yes,3,3,Sometimes,no,2.95311,no,1.445148,0.67321,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.632193,111.886611,yes,yes,3,3,Sometimes,no,2.617988,no,0,0.156187,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.641098,111.818345,yes,yes,3,3,Sometimes,no,2.55057,no,0,0.20482,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.998646,1.640741,104.808542,yes,yes,3,3,Sometimes,no,2.653531,no,0.190061,0.692343,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.618573,104.928643,yes,yes,3,3,Sometimes,no,1.698767,no,0,0.54396,Sometimes,Public_Transportation,Obesity_Type_III
+Female,22.846357,1.757442,133.365094,yes,yes,3,3,Sometimes,no,2.84837,no,1.206059,0.766668,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.885655,1.763343,133.952675,yes,yes,3,3,Sometimes,no,2.835622,no,1.419473,0.816986,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.930376,1.610086,102.38745,yes,yes,3,3,Sometimes,no,1.050564,no,0.026033,0.539074,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.897815,1.664463,102.781971,yes,yes,3,3,Sometimes,no,1.068493,no,0.112122,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.602025,104.899348,yes,yes,3,3,Sometimes,no,2.613928,no,0,0.553093,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.968792,1.632896,104.988925,yes,yes,3,3,Sometimes,no,1.239833,no,0.162279,0.619235,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.335019,1.771043,137.044959,yes,yes,3,3,Sometimes,no,2.861533,no,1.70464,0.655571,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.700748,1.789555,137.767787,yes,yes,3,3,Sometimes,no,2.832659,no,1.505775,0.923005,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.222536,1.801746,141.166265,yes,yes,3,3,Sometimes,no,2.418488,no,1.99507,0.893514,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.3756,1.787195,151.975864,yes,yes,3,3,Sometimes,no,2.304716,no,0.82666,0.914492,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.644141,111.942544,yes,yes,3,3,Sometimes,no,2.763005,no,0,0.101867,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.643355,111.600553,yes,yes,3,3,Sometimes,no,2.652616,no,0,0.250502,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.623707,105.037463,yes,yes,3,3,Sometimes,no,2.553198,no,0,0.393838,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.610636,105.423532,yes,yes,3,3,Sometimes,no,2.180566,no,0,0.519905,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.943827,1.629491,104.839068,yes,yes,3,3,Sometimes,no,2.20979,no,0.114698,0.604422,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.291974,1.684768,104.821175,yes,yes,3,3,Sometimes,no,1.444379,no,0.224482,0.912187,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.653233,1.66494,110.92217,yes,yes,3,3,Sometimes,no,1.604075,no,0.029728,0.200122,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.783865,1.655646,110.21734,yes,yes,3,3,Sometimes,no,1.528258,no,0.01586,0.436068,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.17614,1.666194,124.805868,yes,yes,3,3,Sometimes,no,1.228838,no,0.792553,0.639561,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.207423,1.707508,121.864326,yes,yes,3,3,Sometimes,no,1.615548,no,1.376534,0.926052,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.633056,1.754174,133.783955,yes,yes,3,3,Sometimes,no,1.94595,no,1.493018,0.906611,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.009596,1.714193,131.866734,yes,yes,3,3,Sometimes,no,1.709556,no,1.592494,0.925843,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.62439,110.008636,yes,yes,3,3,Sometimes,no,2.507461,no,0,0.368472,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.638836,110.970479,yes,yes,3,3,Sometimes,no,2.644135,no,0,0.357581,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.621245,111.267334,yes,yes,3,3,Sometimes,no,2.605685,no,0,0.199227,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.954511,1.623514,109.980145,yes,yes,3,3,Sometimes,no,2.217348,no,0.001015,0.481031,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.001969,1.736215,132.145549,yes,yes,3,3,Sometimes,no,1.657541,no,1.672639,0.629285,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.772251,1.732951,132.904884,yes,yes,3,3,Sometimes,no,1.817899,no,1.577824,0.930836,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.76197,1.69135,114.480696,yes,yes,3,3,Sometimes,no,2.509535,no,0.334264,0.668172,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.449655,1.635062,113.277388,yes,yes,3,3,Sometimes,no,2.578038,no,0.165422,0.463196,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.378203,1.746061,128.261402,yes,yes,3,3,Sometimes,no,2.501638,no,1.546179,0.66488,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.725718,1.746529,129.363771,yes,yes,3,3,Sometimes,no,2.250711,no,0.64235,0.685129,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.344018,1.746516,144.302261,yes,yes,3,3,Sometimes,no,2.36651,no,1.247505,0.723366,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.322097,1.751118,149.291106,yes,yes,3,3,Sometimes,no,2.309409,no,1.68291,0.70558,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.262934,1.741014,132.57927,yes,yes,3,3,Sometimes,no,2.436261,no,1.464674,0.87092,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.566815,1.748533,133.94608,yes,yes,3,3,Sometimes,no,2.833566,no,1.413239,0.862724,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.475242,1.694726,112.776612,yes,yes,3,3,Sometimes,no,2.724099,no,0.336795,0.153462,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.612462,1.674515,112.879662,yes,yes,3,3,Sometimes,no,2.989389,no,0.36009,0.169016,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.631332,111.829957,yes,yes,3,3,Sometimes,no,2.55975,no,0,0.237307,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.641918,111.86899,yes,yes,3,3,Sometimes,no,2.635454,no,0,0.088309,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.640125,111.539494,yes,yes,3,3,Sometimes,no,2.625537,no,0,0.162494,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.649178,111.914361,yes,yes,3,3,Sometimes,no,2.884077,no,0,0.096576,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.96773,1.603404,105.031908,yes,yes,3,3,Sometimes,no,1.919473,no,0.027101,0.546137,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.989938,1.644199,105.036075,yes,yes,3,3,Sometimes,no,2.310076,no,0.07115,0.733085,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.628909,106.875927,yes,yes,3,3,Sometimes,no,2.686824,no,0,0.540812,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.617227,1.628019,108.265922,yes,yes,3,3,Sometimes,no,1.6213,no,0.090917,0.438216,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.030909,1.71818,133.466763,yes,yes,3,3,Sometimes,no,1.517215,no,1.486289,0.937492,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.951084,1.708581,131.274851,yes,yes,3,3,Sometimes,no,1.796956,no,1.684582,0.887388,Sometimes,Public_Transportation,Obesity_Type_III
+Female,22.980221,1.724983,132.94066,yes,yes,3,3,Sometimes,no,2.085553,no,1.271651,0.587932,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.426036,1.739991,133.485478,yes,yes,3,3,Sometimes,no,2.837797,no,1.343875,0.803156,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.999185,1.568543,102.000122,yes,yes,3,3,Sometimes,no,1.000544,no,0.001297,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.934757,1.579893,102.134646,yes,yes,3,3,Sometimes,no,1.005864,no,0.02006,0.695838,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.327723,1.782714,154.618446,yes,yes,3,3,Sometimes,no,2.319559,no,1.97073,0.768375,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.47219,1.793824,160.935351,yes,yes,3,3,Sometimes,no,2.069257,no,1.986646,0.947091,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.659557,111.999096,yes,yes,3,3,Sometimes,no,2.956548,no,0,0.010056,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.65782,111.95611,yes,yes,3,3,Sometimes,no,2.777557,no,0,0.051858,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.816445,1.684082,104.592372,yes,yes,3,3,Sometimes,no,1.295436,no,0.282063,0.929356,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.498965,1.68395,104.846817,yes,yes,3,3,Sometimes,no,1.241378,no,0.259427,0.852362,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.137495,1.716521,127.642324,yes,yes,3,3,Sometimes,no,1.313834,no,0.912334,0.707494,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.190733,1.680762,125.418548,yes,yes,3,3,Sometimes,no,1.124366,no,0.877067,0.552006,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.623303,110.81746,yes,yes,3,3,Sometimes,no,2.673754,no,0,0.33929,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.631856,110.804337,yes,yes,3,3,Sometimes,no,2.70485,no,0,0.243338,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.840654,1.747479,136.516648,yes,yes,3,3,Sometimes,no,1.607531,no,1.963379,0.858392,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.871667,1.782453,137.852618,yes,yes,3,3,Sometimes,no,2.748909,no,1.989171,0.832515,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.501721,1.809871,152.394739,yes,yes,3,3,Sometimes,no,2.351207,no,0.246831,0.91336,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.521294,1.803677,160.639405,yes,yes,3,3,Sometimes,no,2.404049,no,0.427905,0.639894,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.314593,1.745602,133.554686,yes,yes,3,3,Sometimes,no,2.923792,no,1.536555,0.62535,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.529746,1.751038,133.843033,yes,yes,3,3,Sometimes,no,2.955075,no,1.46046,0.655558,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.631547,111.588625,yes,yes,3,3,Sometimes,no,2.554007,no,0,0.252635,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.635905,111.571076,yes,yes,3,3,Sometimes,no,2.511399,no,0,0.258472,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.616975,104.846218,yes,yes,3,3,Sometimes,no,2.653563,no,0,0.55415,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.62788,104.92057,yes,yes,3,3,Sometimes,no,2.588107,no,0,0.465607,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.768153,1.76416,133.888629,yes,yes,3,3,Sometimes,no,2.32502,no,1.441791,0.918468,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.238416,1.763847,133.937873,yes,yes,3,3,Sometimes,no,2.836791,no,1.46782,0.890527,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.930376,1.608808,102.083964,yes,yes,3,3,Sometimes,no,1.019684,no,0.026033,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.919571,1.610488,102.174953,yes,yes,3,3,Sometimes,no,1.032834,no,0.04525,0.922749,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.918524,1.621231,104.986792,yes,yes,3,3,Sometimes,no,1.653049,no,0.139159,0.711331,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.565662,1.642392,104.988082,yes,yes,3,3,Sometimes,no,1.204055,no,0.094851,0.603736,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.744914,1.801983,138.034526,yes,yes,3,3,Sometimes,no,2.691591,no,1.168368,0.735372,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.704699,1.787614,137.858254,yes,yes,3,3,Sometimes,no,2.531456,no,1.980738,0.835771,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.089969,1.801478,151.417292,yes,yes,3,3,Sometimes,no,2.226081,no,1.881761,0.933964,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.120739,1.807576,152.567671,yes,yes,3,3,Sometimes,no,2.164718,no,1.999631,0.941336,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.650125,111.939671,yes,yes,3,3,Sometimes,no,2.770732,no,0,0.125235,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.652674,111.919155,yes,yes,3,3,Sometimes,no,2.814517,no,0,0.101598,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.986185,1.663632,105.122109,yes,yes,3,3,Sometimes,no,1.626467,no,0.010183,0.412806,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.982113,1.627818,105.428628,yes,yes,3,3,Sometimes,no,1.48075,no,0.098043,0.663492,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.748627,1.66877,104.774144,yes,yes,3,3,Sometimes,no,1.526416,no,0.180158,0.747909,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.959772,1.642098,104.966758,yes,yes,3,3,Sometimes,no,1.482002,no,0.204108,0.657221,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.653233,1.65757,109.931233,yes,yes,3,3,Sometimes,no,1.610472,no,0.029728,0.472343,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.783865,1.643111,109.910012,yes,yes,3,3,Sometimes,no,1.530992,no,0.01586,0.445495,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.412434,1.675562,121.639178,yes,yes,3,3,Sometimes,no,1.484938,no,0.74225,0.604692,Sometimes,Public_Transportation,Obesity_Type_III
+Female,22.77789,1.661415,120.748213,yes,yes,3,3,Sometimes,no,1.91039,no,0.217455,0.77582,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.140165,1.713133,133.735889,yes,yes,3,3,Sometimes,no,1.411365,no,1.564618,0.910683,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.413498,1.7199,133.886031,yes,yes,3,3,Sometimes,no,2.41752,no,1.556155,0.873936,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.622701,109.982692,yes,yes,3,3,Sometimes,no,2.688229,no,0,0.451377,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.622468,110.400847,yes,yes,3,3,Sometimes,no,2.695094,no,0,0.413474,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.618867,110.777391,yes,yes,3,3,Sometimes,no,2.618198,no,0,0.380695,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.600905,110.074946,yes,yes,3,3,Sometimes,no,2.555189,no,0,0.462973,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.391371,1.730636,131.902591,yes,yes,3,3,Sometimes,no,1.419136,no,1.700889,0.930888,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.051982,1.729719,131.877558,yes,yes,3,3,Sometimes,no,1.422483,no,1.708971,0.673009,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.455303,1.677672,114.470482,yes,yes,3,3,Sometimes,no,2.426094,no,0.294763,0.737226,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.69484,1.637524,113.90506,yes,yes,3,3,Sometimes,no,2.495961,no,0.189831,0.652289,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.601222,1.738717,128.114161,yes,yes,3,3,Sometimes,no,1.797041,no,1.427413,0.966181,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.81158,1.741193,128.763843,yes,yes,3,3,Sometimes,no,1.768111,no,0.616503,0.968151,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.993565,1.792833,152.43563,yes,yes,3,3,Sometimes,no,2.365188,no,1.256115,0.760091,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.074449,1.810427,152.217135,yes,yes,3,3,Sometimes,no,2.299156,no,1.699056,0.729722,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.904037,1.743589,133.281333,yes,yes,3,3,Sometimes,no,2.684819,no,1.46525,0.804491,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.783234,1.747962,133.936535,yes,yes,3,3,Sometimes,no,2.84174,no,1.429256,0.775378,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.291205,1.71146,113.372851,yes,yes,3,3,Sometimes,no,2.796894,no,0.382189,0.16779,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.311534,1.685482,113.451224,yes,yes,3,3,Sometimes,no,2.987718,no,0.387074,0.283804,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.639251,111.927001,yes,yes,3,3,Sometimes,no,2.675567,no,0,0.081929,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.654784,111.933152,yes,yes,3,3,Sometimes,no,2.770125,no,0,0.088236,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.641209,111.856492,yes,yes,3,3,Sometimes,no,2.621877,no,0,0.153669,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.643167,111.894229,yes,yes,3,3,Sometimes,no,2.72005,no,0,0.127443,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.966504,1.63073,104.790549,yes,yes,3,3,Sometimes,no,2.436097,no,0.129178,0.683736,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.950898,1.649867,104.791035,yes,yes,3,3,Sometimes,no,2.535629,no,0.152713,0.7702,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.613574,107.012256,yes,yes,3,3,Sometimes,no,2.678779,no,0,0.508848,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.627532,106.69053,yes,yes,3,3,Sometimes,no,2.569713,no,0,0.396972,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.848608,1.726606,131.76807,yes,yes,3,3,Sometimes,no,1.759352,no,1.469928,0.950438,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.801791,1.721476,131.042274,yes,yes,3,3,Sometimes,no,1.837184,no,1.525165,0.926443,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.712641,1.742901,132.8071,yes,yes,3,3,Sometimes,no,2.856795,no,1.046463,0.586163,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.063874,1.737313,133.166595,yes,yes,3,3,Sometimes,no,2.846259,no,1.295759,0.723154,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.95774,1.62414,102.233445,yes,yes,3,3,Sometimes,no,1.067716,no,0.030541,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.909353,1.644078,102.277765,yes,yes,3,3,Sometimes,no,1.029241,no,0.075776,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.954995,1.592529,102.874549,yes,yes,3,3,Sometimes,no,1.074412,no,0.006265,0.845633,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.908829,1.607734,102.305767,yes,yes,3,3,Sometimes,no,1.030501,no,0.06582,0.991473,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.297004,1.817271,141.917802,yes,yes,3,3,Sometimes,no,2.699675,no,1.520818,0.76399,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.301773,1.808765,140.292018,yes,yes,3,3,Sometimes,no,2.830247,no,1.783138,0.789064,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.872667,1.817231,152.371911,yes,yes,3,3,Sometimes,no,2.305521,no,1.956278,0.92376,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.13782,1.819728,151.278532,yes,yes,3,3,Sometimes,no,2.155926,no,1.999836,0.94603,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.656465,111.949972,yes,yes,3,3,Sometimes,no,2.784303,no,0,0.127775,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.648143,111.950113,yes,yes,3,3,Sometimes,no,2.786417,no,0,0.079673,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.622703,111.216192,yes,yes,3,3,Sometimes,no,2.707201,no,0,0.167272,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.640606,111.036881,yes,yes,3,3,Sometimes,no,2.709428,no,0,0.228486,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.42724,1.683502,104.759643,yes,yes,3,3,Sometimes,no,1.525127,no,0.220825,0.896105,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.795187,1.669039,104.593929,yes,yes,3,3,Sometimes,no,1.554925,no,0.209586,0.823069,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.493586,1.673665,104.704707,yes,yes,3,3,Sometimes,no,1.170515,no,0.264831,0.896842,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.524336,1.668931,104.768318,yes,yes,3,3,Sometimes,no,1.436616,no,0.167086,0.764717,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.908785,1.700996,126.490236,yes,yes,3,3,Sometimes,no,1.242832,no,0.530925,0.575969,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.781751,1.734092,125.117633,yes,yes,3,3,Sometimes,no,1.54249,no,0.6277,0.450479,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.289104,1.708291,130.986338,yes,yes,3,3,Sometimes,no,1.205548,no,1.723934,0.952352,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.210732,1.716497,130.871127,yes,yes,3,3,Sometimes,no,1.301657,no,1.718543,0.947884,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.62495,111.00492,yes,yes,3,3,Sometimes,no,2.704315,no,0,0.322666,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.594776,110.640929,yes,yes,3,3,Sometimes,no,2.639816,no,0,0.452236,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.626503,110.818757,yes,yes,3,3,Sometimes,no,2.708927,no,0,0.278962,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.624576,110.803117,yes,yes,3,3,Sometimes,no,2.704827,no,0,0.269577,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.656907,1.729099,134.842656,yes,yes,3,3,Sometimes,no,1.3954,no,1.931173,0.878258,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.760734,1.73581,135.346677,yes,yes,3,3,Sometimes,no,2.611654,no,1.618512,0.868788,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.028989,1.748524,133.878843,yes,yes,3,3,Sometimes,no,2.868167,no,1.48372,0.83509,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.282238,1.748951,133.662583,yes,yes,3,3,Sometimes,no,2.247979,no,1.609938,0.849236,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.388049,1.777971,137.785027,yes,yes,3,3,Sometimes,no,2.702789,no,1.606109,0.731213,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.022206,1.73995,135.693381,yes,yes,3,3,Sometimes,no,1.663213,no,1.094035,0.786665,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.102241,1.816868,153.959945,yes,yes,3,3,Sometimes,no,2.414739,no,1.917014,0.927982,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.291969,1.8002,155.242672,yes,yes,3,3,Sometimes,no,2.351193,no,1.051889,0.686491,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.530998,1.74647,133.644711,yes,yes,3,3,Sometimes,no,2.896088,no,1.612741,0.77319,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.976968,1.759091,133.903612,yes,yes,3,3,Sometimes,no,2.862408,no,1.456933,0.742423,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.924956,1.752531,133.618706,yes,yes,3,3,Sometimes,no,2.887659,no,1.480919,0.779641,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.28253,1.761773,133.903469,yes,yes,3,3,Sometimes,no,2.893062,no,1.408177,0.807457,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.633195,111.883747,yes,yes,3,3,Sometimes,no,2.619517,no,0,0.140368,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.633945,111.9307,yes,yes,3,3,Sometimes,no,2.682804,no,0,0.15171,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.641601,111.830924,yes,yes,3,3,Sometimes,no,2.552388,no,0,0.196288,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.641132,111.841706,yes,yes,3,3,Sometimes,no,2.617401,no,0,0.200379,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.999174,1.638218,104.810024,yes,yes,3,3,Sometimes,no,2.654636,no,0.069238,0.629578,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.999942,1.627483,104.881994,yes,yes,3,3,Sometimes,no,2.569364,no,0.159255,0.419446,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.610225,104.936381,yes,yes,3,3,Sometimes,no,1.322004,no,0,0.539876,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.61739,105.013901,yes,yes,3,3,Sometimes,no,1.292479,no,0,0.541309,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.365041,1.744319,133.45249,yes,yes,3,3,Sometimes,no,2.839069,no,1.231031,0.792496,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.421726,1.755467,133.478611,yes,yes,3,3,Sometimes,no,2.843782,no,1.302507,0.773807,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.826782,1.746416,133.747012,yes,yes,3,3,Sometimes,no,2.858389,no,1.444382,0.713823,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.94093,1.746411,133.676663,yes,yes,3,3,Sometimes,no,2.864933,no,1.501647,0.786846,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.921678,1.611452,102.363149,yes,yes,3,3,Sometimes,no,1.031701,no,0.03465,0.912345,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.940153,1.596813,102.320437,yes,yes,3,3,Sometimes,no,1.000536,no,0.005939,0.566353,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.999636,1.610126,102.686908,yes,yes,3,3,Sometimes,no,1.020313,no,0.108386,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.907833,1.623113,102.555691,yes,yes,3,3,Sometimes,no,1.063422,no,0.051097,0.543118,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.991194,1.618348,104.94582,yes,yes,3,3,Sometimes,no,2.45126,no,0.043689,0.670402,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.993154,1.609401,104.854928,yes,yes,3,3,Sometimes,no,2.613504,no,0.067985,0.778632,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.988668,1.621671,105.313967,yes,yes,3,3,Sometimes,no,1.042989,no,0.097114,0.42954,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.976209,1.614484,104.999403,yes,yes,3,3,Sometimes,no,1.237557,no,0.083675,0.543761,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.743587,1.789143,138.202869,yes,yes,3,3,Sometimes,no,2.83377,no,0.806422,0.672513,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.323767,1.774207,138.143162,yes,yes,3,3,Sometimes,no,2.816052,no,1.571865,0.688058,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.394047,1.792933,137.832414,yes,yes,3,3,Sometimes,no,2.682909,no,1.318743,0.900497,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.838323,1.758959,137.79299,yes,yes,3,3,Sometimes,no,2.640539,no,1.879818,0.885993,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.20634,1.807406,141.799429,yes,yes,3,3,Sometimes,no,2.472903,no,1.998047,0.840911,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.532826,1.75091,142.545183,yes,yes,3,3,Sometimes,no,2.372058,no,1.324805,0.870795,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.438478,1.805803,153.149491,yes,yes,3,3,Sometimes,no,2.387991,no,0.850715,0.656491,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.796266,1.796538,152.473675,yes,yes,3,3,Sometimes,no,2.322003,no,0.886602,0.843283,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.634894,111.946321,yes,yes,3,3,Sometimes,no,2.737353,no,0,0.076094,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.639524,111.945588,yes,yes,3,3,Sometimes,no,2.739351,no,0,0.064769,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.643017,111.720238,yes,yes,3,3,Sometimes,no,2.632498,no,0,0.140792,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.641849,111.682693,yes,yes,3,3,Sometimes,no,2.632253,no,0,0.244205,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.621167,104.947703,yes,yes,3,3,Sometimes,no,2.57721,no,0,0.402075,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.992898,1.638075,105.036522,yes,yes,3,3,Sometimes,no,2.419153,no,0.019404,0.597626,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.60937,105.407313,yes,yes,3,3,Sometimes,no,2.609052,no,0,0.54859,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.608283,105.359688,yes,yes,3,3,Sometimes,no,2.476002,no,0,0.546345,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.951737,1.629442,104.835346,yes,yes,3,3,Sometimes,no,2.225139,no,0.035928,0.565315,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.982261,1.629225,104.838425,yes,yes,3,3,Sometimes,no,2.556068,no,0.01682,0.58284,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.470652,1.680218,104.807284,yes,yes,3,3,Sometimes,no,1.423073,no,0.197993,0.763359,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.289428,1.686033,104.772164,yes,yes,3,3,Sometimes,no,1.299194,no,0.234303,0.946888,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.696073,1.662978,110.930509,yes,yes,3,3,Sometimes,no,1.679489,no,0.017804,0.21523,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.561868,1.675185,110.621723,yes,yes,3,3,Sometimes,no,1.49583,no,0.109327,0.384129,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.834018,1.62456,110.10589,yes,yes,3,3,Sometimes,no,1.81786,no,0.011161,0.447224,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.895546,1.626179,110.074019,yes,yes,3,3,Sometimes,no,1.967707,no,0.01437,0.434073,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.035557,1.682594,127.427458,yes,yes,3,3,Sometimes,no,1.441289,no,1.425712,0.662277,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.634286,1.669354,126.088301,yes,yes,3,3,Sometimes,no,1.144539,no,0.922014,0.899673,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.700876,1.68838,121.889803,yes,yes,3,3,Sometimes,no,1.353167,no,1.093679,0.534553,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.741442,1.694439,122.813033,yes,yes,3,3,Sometimes,no,1.409444,no,0.933595,0.840393,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.695892,1.755476,133.870501,yes,yes,3,3,Sometimes,no,1.959287,no,1.433151,0.911335,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.635977,1.748106,133.259033,yes,yes,3,3,Sometimes,no,1.931163,no,1.562213,0.926565,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.232659,1.719913,131.567481,yes,yes,3,3,Sometimes,no,1.651462,no,1.655993,0.939982,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.008297,1.723587,131.929712,yes,yes,3,3,Sometimes,no,1.683448,no,1.645532,0.858059,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.962949,1.623812,109.996742,yes,yes,3,3,Sometimes,no,2.395387,no,0.000272,0.461532,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.624099,109.978402,yes,yes,3,3,Sometimes,no,2.523793,no,0,0.383953,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.632983,111.157811,yes,yes,3,3,Sometimes,no,2.638896,no,0,0.224559,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.640745,110.919646,yes,yes,3,3,Sometimes,no,2.666178,no,0,0.276907,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.629727,111.275646,yes,yes,3,3,Sometimes,no,2.495851,no,0,0.218645,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.624134,111.531208,yes,yes,3,3,Sometimes,no,2.609188,no,0,0.17403,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.964788,1.623938,109.984263,yes,yes,3,3,Sometimes,no,2.471721,no,0.000096,0.433463,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.994949,1.593321,110.168166,yes,yes,3,3,Sometimes,no,2.447306,no,0.000454,0.486558,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.952737,1.730333,132.116491,yes,yes,3,3,Sometimes,no,1.674061,no,1.683497,0.780199,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.978166,1.721057,132.054793,yes,yes,3,3,Sometimes,no,1.678791,no,1.68249,0.818871,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.67447,1.71978,132.262558,yes,yes,3,3,Sometimes,no,1.777805,no,1.584716,0.927341,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.568951,1.699315,133.10761,yes,yes,3,3,Sometimes,no,1.733306,no,1.773304,0.921136,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.647935,1.681394,114.479459,yes,yes,3,3,Sometimes,no,2.435978,no,0.232742,0.692608,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.196367,1.697421,114.482386,yes,yes,3,3,Sometimes,no,2.909675,no,0.360908,0.573887,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.284833,1.650726,113.774198,yes,yes,3,3,Sometimes,no,2.75137,no,0.324913,0.516764,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.693108,1.667383,112.982549,yes,yes,3,3,Sometimes,no,2.887909,no,0.312923,0.210997,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.862264,1.746277,128.705761,yes,yes,3,3,Sometimes,no,2.411582,no,0.985287,0.675076,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.423482,1.735461,126.798173,yes,yes,3,3,Sometimes,no,2.38639,no,1.544632,0.900067,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.394082,1.747714,128.148108,yes,yes,3,3,Sometimes,no,2.232601,no,0.554323,0.836151,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.217015,1.71582,129.466541,yes,yes,3,3,Sometimes,no,1.755907,no,1.48809,0.857718,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.330178,1.747987,147.296186,yes,yes,3,3,Sometimes,no,2.336349,no,1.4164,0.711724,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.528936,1.817917,142.559161,yes,yes,3,3,Sometimes,no,2.562002,no,1.976427,0.740331,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.364339,1.80835,150.51648,yes,yes,3,3,Sometimes,no,2.305574,no,1.734424,0.863043,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.131526,1.739457,150.37757,yes,yes,3,3,Sometimes,no,2.319912,no,1.582675,0.680746,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.012872,1.742062,133.779919,yes,yes,3,3,Sometimes,no,2.70196,no,1.465909,0.813235,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.469086,1.741925,133.017105,yes,yes,3,3,Sometimes,no,2.474518,no,1.560261,0.662489,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.572114,1.751067,133.845064,yes,yes,3,3,Sometimes,no,2.748569,no,1.427037,0.902825,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.680123,1.749118,133.955091,yes,yes,3,3,Sometimes,no,2.827773,no,1.412357,0.901071,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.469756,1.663341,113.077187,yes,yes,3,3,Sometimes,no,2.632224,no,0.300964,0.26956,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.12791,1.668537,112.555456,yes,yes,3,3,Sometimes,no,2.868679,no,0.115369,0.028583,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.986368,1.668951,112.249699,yes,yes,3,3,Sometimes,no,2.930137,no,0.043101,0.138629,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.951979,1.661712,112.098616,yes,yes,3,3,Sometimes,no,2.961899,no,0.259424,0.080128,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.633442,111.821817,yes,yes,3,3,Sometimes,no,2.550672,no,0,0.224655,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.63302,111.863186,yes,yes,3,3,Sometimes,no,2.584305,no,0,0.232108,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.633887,111.878132,yes,yes,3,3,Sometimes,no,2.621976,no,0,0.123861,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.643421,111.939983,yes,yes,3,3,Sometimes,no,2.722276,no,0,0.091711,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.640535,111.555967,yes,yes,3,3,Sometimes,no,2.634342,no,0,0.178301,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.626483,111.357062,yes,yes,3,3,Sometimes,no,2.61939,no,0,0.171034,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.64599,111.922491,yes,yes,3,3,Sometimes,no,2.78678,no,0,0.09776,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.643892,111.884535,yes,yes,3,3,Sometimes,no,2.768141,no,0,0.094213,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.97731,1.617817,104.950776,yes,yes,3,3,Sometimes,no,1.71659,no,0.001272,0.545993,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.955014,1.626449,104.879602,yes,yes,3,3,Sometimes,no,2.094901,no,0.07089,0.599441,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.996716,1.62658,105.037203,yes,yes,3,3,Sometimes,no,2.347322,no,0.008013,0.503896,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.992348,1.606474,104.954291,yes,yes,3,3,Sometimes,no,2.331123,no,0.063383,0.561661,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.974446,1.628855,108.090006,yes,yes,3,3,Sometimes,no,1.757105,no,0.085119,0.465444,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.777565,1.628205,107.378702,yes,yes,3,3,Sometimes,no,2.506631,no,0.025787,0.484165,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.722004,1.62847,107.218949,yes,yes,3,3,Sometimes,no,2.48707,no,0.067329,0.455823,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.765628,1.627839,108.10736,yes,yes,3,3,Sometimes,no,2.320068,no,0.045246,0.413106,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.016849,1.724268,133.033523,yes,yes,3,3,Sometimes,no,1.650612,no,1.537639,0.912457,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.682367,1.732383,133.043941,yes,yes,3,3,Sometimes,no,1.610768,no,1.510398,0.931455,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.285965,1.72692,131.335786,yes,yes,3,3,Sometimes,no,1.796267,no,1.728332,0.897924,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.976842,1.71073,131.408528,yes,yes,3,3,Sometimes,no,1.728139,no,1.676269,0.906247,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.982942,1.748584,133.742943,yes,yes,3,3,Sometimes,no,2.00513,no,1.34139,0.59927,Sometimes,Public_Transportation,Obesity_Type_III
+Female,22.524036,1.752206,133.689352,yes,yes,3,3,Sometimes,no,2.054193,no,1.414209,0.646288,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.361936,1.73945,133.346641,yes,yes,3,3,Sometimes,no,2.852339,no,1.139107,0.586035,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.664709,1.738836,133.472641,yes,yes,3,3,Sometimes,no,2.863513,no,1.026452,0.714137,Sometimes,Public_Transportation,Obesity_Type_III
diff --git a/data_science/shashank_week9_xgboost/notebooks/pre_processing.ipynb b/data_science/shashank_week9_xgboost/notebooks/pre_processing.ipynb
new file mode 100644
index 0000000..6961c9c
--- /dev/null
+++ b/data_science/shashank_week9_xgboost/notebooks/pre_processing.ipynb
@@ -0,0 +1,1122 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rUGML3Px2GYN",
+        "outputId": "32835967-5b36-4d8e-f75b-0eff5fd3b141"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting ucimlrepo\n",
+            "  Downloading ucimlrepo-0.0.7-py3-none-any.whl.metadata (5.5 kB)\n",
+            "Requirement already satisfied: pandas>=1.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ucimlrepo) (2.2.3)\n",
+            "Requirement already satisfied: certifi>=2020.12.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ucimlrepo) (2025.7.14)\n",
+            "Requirement already satisfied: numpy>=1.26.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas>=1.0.0->ucimlrepo) (2.1.3)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas>=1.0.0->ucimlrepo) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas>=1.0.0->ucimlrepo) (2024.1)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas>=1.0.0->ucimlrepo) (2025.2)\n",
+            "Requirement already satisfied: six>=1.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas>=1.0.0->ucimlrepo) (1.17.0)\n",
+            "Downloading ucimlrepo-0.0.7-py3-none-any.whl (8.0 kB)\n",
+            "Installing collected packages: ucimlrepo\n",
+            "Successfully installed ucimlrepo-0.0.7\n",
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "pip install ucimlrepo"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RDdnY04h5xpK",
+        "outputId": "98be51ad-02dc-48b0-da14-1f8f3a6f8386"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'uci_id': 544, 'name': 'Estimation of Obesity Levels Based On Eating Habits and Physical Condition ', 'repository_url': 'https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition', 'data_url': 'https://archive.ics.uci.edu/static/public/544/data.csv', 'abstract': 'This dataset include data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition. ', 'area': 'Health and Medicine', 'tasks': ['Classification', 'Regression', 'Clustering'], 'characteristics': ['Multivariate'], 'num_instances': 2111, 'num_features': 16, 'feature_types': ['Integer'], 'demographics': ['Gender', 'Age'], 'target_col': ['NObeyesdad'], 'index_col': None, 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 2019, 'last_updated': 'Tue Sep 10 2024', 'dataset_doi': '10.24432/C5H31Z', 'creators': [], 'intro_paper': {'ID': 358, 'type': 'NATIVE', 'title': 'Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico', 'authors': 'Fabio Mendoza Palechor, Alexis De la Hoz Manotas', 'venue': 'Data in Brief', 'year': 2019, 'journal': None, 'DOI': '10.1016/j.dib.2019.104344', 'URL': 'https://www.semanticscholar.org/paper/35b40bacd2ffa9370885b7a3004d88995fd1d011', 'sha': None, 'corpus': None, 'arxiv': None, 'mag': None, 'acl': None, 'pmid': None, 'pmcid': None}, 'additional_info': {'summary': 'This dataset include data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition. The data contains 17 attributes and 2111 records, the records are labeled with the class variable NObesity (Obesity Level), that allows classification of the data using the values of Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II and Obesity Type III. 77% of the data was generated synthetically using the Weka tool and the SMOTE filter, 23% of the data was collected directly from users through a web platform.', 'purpose': None, 'funded_by': None, 'instances_represent': None, 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': 'Read the article (https://doi.org/10.1016/j.dib.2019.104344) to see the description of the attributes.', 'citation': None}}\n",
+            "                              name     role         type demographic  \\\n",
+            "0                           Gender  Feature  Categorical      Gender   \n",
+            "1                              Age  Feature   Continuous         Age   \n",
+            "2                           Height  Feature   Continuous        None   \n",
+            "3                           Weight  Feature   Continuous        None   \n",
+            "4   family_history_with_overweight  Feature       Binary        None   \n",
+            "5                             FAVC  Feature       Binary        None   \n",
+            "6                             FCVC  Feature      Integer        None   \n",
+            "7                              NCP  Feature   Continuous        None   \n",
+            "8                             CAEC  Feature  Categorical        None   \n",
+            "9                            SMOKE  Feature       Binary        None   \n",
+            "10                            CH2O  Feature   Continuous        None   \n",
+            "11                             SCC  Feature       Binary        None   \n",
+            "12                             FAF  Feature   Continuous        None   \n",
+            "13                             TUE  Feature      Integer        None   \n",
+            "14                            CALC  Feature  Categorical        None   \n",
+            "15                          MTRANS  Feature  Categorical        None   \n",
+            "16                      NObeyesdad   Target  Categorical        None   \n",
+            "\n",
+            "                                          description units missing_values  \n",
+            "0                                                None  None             no  \n",
+            "1                                                None  None             no  \n",
+            "2                                                None  None             no  \n",
+            "3                                                None  None             no  \n",
+            "4   Has a family member suffered or suffers from o...  None             no  \n",
+            "5            Do you eat high caloric food frequently?  None             no  \n",
+            "6        Do you usually eat vegetables in your meals?  None             no  \n",
+            "7              How many main meals do you have daily?  None             no  \n",
+            "8                  Do you eat any food between meals?  None             no  \n",
+            "9                                       Do you smoke?  None             no  \n",
+            "10                 How much water do you drink daily?  None             no  \n",
+            "11         Do you monitor the calories you eat daily?  None             no  \n",
+            "12           How often do you have physical activity?  None             no  \n",
+            "13  How much time do you use technological devices...  None             no  \n",
+            "14                    How often do you drink alcohol?  None             no  \n",
+            "15           Which transportation do you usually use?  None             no  \n",
+            "16                                      Obesity level  None             no  \n"
+          ]
+        }
+      ],
+      "source": [
+        "from ucimlrepo import fetch_ucirepo\n",
+        "\n",
+        "# fetch dataset\n",
+        "estimation_of_obesity_levels_based_on_eating_habits_and_physical_condition = fetch_ucirepo(id=544)\n",
+        "\n",
+        "# data (as pandas dataframes)\n",
+        "X = estimation_of_obesity_levels_based_on_eating_habits_and_physical_condition.data.features\n",
+        "y = estimation_of_obesity_levels_based_on_eating_habits_and_physical_condition.data.targets\n",
+        "\n",
+        "# metadata\n",
+        "print(estimation_of_obesity_levels_based_on_eating_habits_and_physical_condition.metadata)\n",
+        "\n",
+        "# variable information\n",
+        "print(estimation_of_obesity_levels_based_on_eating_habits_and_physical_condition.variables)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ha3DtFGa5_i_",
+        "outputId": "aa0f1c6b-5a86-4613-c19d-fc9063e6ef40"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "   Gender   Age  Height  Weight family_history_with_overweight FAVC  FCVC  \\\n",
+            "0  Female  21.0    1.62    64.0                            yes   no   2.0   \n",
+            "1  Female  21.0    1.52    56.0                            yes   no   3.0   \n",
+            "2    Male  23.0    1.80    77.0                            yes   no   2.0   \n",
+            "3    Male  27.0    1.80    87.0                             no   no   3.0   \n",
+            "4    Male  22.0    1.78    89.8                             no   no   2.0   \n",
+            "\n",
+            "   NCP       CAEC SMOKE  CH2O  SCC  FAF  TUE        CALC  \\\n",
+            "0  3.0  Sometimes    no   2.0   no  0.0  1.0          no   \n",
+            "1  3.0  Sometimes   yes   3.0  yes  3.0  0.0   Sometimes   \n",
+            "2  3.0  Sometimes    no   2.0   no  2.0  1.0  Frequently   \n",
+            "3  3.0  Sometimes    no   2.0   no  2.0  0.0  Frequently   \n",
+            "4  1.0  Sometimes    no   2.0   no  0.0  0.0   Sometimes   \n",
+            "\n",
+            "                  MTRANS           NObeyesdad  \n",
+            "0  Public_Transportation        Normal_Weight  \n",
+            "1  Public_Transportation        Normal_Weight  \n",
+            "2  Public_Transportation        Normal_Weight  \n",
+            "3                Walking   Overweight_Level_I  \n",
+            "4  Public_Transportation  Overweight_Level_II  \n"
+          ]
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "df1 = pd.concat([X, y], axis=1)\n",
+        "print(df1.head())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_jdNJMpS6viY",
+        "outputId": "9e7d7790-a7b9-4503-f7e2-285fc3569de2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Youngest age: 14.0\n",
+            "Oldest age: 61.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Youngest age:\", X['Age'].min())\n",
+        "print(\"Oldest age:\", X['Age'].max())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yUAyOSSp6-I6",
+        "outputId": "53eb8640-3d58-44a6-cccf-e3e665fd1c0f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Number of people under 18: 113\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Number of people under 18:\", (X['Age'] < 18).sum())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YOgUn_zgMN6G"
+      },
+      "source": [
+        "***Importing Data set \"Obesity Risk Dataset\"***"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xbQfwedkL2SA",
+        "outputId": "89f51e4a-377b-49c3-dafd-1ab8630761f6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting kagglehub\n",
+            "  Downloading kagglehub-0.3.13-py3-none-any.whl.metadata (38 kB)\n",
+            "Requirement already satisfied: pandas in c:\\users\\user\\anaconda3\\lib\\site-packages (2.2.3)\n",
+            "Requirement already satisfied: packaging in c:\\users\\user\\anaconda3\\lib\\site-packages (from kagglehub) (24.2)\n",
+            "Requirement already satisfied: pyyaml in c:\\users\\user\\anaconda3\\lib\\site-packages (from kagglehub) (6.0.2)\n",
+            "Requirement already satisfied: requests in c:\\users\\user\\anaconda3\\lib\\site-packages (from kagglehub) (2.32.3)\n",
+            "Requirement already satisfied: tqdm in c:\\users\\user\\anaconda3\\lib\\site-packages (from kagglehub) (4.67.1)\n",
+            "Requirement already satisfied: numpy>=1.26.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (2.1.3)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (2024.1)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (2025.2)\n",
+            "Requirement already satisfied: six>=1.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->kagglehub) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->kagglehub) (3.7)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->kagglehub) (2.3.0)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->kagglehub) (2025.7.14)\n",
+            "Requirement already satisfied: colorama in c:\\users\\user\\anaconda3\\lib\\site-packages (from tqdm->kagglehub) (0.4.6)\n",
+            "Downloading kagglehub-0.3.13-py3-none-any.whl (68 kB)\n",
+            "Installing collected packages: kagglehub\n",
+            "Successfully installed kagglehub-0.3.13\n",
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "pip install kagglehub pandas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0Byr20YxL6mS",
+        "outputId": "b790a46a-1ad9-4d0d-e1e5-6c367a37d022"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/datasets/download/jpkochar/obesity-risk-dataset?dataset_version_number=1...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 524k/524k [00:00<00:00, 1.62MB/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Extracting files...\n",
+            "Dataset downloaded to: C:\\Users\\user\\.cache\\kagglehub\\datasets\\jpkochar\\obesity-risk-dataset\\versions\\1\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "import kagglehub\n",
+        "\n",
+        "# Download latest version of the dataset\n",
+        "path = kagglehub.dataset_download(\"jpkochar/obesity-risk-dataset\")\n",
+        "\n",
+        "print(\"Dataset downloaded to:\", path)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3oJOwPmCMI8z",
+        "outputId": "b504d5d0-2ba6-4f90-fc5e-0b5b655a42ab"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "First 5 rows:\n",
+            "   id  Gender        Age    Height      Weight  \\\n",
+            "0   0    Male  24.443011  1.699998   81.669950   \n",
+            "1   1  Female  18.000000  1.560000   57.000000   \n",
+            "2   2  Female  18.000000  1.711460   50.165754   \n",
+            "3   3  Female  20.952737  1.710730  131.274851   \n",
+            "4   4    Male  31.641081  1.914186   93.798055   \n",
+            "\n",
+            "   family_history_with_overweight  FAVC      FCVC       NCP        CAEC  \\\n",
+            "0                               1     1  2.000000  2.983297   Sometimes   \n",
+            "1                               1     1  2.000000  3.000000  Frequently   \n",
+            "2                               1     1  1.880534  1.411685   Sometimes   \n",
+            "3                               1     1  3.000000  3.000000   Sometimes   \n",
+            "4                               1     1  2.679664  1.971472   Sometimes   \n",
+            "\n",
+            "   SMOKE      CH2O  SCC       FAF       TUE       CALC                 MTRANS  \\\n",
+            "0      0  2.763573    0  0.000000  0.976473  Sometimes  Public_Transportation   \n",
+            "1      0  2.000000    0  1.000000  1.000000          0             Automobile   \n",
+            "2      0  1.910378    0  0.866045  1.673584          0  Public_Transportation   \n",
+            "3      0  1.674061    0  1.467863  0.780199  Sometimes  Public_Transportation   \n",
+            "4      0  1.979848    0  1.967973  0.931721  Sometimes  Public_Transportation   \n",
+            "\n",
+            "               0be1dad  \n",
+            "0  Overweight_Level_II  \n",
+            "1         0rmal_Weight  \n",
+            "2  Insufficient_Weight  \n",
+            "3     Obesity_Type_III  \n",
+            "4  Overweight_Level_II  \n"
+          ]
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "import os\n",
+        "\n",
+        "# Find the CSV file in the downloaded folder\n",
+        "csv_files = [f for f in os.listdir(path) if f.endswith(\".csv\")]\n",
+        "if not csv_files:\n",
+        "    raise FileNotFoundError(\"No CSV file found in the dataset folder.\")\n",
+        "\n",
+        "csv_path = os.path.join(path, csv_files[0])\n",
+        "\n",
+        "# Load into pandas\n",
+        "ObesityRiskDatasetdf = pd.read_csv(csv_path)\n",
+        "\n",
+        "print(\"First 5 rows:\")\n",
+        "print(ObesityRiskDatasetdf.head())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "j_K_oqwrM5wk",
+        "outputId": "51ed8a14-a97d-4f68-da71-9ba5444f15d3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "First 5 rows:\n",
+            "   id  Gender        Age    Height      Weight  \\\n",
+            "0   0    Male  24.443011  1.699998   81.669950   \n",
+            "1   1  Female  18.000000  1.560000   57.000000   \n",
+            "2   2  Female  18.000000  1.711460   50.165754   \n",
+            "3   3  Female  20.952737  1.710730  131.274851   \n",
+            "4   4    Male  31.641081  1.914186   93.798055   \n",
+            "\n",
+            "   family_history_with_overweight  FAVC      FCVC       NCP        CAEC  \\\n",
+            "0                               1     1  2.000000  2.983297   Sometimes   \n",
+            "1                               1     1  2.000000  3.000000  Frequently   \n",
+            "2                               1     1  1.880534  1.411685   Sometimes   \n",
+            "3                               1     1  3.000000  3.000000   Sometimes   \n",
+            "4                               1     1  2.679664  1.971472   Sometimes   \n",
+            "\n",
+            "   SMOKE      CH2O  SCC       FAF       TUE       CALC                 MTRANS  \\\n",
+            "0      0  2.763573    0  0.000000  0.976473  Sometimes  Public_Transportation   \n",
+            "1      0  2.000000    0  1.000000  1.000000          0             Automobile   \n",
+            "2      0  1.910378    0  0.866045  1.673584          0  Public_Transportation   \n",
+            "3      0  1.674061    0  1.467863  0.780199  Sometimes  Public_Transportation   \n",
+            "4      0  1.979848    0  1.967973  0.931721  Sometimes  Public_Transportation   \n",
+            "\n",
+            "               0be1dad  \n",
+            "0  Overweight_Level_II  \n",
+            "1         0rmal_Weight  \n",
+            "2  Insufficient_Weight  \n",
+            "3     Obesity_Type_III  \n",
+            "4  Overweight_Level_II  \n"
+          ]
+        }
+      ],
+      "source": [
+        "df2 = pd.read_csv(csv_path)\n",
+        "\n",
+        "print(\"First 5 rows:\")\n",
+        "print(df2.head())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9DvxDRPGO3V-",
+        "outputId": "3a122466-dd5b-42b1-f140-0aa12585aadc"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Index(['id', 'Gender', 'Age', 'Height', 'Weight',\n",
+            "       'family_history_with_overweight', 'FAVC', 'FCVC', 'NCP', 'CAEC',\n",
+            "       'SMOKE', 'CH2O', 'SCC', 'FAF', 'TUE', 'CALC', 'MTRANS', '0be1dad'],\n",
+            "      dtype='object')\n",
+            "Youngest age: 14.0\n",
+            "Oldest age: 61.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Check the exact column names first\n",
+        "print(df2.columns)\n",
+        "\n",
+        "# Then:\n",
+        "print(\"Youngest age:\", df2['Age'].min())\n",
+        "print(\"Oldest age:\", df2['Age'].max())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "3kTfAKSoPbk_"
+      },
+      "outputs": [],
+      "source": [
+        "df1.columns = df1.columns.str.strip().str.lower()\n",
+        "df2.columns = df2.columns.str.strip().str.lower()\n",
+        "\n",
+        "df1_filtered = df1[(df1['age'] >= 14) & (df1['age'] <= 18)]\n",
+        "df2_filtered = df2[(df2['age'] >= 14) & (df2['age'] <= 18)]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "724TfjRZPmQV",
+        "outputId": "db021d8a-5d39-4733-9812-a0deb190362a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Rows in df1_filtered: 241\n",
+            "Rows in df2_filtered: 2972\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Rows in df1_filtered:\", df1_filtered.shape[0])\n",
+        "print(\"Rows in df2_filtered:\", df2_filtered.shape[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "LXe2c1ZktqlK"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-wdHvbfgtq1x",
+        "outputId": "eed67e7d-9be6-4fb1-fb61-8f1d6f520881"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "First 5 rows Df1:\n",
+            "     gender   age  height  weight family_history_with_overweight favc  fcvc  \\\n",
+            "58   Female  17.0    1.65    67.0                            yes  yes   3.0   \n",
+            "112  Female  18.0    1.56    51.0                            yes  yes   2.0   \n",
+            "115  Female  17.0    1.75    57.0                            yes  yes   3.0   \n",
+            "116  Female  15.0    1.65    86.0                            yes  yes   3.0   \n",
+            "117  Female  17.0    1.70    85.0                            yes   no   2.0   \n",
+            "\n",
+            "     ncp        caec smoke  ch2o scc  faf  tue       calc  \\\n",
+            "58   1.0   Sometimes    no   2.0  no  1.0  1.0         no   \n",
+            "112  4.0  Frequently    no   2.0  no  1.0  0.0  Sometimes   \n",
+            "115  3.0  Frequently    no   2.0  no  0.0  1.0         no   \n",
+            "116  3.0   Sometimes    no   1.0  no  3.0  2.0         no   \n",
+            "117  3.0  Frequently    no   2.0  no  1.0  1.0         no   \n",
+            "\n",
+            "                    mtrans           nobeyesdad  \n",
+            "58                 Walking        Normal_Weight  \n",
+            "112  Public_Transportation        Normal_Weight  \n",
+            "115  Public_Transportation        Normal_Weight  \n",
+            "116                Walking       Obesity_Type_I  \n",
+            "117  Public_Transportation  Overweight_Level_II  \n",
+            "First 5 rows Df2:\n",
+            "    id  gender   age    height      weight  family_history_with_overweight  \\\n",
+            "1    1  Female  18.0  1.560000   57.000000                               1   \n",
+            "2    2  Female  18.0  1.711460   50.165754                               1   \n",
+            "8    8    Male  17.0  1.700000   70.000000                               0   \n",
+            "12  12    Male  18.0  1.811189  108.251044                               1   \n",
+            "17  17  Female  18.0  1.560000   50.000000                               0   \n",
+            "\n",
+            "    favc      fcvc       ncp        caec  smoke      ch2o  scc       faf  \\\n",
+            "1      1  2.000000  3.000000  Frequently      0  2.000000    0  1.000000   \n",
+            "2      1  1.880534  1.411685   Sometimes      0  1.910378    0  0.866045   \n",
+            "8      1  2.000000  3.000000   Sometimes      0  3.000000    1  1.000000   \n",
+            "12     1  2.000000  2.164839   Sometimes      0  2.530157    0  1.000000   \n",
+            "17     1  3.000000  3.000000   Sometimes      0  1.000000    0  1.000000   \n",
+            "\n",
+            "         tue       calc                 mtrans              0be1dad  \n",
+            "1   1.000000          0             Automobile         0rmal_Weight  \n",
+            "2   1.673584          0  Public_Transportation  Insufficient_Weight  \n",
+            "8   1.000000          0  Public_Transportation   Overweight_Level_I  \n",
+            "12  0.553311          0  Public_Transportation       Obesity_Type_I  \n",
+            "17  0.000000  Sometimes  Public_Transportation         0rmal_Weight  \n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"First 5 rows Df1:\")\n",
+        "print(df1_filtered.head())\n",
+        "print(\"First 5 rows Df2:\")\n",
+        "print(df2_filtered.head())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2NFhxW-XuP6-"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fbngCBSrzjHa",
+        "outputId": "bb756e6b-a807-4395-e902-06b7f677ba25"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "df1 Gender options: ['Female', 'Male']\n",
+            "df2 Gender options: ['Female', 'Male']\n",
+            "df1 family_history_with_overweight options: ['no', 'yes']\n",
+            "df2 family_history_with_overweight options: [np.int64(0), np.int64(1)]\n",
+            "df1 Favc options: ['no', 'yes']\n",
+            "df2 Favc options: [np.int64(0), np.int64(1)]\n",
+            "df1 Caec options: ['Always', 'Frequently', 'Sometimes']\n",
+            "df2 Caec options: ['0', 'Always', 'Frequently', 'Sometimes']\n",
+            "df1 smoke options: ['no', 'yes']\n",
+            "df2 smoke options: [np.int64(0), np.int64(1)]\n",
+            "df1 scc options: ['no', 'yes']\n",
+            "df2 scc options: [np.int64(0), np.int64(1)]\n",
+            "df1 calc options: ['Frequently', 'Sometimes', 'no']\n",
+            "df2 calc options: ['0', 'Frequently', 'Sometimes']\n",
+            "df1 mtrans options: ['Automobile', 'Public_Transportation', 'Walking']\n",
+            "df2 mtrans options: ['Automobile', 'Bike', 'Motorbike', 'Public_Transportation', 'Walking']\n",
+            "df1 obesity level options: ['Insufficient_Weight', 'Normal_Weight', 'Obesity_Type_I', 'Obesity_Type_III', 'Overweight_Level_I', 'Overweight_Level_II']\n",
+            "df2 obestiy level options: ['0rmal_Weight', 'Insufficient_Weight', 'Obesity_Type_I', 'Obesity_Type_II', 'Obesity_Type_III', 'Overweight_Level_I', 'Overweight_Level_II']\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"df1 Gender options:\", sorted(df1_filtered['gender'].dropna().unique()))\n",
+        "print(\"df2 Gender options:\", sorted(df2_filtered['gender'].dropna().unique()))\n",
+        "print(\"df1 family_history_with_overweight options:\", sorted(df1_filtered['family_history_with_overweight'].dropna().unique()))\n",
+        "print(\"df2 family_history_with_overweight options:\", sorted(df2_filtered['family_history_with_overweight'].dropna().unique()))\n",
+        "print(\"df1 Favc options:\", sorted(df1_filtered['favc'].dropna().unique()))\n",
+        "print(\"df2 Favc options:\", sorted(df2_filtered['favc'].dropna().unique()))\n",
+        "print(\"df1 Caec options:\", sorted(df1_filtered['caec'].dropna().unique()))\n",
+        "print(\"df2 Caec options:\", sorted(df2_filtered['caec'].dropna().unique()))\n",
+        "print(\"df1 smoke options:\", sorted(df1_filtered['smoke'].dropna().unique()))\n",
+        "print(\"df2 smoke options:\", sorted(df2_filtered['smoke'].dropna().unique()))\n",
+        "print(\"df1 scc options:\", sorted(df1_filtered['scc'].dropna().unique()))\n",
+        "print(\"df2 scc options:\", sorted(df2_filtered['scc'].dropna().unique()))\n",
+        "print(\"df1 calc options:\", sorted(df1_filtered['calc'].dropna().unique()))\n",
+        "print(\"df2 calc options:\", sorted(df2_filtered['calc'].dropna().unique()))\n",
+        "print(\"df1 mtrans options:\", sorted(df1_filtered['mtrans'].dropna().unique()))\n",
+        "print(\"df2 mtrans options:\", sorted(df2_filtered['mtrans'].dropna().unique()))\n",
+        "print(\"df1 obesity level options:\", sorted(df1_filtered['nobeyesdad'].dropna().unique()))\n",
+        "print(\"df2 obestiy level options:\", sorted(df2_filtered['0be1dad'].dropna().unique()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "id": "z9ho6SAjzo--"
+      },
+      "outputs": [],
+      "source": [
+        "#family history - df1 no/yes to 0/1\n",
+        "#favc -  df1 no/yes to 0/1\n",
+        "#caec - df2 0 to \"never\"\n",
+        "#smoke - df1 no/yes to 0/1\n",
+        "#scc - df1 no/yes to 0/1\n",
+        "#calc - df2 0 to \"no\"\n",
+        "#mtrans - can combine\n",
+        "#obesity - df2 0ormal_Weight to \"Normal_Weight\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "l8ke3yw6ss18",
+        "outputId": "1e04b973-a026-4d7f-fd1b-41fb5c4d16d6"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_14828\\4117563716.py:5: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
+            "  df1_filtered[col] = df1_filtered[col].replace({\"no\": 0, \"yes\": 1})\n",
+            "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_14828\\4117563716.py:5: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  df1_filtered[col] = df1_filtered[col].replace({\"no\": 0, \"yes\": 1})\n",
+            "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_14828\\4117563716.py:10: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  df2_filtered[\"caec\"] = df2_filtered[\"caec\"].replace({0: \"never\"})\n",
+            "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_14828\\4117563716.py:14: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  df2_filtered[\"calc\"] = df2_filtered[\"calc\"].replace({0: \"no\"})\n",
+            "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_14828\\4117563716.py:18: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  df2_filtered[\"0be1dad\"] = df2_filtered[\"0be1dad\"].replace({\"0rmal_Weight\": \"Normal_Weight\"})\n"
+          ]
+        }
+      ],
+      "source": [
+        "# --- df1_filtered changes ---\n",
+        "# Family history, FAVC, Smoke, SCC: convert \"no\"/\"yes\" -> 0/1\n",
+        "for col in [\"family_history_with_overweight\", \"favc\", \"smoke\", \"scc\"]:\n",
+        "    if col in df1_filtered.columns:\n",
+        "        df1_filtered[col] = df1_filtered[col].replace({\"no\": 0, \"yes\": 1})\n",
+        "\n",
+        "# --- df2_filtered changes ---\n",
+        "# CAEC: 0 -> \"never\"\n",
+        "if \"caec\" in df2_filtered.columns:\n",
+        "    df2_filtered[\"caec\"] = df2_filtered[\"caec\"].replace({0: \"never\"})\n",
+        "\n",
+        "# Calc: 0 -> \"no\"\n",
+        "if \"calc\" in df2_filtered.columns:\n",
+        "    df2_filtered[\"calc\"] = df2_filtered[\"calc\"].replace({0: \"no\"})\n",
+        "\n",
+        "# Obesity: fix typo \"0ormal_Weight\" -> \"Normal_Weight\"\n",
+        "if \"0be1dad\" in df2_filtered.columns:\n",
+        "    df2_filtered[\"0be1dad\"] = df2_filtered[\"0be1dad\"].replace({\"0rmal_Weight\": \"Normal_Weight\"})\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xrY5yX6As1Eu",
+        "outputId": "8a3b0677-bff1-4632-b170-f1b881d87a3d"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "df1 family history: [1 0]\n",
+            "df1 favc: [1 0]\n",
+            "df1 smoke: [0 1]\n",
+            "df1 scc: [0 1]\n",
+            "\n",
+            "df2 caec: ['Frequently' 'Sometimes' 'Always' '0']\n",
+            "df2 calc: ['0' 'Sometimes' 'Frequently']\n",
+            "df2 obesity: ['Normal_Weight' 'Insufficient_Weight' 'Overweight_Level_I'\n",
+            " 'Obesity_Type_I' 'Overweight_Level_II' 'Obesity_Type_II'\n",
+            " 'Obesity_Type_III']\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"df1 family history:\", df1_filtered[\"family_history_with_overweight\"].unique())\n",
+        "print(\"df1 favc:\", df1_filtered[\"favc\"].unique())\n",
+        "print(\"df1 smoke:\", df1_filtered[\"smoke\"].unique())\n",
+        "print(\"df1 scc:\", df1_filtered[\"scc\"].unique())\n",
+        "\n",
+        "print(\"\\ndf2 caec:\", df2_filtered[\"caec\"].unique())\n",
+        "print(\"df2 calc:\", df2_filtered[\"calc\"].unique())\n",
+        "print(\"df2 obesity:\", df2_filtered[\"0be1dad\"].unique())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TGHxwUMzufbR",
+        "outputId": "32206a33-2c63-4780-89cf-d85b4b6c4955"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "df1 columns: ['gender', 'age', 'height', 'weight', 'family_history_with_overweight', 'favc', 'fcvc', 'ncp', 'caec', 'smoke', 'ch2o', 'scc', 'faf', 'tue', 'calc', 'mtrans', 'nobeyesdad']\n",
+            "df2 columns: ['id', 'gender', 'age', 'height', 'weight', 'family_history_with_overweight', 'favc', 'fcvc', 'ncp', 'caec', 'smoke', 'ch2o', 'scc', 'faf', 'tue', 'calc', 'mtrans', '0be1dad']\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"df1 columns:\", list(df1_filtered.columns))\n",
+        "print(\"df2 columns:\", list(df2_filtered.columns))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hfD_ZnMku7hO",
+        "outputId": "2d2e93f1-31cd-40d0-e86c-f289183fc015"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Combined dataset shape: (3213, 17)\n",
+            "Combined columns: ['gender', 'age', 'height', 'weight', 'family_history_with_overweight', 'favc', 'fcvc', 'ncp', 'caec', 'smoke', 'ch2o', 'scc', 'faf', 'tue', 'calc', 'mtrans', 'nobeyesdad']\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Step 1: Drop 'id' column\n",
+        "if \"id\" in df2_filtered.columns:\n",
+        "    df2_filtered = df2_filtered.drop(columns=[\"id\"])\n",
+        "\n",
+        "# Step 2: Rename '0be1dad' to 'nobeyesdad'\n",
+        "df2_filtered = df2_filtered.rename(columns={\"0be1dad\": \"nobeyesdad\"})\n",
+        "\n",
+        "# Step 3 (optional): Ensure same column order as df1_filtered\n",
+        "df2_filtered = df2_filtered[df1_filtered.columns]\n",
+        "\n",
+        "# Step 4: Combine\n",
+        "import pandas as pd\n",
+        "combined_df = pd.concat([df1_filtered, df2_filtered], ignore_index=True)\n",
+        "\n",
+        "print(\"Combined dataset shape:\", combined_df.shape)\n",
+        "print(\"Combined columns:\", combined_df.columns.tolist())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qo0TETLFna_X",
+        "outputId": "39be0dd5-39a5-4701-d790-aa6c70633932"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 3213 entries, 0 to 3212\n",
+            "Data columns (total 17 columns):\n",
+            " #   Column                          Non-Null Count  Dtype  \n",
+            "---  ------                          --------------  -----  \n",
+            " 0   gender                          3213 non-null   object \n",
+            " 1   age                             3213 non-null   float64\n",
+            " 2   height                          3213 non-null   float64\n",
+            " 3   weight                          3213 non-null   float64\n",
+            " 4   family_history_with_overweight  3213 non-null   int64  \n",
+            " 5   favc                            3213 non-null   int64  \n",
+            " 6   fcvc                            3213 non-null   float64\n",
+            " 7   ncp                             3213 non-null   float64\n",
+            " 8   caec                            3213 non-null   object \n",
+            " 9   smoke                           3213 non-null   int64  \n",
+            " 10  ch2o                            3213 non-null   float64\n",
+            " 11  scc                             3213 non-null   int64  \n",
+            " 12  faf                             3213 non-null   float64\n",
+            " 13  tue                             3213 non-null   float64\n",
+            " 14  calc                            3213 non-null   object \n",
+            " 15  mtrans                          3213 non-null   object \n",
+            " 16  nobeyesdad                      3213 non-null   object \n",
+            "dtypes: float64(8), int64(4), object(5)\n",
+            "memory usage: 426.9+ KB\n"
+          ]
+        }
+      ],
+      "source": [
+        "combined_df.info()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "w0f0jmphrUnL",
+        "outputId": "84abef2b-e6d7-4c8a-f083-1e3ec1b913e7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Original shape: (3213, 17)\n",
+            "Encoded features shape: (3213, 23)\n",
+            "Target shape: (3213,)\n"
+          ]
+        }
+      ],
+      "source": [
+        "#One-Hot Encoding\n",
+        "categorical_cols = ['gender', 'caec', 'calc', 'mtrans']\n",
+        "\n",
+        "X = combined_df.drop(columns=['nobeyesdad'])\n",
+        "y = combined_df['nobeyesdad']\n",
+        "\n",
+        "categorical_encoded = pd.get_dummies(X, columns=categorical_cols, drop_first=True)\n",
+        "\n",
+        "print(\"Original shape:\", combined_df.shape)\n",
+        "print(\"Encoded features shape:\", categorical_encoded.shape)\n",
+        "print(\"Target shape:\", y.shape)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OeUCF3NYr1PP",
+        "outputId": "065daf47-b796-4473-911c-b5ce5856fe19"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'Insufficient_Weight': np.int64(0), 'Normal_Weight': np.int64(1), 'Obesity_Type_I': np.int64(2), 'Obesity_Type_II': np.int64(3), 'Obesity_Type_III': np.int64(4), 'Overweight_Level_I': np.int64(5), 'Overweight_Level_II': np.int64(6)}\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Encoding Target Variable\n",
+        "\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "\n",
+        "le = LabelEncoder()\n",
+        "y_encoded = le.fit_transform(y)\n",
+        "\n",
+        "# Check?\n",
+        "print(dict(zip(le.classes_, le.transform(le.classes_))))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "zIZmYN92tQ5p"
+      },
+      "outputs": [
+        {
+          "ename": "NameError",
+          "evalue": "name 'X_encoded' is not defined",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+            "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[24]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m final_df = \u001b[43mX_encoded\u001b[49m.copy()\n\u001b[32m      2\u001b[39m final_df[\u001b[33m'\u001b[39m\u001b[33mnobeyesdad\u001b[39m\u001b[33m'\u001b[39m] = y_encoded\n\u001b[32m      4\u001b[39m \u001b[38;5;28mprint\u001b[39m(final_df.head())\n",
+            "\u001b[31mNameError\u001b[39m: name 'X_encoded' is not defined"
+          ]
+        }
+      ],
+      "source": [
+        "final_df = X_encoded.copy()\n",
+        "final_df['nobeyesdad'] = y_encoded\n",
+        "\n",
+        "print(final_df.head())\n",
+        "print(\"Final dataset shape:\", final_df.shape)\n",
+        "\n",
+        "#categorical_encoded"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dDt_nSB8th8T",
+        "outputId": "c0ede394-f4a8-47ff-f641-853be976d91e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "    age  height  weight  family_history_with_overweight  favc  fcvc  ncp  \\\n",
+            "0  17.0    1.65    67.0                               1     1   3.0  1.0   \n",
+            "1  18.0    1.56    51.0                               1     1   2.0  4.0   \n",
+            "2  17.0    1.75    57.0                               1     1   3.0  3.0   \n",
+            "3  15.0    1.65    86.0                               1     1   3.0  3.0   \n",
+            "4  17.0    1.70    85.0                               1     0   2.0  3.0   \n",
+            "\n",
+            "   smoke  ch2o  scc  ...  caec_Frequently  caec_Sometimes  calc_Frequently  \\\n",
+            "0      0   2.0    0  ...            False            True            False   \n",
+            "1      0   2.0    0  ...             True           False            False   \n",
+            "2      0   2.0    0  ...             True           False            False   \n",
+            "3      0   1.0    0  ...            False            True            False   \n",
+            "4      0   2.0    0  ...             True           False            False   \n",
+            "\n",
+            "   calc_Sometimes  calc_no  mtrans_Bike  mtrans_Motorbike  \\\n",
+            "0           False     True        False             False   \n",
+            "1            True    False        False             False   \n",
+            "2           False     True        False             False   \n",
+            "3           False     True        False             False   \n",
+            "4           False     True        False             False   \n",
+            "\n",
+            "   mtrans_Public_Transportation  mtrans_Walking  nobeyesdad  \n",
+            "0                         False            True           1  \n",
+            "1                          True           False           1  \n",
+            "2                          True           False           1  \n",
+            "3                         False            True           2  \n",
+            "4                          True           False           6  \n",
+            "\n",
+            "[5 rows x 24 columns]\n",
+            "Final dataset shape: (3213, 24)\n"
+          ]
+        }
+      ],
+      "source": [
+        "final_df = categorical_encoded.copy()\n",
+        "final_df['nobeyesdad'] = y_encoded\n",
+        "\n",
+        "print(final_df.head())\n",
+        "print(\"Final dataset shape:\", final_df.shape)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "m1BUP8DxvWr8",
+        "outputId": "a6158bef-3f2e-428f-99d5-f0cfb7598202"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Dataset exported as combined_dataset.csv\n"
+          ]
+        }
+      ],
+      "source": [
+        "final_df.to_csv(\"Final_combined_dataset.csv\", index=False)\n",
+        "\n",
+        "print(\"Dataset exported as combined_dataset.csv\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "L5dkNDQ6vuGp",
+        "outputId": "9bb1c53e-a46c-4189-c121-84a2a9109fee"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "First 5 rows:\n",
+            "    age  height  weight  family_history_with_overweight  favc  fcvc  ncp  \\\n",
+            "0  17.0    1.65    67.0                               1     1   3.0  1.0   \n",
+            "1  18.0    1.56    51.0                               1     1   2.0  4.0   \n",
+            "2  17.0    1.75    57.0                               1     1   3.0  3.0   \n",
+            "3  15.0    1.65    86.0                               1     1   3.0  3.0   \n",
+            "4  17.0    1.70    85.0                               1     0   2.0  3.0   \n",
+            "\n",
+            "   smoke  ch2o  scc  ...  caec_Frequently  caec_Sometimes  calc_Frequently  \\\n",
+            "0      0   2.0    0  ...            False            True            False   \n",
+            "1      0   2.0    0  ...             True           False            False   \n",
+            "2      0   2.0    0  ...             True           False            False   \n",
+            "3      0   1.0    0  ...            False            True            False   \n",
+            "4      0   2.0    0  ...             True           False            False   \n",
+            "\n",
+            "   calc_Sometimes  calc_no  mtrans_Bike  mtrans_Motorbike  \\\n",
+            "0           False     True        False             False   \n",
+            "1            True    False        False             False   \n",
+            "2           False     True        False             False   \n",
+            "3           False     True        False             False   \n",
+            "4           False     True        False             False   \n",
+            "\n",
+            "   mtrans_Public_Transportation  mtrans_Walking  nobeyesdad  \n",
+            "0                         False            True           1  \n",
+            "1                          True           False           1  \n",
+            "2                          True           False           1  \n",
+            "3                         False            True           2  \n",
+            "4                          True           False           6  \n",
+            "\n",
+            "[5 rows x 24 columns]\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"First 5 rows:\")\n",
+        "print(final_df.head())"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "base",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/data_science/shashank_week9_xgboost/notebooks/week9_xgboost.ipynb b/data_science/shashank_week9_xgboost/notebooks/week9_xgboost.ipynb
new file mode 100644
index 0000000..660fafa
--- /dev/null
+++ b/data_science/shashank_week9_xgboost/notebooks/week9_xgboost.ipynb
@@ -0,0 +1,3676 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NxxYimFjN2LK",
+        "outputId": "3f51e4a2-fd30-4309-f7ff-32cca23e1cba"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (2.0.2)\n",
+            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.12/dist-packages (1.6.1)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n",
+            "Requirement already satisfied: seaborn in /usr/local/lib/python3.12/dist-packages (0.13.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n",
+            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.16.1)\n",
+            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.5.2)\n",
+            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (3.6.0)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.59.2)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.4.9)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (25.0)\n",
+            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.2.3)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Install required packages (skip if already available)\n",
+        "!pip install pandas numpy scikit-learn matplotlib seaborn\n",
+        "\n",
+        "import pandas as pd, numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from pathlib import Path\n",
+        "\n",
+        "pd.set_option(\"display.max_columns\", 120)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "RAW_URL = \"https://raw.githubusercontent.com/MAYURA26-bot/Lachesis-CHOP/main/Final_combined_dataset.csv\"\n",
+        "df = pd.read_csv(RAW_URL)\n",
+        "\n",
+        "print(\"Rows, Cols:\", df.shape)\n",
+        "df.head(10)   # <-- Take screenshot of this output\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 400
+        },
+        "id": "GoKbmmpgN-CR",
+        "outputId": "a82a0cea-d20a-4797-e754-cbe3269c44c0"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Rows, Cols: (3213, 24)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    age  height  weight  family_history_with_overweight  favc  fcvc  ncp  \\\n",
+              "0  17.0    1.65    67.0                               1     1   3.0  1.0   \n",
+              "1  18.0    1.56    51.0                               1     1   2.0  4.0   \n",
+              "2  17.0    1.75    57.0                               1     1   3.0  3.0   \n",
+              "3  15.0    1.65    86.0                               1     1   3.0  3.0   \n",
+              "4  17.0    1.70    85.0                               1     0   2.0  3.0   \n",
+              "5  18.0    1.75    80.0                               1     1   2.0  3.0   \n",
+              "6  18.0    1.60    58.0                               1     1   2.0  3.0   \n",
+              "7  18.0    1.74    64.0                               0     0   3.0  3.0   \n",
+              "8  18.0    1.57    48.0                               1     1   3.0  1.0   \n",
+              "9  18.0    1.58    48.0                               0     1   2.0  3.0   \n",
+              "\n",
+              "   smoke  ch2o  scc  faf  tue  gender_Male  caec_Always  caec_Frequently  \\\n",
+              "0      0   2.0    0  1.0  1.0        False        False            False   \n",
+              "1      0   2.0    0  1.0  0.0        False        False             True   \n",
+              "2      0   2.0    0  0.0  1.0        False        False             True   \n",
+              "3      0   1.0    0  3.0  2.0        False        False            False   \n",
+              "4      0   2.0    0  1.0  1.0        False        False             True   \n",
+              "5      0   2.0    0  0.0  0.0         True         True            False   \n",
+              "6      0   2.0    0  1.0  0.0         True        False             True   \n",
+              "7      0   2.0    1  0.0  1.0         True        False            False   \n",
+              "8      0   1.0    0  1.0  0.0        False         True            False   \n",
+              "9      0   2.0    0  1.0  0.0        False        False            False   \n",
+              "\n",
+              "   caec_Sometimes  calc_Frequently  calc_Sometimes  calc_no  mtrans_Bike  \\\n",
+              "0            True            False           False     True        False   \n",
+              "1           False            False            True    False        False   \n",
+              "2           False            False           False     True        False   \n",
+              "3            True            False           False     True        False   \n",
+              "4           False            False           False     True        False   \n",
+              "5           False             True           False    False        False   \n",
+              "6           False            False            True    False        False   \n",
+              "7            True            False           False     True        False   \n",
+              "8           False            False           False     True        False   \n",
+              "9            True            False           False     True        False   \n",
+              "\n",
+              "   mtrans_Motorbike  mtrans_Public_Transportation  mtrans_Walking  nobeyesdad  \n",
+              "0             False                         False            True           1  \n",
+              "1             False                          True           False           1  \n",
+              "2             False                          True           False           1  \n",
+              "3             False                         False            True           2  \n",
+              "4             False                          True           False           6  \n",
+              "5             False                          True           False           5  \n",
+              "6             False                          True           False           1  \n",
+              "7             False                          True           False           1  \n",
+              "8             False                          True           False           1  \n",
+              "9             False                          True           False           1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f757f6e1-3d0e-448f-b864-3f8016939ef6\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>height</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>family_history_with_overweight</th>\n",
+              "      <th>favc</th>\n",
+              "      <th>fcvc</th>\n",
+              "      <th>ncp</th>\n",
+              "      <th>smoke</th>\n",
+              "      <th>ch2o</th>\n",
+              "      <th>scc</th>\n",
+              "      <th>faf</th>\n",
+              "      <th>tue</th>\n",
+              "      <th>gender_Male</th>\n",
+              "      <th>caec_Always</th>\n",
+              "      <th>caec_Frequently</th>\n",
+              "      <th>caec_Sometimes</th>\n",
+              "      <th>calc_Frequently</th>\n",
+              "      <th>calc_Sometimes</th>\n",
+              "      <th>calc_no</th>\n",
+              "      <th>mtrans_Bike</th>\n",
+              "      <th>mtrans_Motorbike</th>\n",
+              "      <th>mtrans_Public_Transportation</th>\n",
+              "      <th>mtrans_Walking</th>\n",
+              "      <th>nobeyesdad</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1.65</td>\n",
+              "      <td>67.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
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+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1.56</td>\n",
+              "      <td>51.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>4.0</td>\n",
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+              "      <td>0.0</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1.75</td>\n",
+              "      <td>57.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>15.0</td>\n",
+              "      <td>1.65</td>\n",
+              "      <td>86.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1.70</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1.75</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
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+              "      <td>3.0</td>\n",
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+              "      <td>0.0</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1.60</td>\n",
+              "      <td>58.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1.74</td>\n",
+              "      <td>64.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1.57</td>\n",
+              "      <td>48.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1.58</td>\n",
+              "      <td>48.0</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f757f6e1-3d0e-448f-b864-3f8016939ef6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
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+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f757f6e1-3d0e-448f-b864-3f8016939ef6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-f757f6e1-3d0e-448f-b864-3f8016939ef6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "\n",
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+              "      </button>\n",
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+              "<style>\n",
+              "  .colab-df-quickchart {\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "      --hover-bg-color: #434B5C;\n",
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+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-4ede8783-362e-4086-af8a-64bb12cf2d63 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Structure of dataset\n",
+        "df.info()\n",
+        "\n",
+        "# Quick summary statistics\n",
+        "df.describe(include=\"all\").T.head(20)  # shows first 20 rows of summary\n",
+        "\n",
+        "# Missing values (%)\n",
+        "df.isna().mean().sort_values(ascending=False).head(20)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "WMqf_Q7jORfB",
+        "outputId": "c5c30ef4-a377-4a34-829c-d191e957f588"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 3213 entries, 0 to 3212\n",
+            "Data columns (total 24 columns):\n",
+            " #   Column                          Non-Null Count  Dtype  \n",
+            "---  ------                          --------------  -----  \n",
+            " 0   age                             3213 non-null   float64\n",
+            " 1   height                          3213 non-null   float64\n",
+            " 2   weight                          3213 non-null   float64\n",
+            " 3   family_history_with_overweight  3213 non-null   int64  \n",
+            " 4   favc                            3213 non-null   int64  \n",
+            " 5   fcvc                            3213 non-null   float64\n",
+            " 6   ncp                             3213 non-null   float64\n",
+            " 7   smoke                           3213 non-null   int64  \n",
+            " 8   ch2o                            3213 non-null   float64\n",
+            " 9   scc                             3213 non-null   int64  \n",
+            " 10  faf                             3213 non-null   float64\n",
+            " 11  tue                             3213 non-null   float64\n",
+            " 12  gender_Male                     3213 non-null   bool   \n",
+            " 13  caec_Always                     3213 non-null   bool   \n",
+            " 14  caec_Frequently                 3213 non-null   bool   \n",
+            " 15  caec_Sometimes                  3213 non-null   bool   \n",
+            " 16  calc_Frequently                 3213 non-null   bool   \n",
+            " 17  calc_Sometimes                  3213 non-null   bool   \n",
+            " 18  calc_no                         3213 non-null   bool   \n",
+            " 19  mtrans_Bike                     3213 non-null   bool   \n",
+            " 20  mtrans_Motorbike                3213 non-null   bool   \n",
+            " 21  mtrans_Public_Transportation    3213 non-null   bool   \n",
+            " 22  mtrans_Walking                  3213 non-null   bool   \n",
+            " 23  nobeyesdad                      3213 non-null   int64  \n",
+            "dtypes: bool(11), float64(8), int64(5)\n",
+            "memory usage: 361.0 KB\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "age                               0.0\n",
+              "height                            0.0\n",
+              "weight                            0.0\n",
+              "family_history_with_overweight    0.0\n",
+              "favc                              0.0\n",
+              "fcvc                              0.0\n",
+              "ncp                               0.0\n",
+              "smoke                             0.0\n",
+              "ch2o                              0.0\n",
+              "scc                               0.0\n",
+              "faf                               0.0\n",
+              "tue                               0.0\n",
+              "gender_Male                       0.0\n",
+              "caec_Always                       0.0\n",
+              "caec_Frequently                   0.0\n",
+              "caec_Sometimes                    0.0\n",
+              "calc_Frequently                   0.0\n",
+              "calc_Sometimes                    0.0\n",
+              "calc_no                           0.0\n",
+              "mtrans_Bike                       0.0\n",
+              "dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>age</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>height</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>weight</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>family_history_with_overweight</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>favc</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>fcvc</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ncp</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>smoke</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ch2o</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>scc</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>faf</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>tue</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>gender_Male</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>caec_Always</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>caec_Frequently</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>caec_Sometimes</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>calc_Frequently</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>calc_Sometimes</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>calc_no</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mtrans_Bike</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "direct_ids = {\"name\",\"full_name\",\"first_name\",\"last_name\",\"email\",\"phone\",\"mobile\",\"address\",\"dob\",\"date_of_birth\",\"studentid\",\"id\"}\n",
+        "quasi_ids  = {\"age\",\"postcode\",\"postal_code\",\"zip\",\"suburb\",\"school\",\"class\",\"gps\",\"lat\",\"lon\",\"latitude\",\"longitude\",\"lga\",\"sa2\"}\n",
+        "sensitive  = {\"gender\",\"sex\",\"height\",\"weight\",\"bmi\",\"heartrate\",\"heart_rate\",\"diagnosis\",\"condition\",\"sleep\",\"screen_time\",\"diet\",\"activity\",\"steps\",\"calories\",\"bp\",\"blood_pressure\"}\n",
+        "\n",
+        "lower_map = {c.lower(): c for c in df.columns}\n",
+        "present = {\n",
+        "    \"Direct identifiers\": [lower_map[c] for c in lower_map if c in direct_ids],\n",
+        "    \"Quasi-identifiers\":  [lower_map[c] for c in lower_map if c in quasi_ids],\n",
+        "    \"Sensitive fields\":   [lower_map[c] for c in lower_map if c in sensitive],\n",
+        "}\n",
+        "present\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hr0I2w4UOY5g",
+        "outputId": "1dbf5815-54ae-4cd3-c01d-02004613e429"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'Direct identifiers': [],\n",
+              " 'Quasi-identifiers': ['age'],\n",
+              " 'Sensitive fields': ['height', 'weight']}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "rows = []\n",
+        "for k, cols in present.items():\n",
+        "    rows.append({\"Category\": k, \"Columns found\": \", \".join(cols) if cols else \"(none)\"})\n",
+        "audit_df = pd.DataFrame(rows)\n",
+        "audit_df.to_csv(\"legality_ethics_audit_findings.csv\", index=False)\n",
+        "audit_df\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 143
+        },
+        "id": "72eU1MKEOZpW",
+        "outputId": "da9f834c-c318-4eda-c157-0c3c176cde8f"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             Category   Columns found\n",
+              "0  Direct identifiers          (none)\n",
+              "1   Quasi-identifiers             age\n",
+              "2    Sensitive fields  height, weight"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-42953b5a-a943-44be-b82f-d090515482d9\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Category</th>\n",
+              "      <th>Columns found</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Direct identifiers</td>\n",
+              "      <td>(none)</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Quasi-identifiers</td>\n",
+              "      <td>age</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sensitive fields</td>\n",
+              "      <td>height, weight</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-42953b5a-a943-44be-b82f-d090515482d9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-42953b5a-a943-44be-b82f-d090515482d9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-42953b5a-a943-44be-b82f-d090515482d9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-52e0832d-f56c-4963-a14e-95e46e9875df\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-52e0832d-f56c-4963-a14e-95e46e9875df')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-52e0832d-f56c-4963-a14e-95e46e9875df button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_2f67cff0-5d3d-44ae-8273-c155ed3d9957\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('audit_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_2f67cff0-5d3d-44ae-8273-c155ed3d9957 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('audit_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "audit_df",
+              "summary": "{\n  \"name\": \"audit_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Direct identifiers\",\n          \"Quasi-identifiers\",\n          \"Sensitive fields\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Columns found\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"(none)\",\n          \"age\",\n          \"height, weight\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_demo = df.copy()\n",
+        "\n",
+        "# Drop direct identifiers if present (not in this dataset, but just in case)\n",
+        "for cand in [\"Name\",\"Email\",\"Address\",\"Phone\"]:\n",
+        "    if cand in df_demo.columns:\n",
+        "        df_demo = df_demo.drop(columns=[cand])\n",
+        "\n",
+        "# Example anonymization: bucket Age into groups\n",
+        "if \"Age\" in df_demo.columns:\n",
+        "    df_demo[\"Age_Group\"] = pd.cut(df_demo[\"Age\"], bins=[0,5,12,18], labels=[\"0-5\",\"6-12\",\"13-18\"])\n",
+        "\n",
+        "# Example: replace Weight with category\n",
+        "if \"Weight\" in df_demo.columns:\n",
+        "    df_demo[\"Weight_Category\"] = pd.qcut(df_demo[\"Weight\"], 4, labels=[\"Low\",\"Medium\",\"High\",\"Very High\"])\n",
+        "\n",
+        "df_demo.head(10)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 382
+        },
+        "id": "A7kSnf9xO49E",
+        "outputId": "ca54c51a-2695-4c48-d3be-b6369df6c591"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    age  height  weight  family_history_with_overweight  favc  fcvc  ncp  \\\n",
+              "0  17.0    1.65    67.0                               1     1   3.0  1.0   \n",
+              "1  18.0    1.56    51.0                               1     1   2.0  4.0   \n",
+              "2  17.0    1.75    57.0                               1     1   3.0  3.0   \n",
+              "3  15.0    1.65    86.0                               1     1   3.0  3.0   \n",
+              "4  17.0    1.70    85.0                               1     0   2.0  3.0   \n",
+              "5  18.0    1.75    80.0                               1     1   2.0  3.0   \n",
+              "6  18.0    1.60    58.0                               1     1   2.0  3.0   \n",
+              "7  18.0    1.74    64.0                               0     0   3.0  3.0   \n",
+              "8  18.0    1.57    48.0                               1     1   3.0  1.0   \n",
+              "9  18.0    1.58    48.0                               0     1   2.0  3.0   \n",
+              "\n",
+              "   smoke  ch2o  scc  faf  tue  gender_Male  caec_Always  caec_Frequently  \\\n",
+              "0      0   2.0    0  1.0  1.0        False        False            False   \n",
+              "1      0   2.0    0  1.0  0.0        False        False             True   \n",
+              "2      0   2.0    0  0.0  1.0        False        False             True   \n",
+              "3      0   1.0    0  3.0  2.0        False        False            False   \n",
+              "4      0   2.0    0  1.0  1.0        False        False             True   \n",
+              "5      0   2.0    0  0.0  0.0         True         True            False   \n",
+              "6      0   2.0    0  1.0  0.0         True        False             True   \n",
+              "7      0   2.0    1  0.0  1.0         True        False            False   \n",
+              "8      0   1.0    0  1.0  0.0        False         True            False   \n",
+              "9      0   2.0    0  1.0  0.0        False        False            False   \n",
+              "\n",
+              "   caec_Sometimes  calc_Frequently  calc_Sometimes  calc_no  mtrans_Bike  \\\n",
+              "0            True            False           False     True        False   \n",
+              "1           False            False            True    False        False   \n",
+              "2           False            False           False     True        False   \n",
+              "3            True            False           False     True        False   \n",
+              "4           False            False           False     True        False   \n",
+              "5           False             True           False    False        False   \n",
+              "6           False            False            True    False        False   \n",
+              "7            True            False           False     True        False   \n",
+              "8           False            False           False     True        False   \n",
+              "9            True            False           False     True        False   \n",
+              "\n",
+              "   mtrans_Motorbike  mtrans_Public_Transportation  mtrans_Walking  nobeyesdad  \n",
+              "0             False                         False            True           1  \n",
+              "1             False                          True           False           1  \n",
+              "2             False                          True           False           1  \n",
+              "3             False                         False            True           2  \n",
+              "4             False                          True           False           6  \n",
+              "5             False                          True           False           5  \n",
+              "6             False                          True           False           1  \n",
+              "7             False                          True           False           1  \n",
+              "8             False                          True           False           1  \n",
+              "9             False                          True           False           1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e1abc783-ddfc-4e42-ba19-a43fa7370b1d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>height</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>family_history_with_overweight</th>\n",
+              "      <th>favc</th>\n",
+              "      <th>fcvc</th>\n",
+              "      <th>ncp</th>\n",
+              "      <th>smoke</th>\n",
+              "      <th>ch2o</th>\n",
+              "      <th>scc</th>\n",
+              "      <th>faf</th>\n",
+              "      <th>tue</th>\n",
+              "      <th>gender_Male</th>\n",
+              "      <th>caec_Always</th>\n",
+              "      <th>caec_Frequently</th>\n",
+              "      <th>caec_Sometimes</th>\n",
+              "      <th>calc_Frequently</th>\n",
+              "      <th>calc_Sometimes</th>\n",
+              "      <th>calc_no</th>\n",
+              "      <th>mtrans_Bike</th>\n",
+              "      <th>mtrans_Motorbike</th>\n",
+              "      <th>mtrans_Public_Transportation</th>\n",
+              "      <th>mtrans_Walking</th>\n",
+              "      <th>nobeyesdad</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1.65</td>\n",
+              "      <td>67.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1.56</td>\n",
+              "      <td>51.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1.75</td>\n",
+              "      <td>57.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>15.0</td>\n",
+              "      <td>1.65</td>\n",
+              "      <td>86.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1.70</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1.75</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1.60</td>\n",
+              "      <td>58.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1.74</td>\n",
+              "      <td>64.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1.57</td>\n",
+              "      <td>48.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1.58</td>\n",
+              "      <td>48.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e1abc783-ddfc-4e42-ba19-a43fa7370b1d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e1abc783-ddfc-4e42-ba19-a43fa7370b1d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e1abc783-ddfc-4e42-ba19-a43fa7370b1d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-8dd72977-7d94-466c-aa67-99e661a9b0ff\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8dd72977-7d94-466c-aa67-99e661a9b0ff')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-8dd72977-7d94-466c-aa67-99e661a9b0ff button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_demo"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 1) Inspect columns\n",
+        "print(\"Total columns:\", len(df.columns))\n",
+        "print(list(df.columns)[:60])  # first 60 names\n",
+        "\n",
+        "# 2) Heuristics to find target candidates\n",
+        "name_hits = [c for c in df.columns\n",
+        "             if any(k in c.lower() for k in [\"obesity\", \"nobeyesdad\", \"label\", \"class\", \"target\"])]\n",
+        "\n",
+        "print(\"\\nName-based candidates:\", name_hits)\n",
+        "\n",
+        "# 3) Check if there are one-hot label columns (e.g., NObeyesdad_* )\n",
+        "ohe_label_prefixes = []\n",
+        "for c in df.columns:\n",
+        "    cl = c.lower()\n",
+        "    if cl.startswith(\"nobeyesdad\") or cl.startswith(\"obesity_\") or cl.startswith(\"obesitylevel\"):\n",
+        "        ohe_label_prefixes.append(c.split(\"_\")[0])  # crude prefix guess\n",
+        "\n",
+        "print(\"Possible one-hot label prefixes:\", sorted(set(ohe_label_prefixes)))\n",
+        "\n",
+        "# 4) Show nunique for first 40 columns (small-unique columns could be labels)\n",
+        "print(\"\\nNunique (first 40 cols):\")\n",
+        "for c in list(df.columns)[:40]:\n",
+        "    try:\n",
+        "        print(f\"{c:35s} -> {df[c].nunique()}\")\n",
+        "    except Exception:\n",
+        "        pass\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PgCfNgRPPMNn",
+        "outputId": "4e750d86-ff49-4f9d-d813-688c6ee78bd0"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Total columns: 24\n",
+            "['age', 'height', 'weight', 'family_history_with_overweight', 'favc', 'fcvc', 'ncp', 'smoke', 'ch2o', 'scc', 'faf', 'tue', 'gender_Male', 'caec_Always', 'caec_Frequently', 'caec_Sometimes', 'calc_Frequently', 'calc_Sometimes', 'calc_no', 'mtrans_Bike', 'mtrans_Motorbike', 'mtrans_Public_Transportation', 'mtrans_Walking', 'nobeyesdad']\n",
+            "\n",
+            "Name-based candidates: ['nobeyesdad']\n",
+            "Possible one-hot label prefixes: ['nobeyesdad']\n",
+            "\n",
+            "Nunique (first 40 cols):\n",
+            "age                                 -> 98\n",
+            "height                              -> 622\n",
+            "weight                              -> 436\n",
+            "family_history_with_overweight      -> 2\n",
+            "favc                                -> 2\n",
+            "fcvc                                -> 337\n",
+            "ncp                                 -> 252\n",
+            "smoke                               -> 2\n",
+            "ch2o                                -> 383\n",
+            "scc                                 -> 2\n",
+            "faf                                 -> 353\n",
+            "tue                                 -> 415\n",
+            "gender_Male                         -> 2\n",
+            "caec_Always                         -> 2\n",
+            "caec_Frequently                     -> 2\n",
+            "caec_Sometimes                      -> 2\n",
+            "calc_Frequently                     -> 2\n",
+            "calc_Sometimes                      -> 2\n",
+            "calc_no                             -> 2\n",
+            "mtrans_Bike                         -> 2\n",
+            "mtrans_Motorbike                    -> 2\n",
+            "mtrans_Public_Transportation        -> 2\n",
+            "mtrans_Walking                      -> 2\n",
+            "nobeyesdad                          -> 7\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.preprocessing import OneHotEncoder\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "\n",
+        "# ✅ Target column confirmed\n",
+        "TARGET = \"nobeyesdad\"\n",
+        "\n",
+        "X = df.drop(columns=[TARGET])\n",
+        "y = df[TARGET]\n",
+        "\n",
+        "# Split numeric vs categorical\n",
+        "num_cols = X.select_dtypes(include=[\"number\"]).columns.tolist()\n",
+        "cat_cols = [c for c in X.columns if c not in num_cols]\n",
+        "\n",
+        "pre = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        (\"num\", \"passthrough\", num_cols),\n",
+        "        (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "clf = RandomForestClassifier(n_estimators=400, random_state=42, n_jobs=-1)\n",
+        "pipe = Pipeline(steps=[(\"prep\", pre), (\"model\", clf)])\n",
+        "\n",
+        "# Train/test split + cross-validation\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)\n",
+        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+        "cv_scores = cross_val_score(pipe, X_train, y_train, cv=cv, scoring=\"accuracy\", n_jobs=-1)\n",
+        "print(f\"CV Accuracy (mean ± std): {cv_scores.mean():.3f} ± {cv_scores.std():.3f}\")\n",
+        "\n",
+        "# Fit + evaluate\n",
+        "pipe.fit(X_train, y_train)\n",
+        "y_pred = pipe.predict(X_test)\n",
+        "acc = accuracy_score(y_test, y_pred)\n",
+        "print(f\"Test Accuracy: {acc:.3f}\")\n",
+        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n",
+        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nSIzGYnxQDXr",
+        "outputId": "5df4ab64-704e-4cad-b2eb-8e7a93bea926"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/sklearn/model_selection/_split.py:805: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "CV Accuracy (mean ± std): 0.843 ± 0.012\n",
+            "Test Accuracy: 0.854\n",
+            "\n",
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.93      0.93      0.93       221\n",
+            "           1       0.81      0.89      0.85       193\n",
+            "           2       0.94      0.94      0.94        94\n",
+            "           3       0.00      0.00      0.00         1\n",
+            "           5       0.65      0.47      0.55        64\n",
+            "           6       0.76      0.79      0.77        70\n",
+            "\n",
+            "    accuracy                           0.85       643\n",
+            "   macro avg       0.68      0.67      0.67       643\n",
+            "weighted avg       0.85      0.85      0.85       643\n",
+            "\n",
+            "\n",
+            "Confusion Matrix:\n",
+            " [[205  16   0   0   0   0]\n",
+            " [ 11 171   0   0   8   3]\n",
+            " [  1   0  88   0   0   5]\n",
+            " [  0   0   1   0   0   0]\n",
+            " [  2  22   1   0  30   9]\n",
+            " [  1   2   4   0   8  55]]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.preprocessing import OneHotEncoder\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "\n",
+        "X = df.drop(columns=[TARGET])\n",
+        "y = df[TARGET]\n",
+        "\n",
+        "num_cols = X.select_dtypes(include=[\"number\"]).columns.tolist()\n",
+        "cat_cols = [c for c in X.columns if c not in num_cols]\n",
+        "\n",
+        "pre = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        (\"num\", \"passthrough\", num_cols),\n",
+        "        (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "clf = RandomForestClassifier(n_estimators=400, random_state=42, n_jobs=-1)\n",
+        "pipe = Pipeline(steps=[(\"prep\", pre), (\"model\", clf)])\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)\n",
+        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+        "cv_scores = cross_val_score(pipe, X_train, y_train, cv=cv, scoring=\"accuracy\", n_jobs=-1)\n",
+        "print(f\"CV Accuracy (mean ± std): {cv_scores.mean():.3f} ± {cv_scores.std():.3f}\")\n",
+        "\n",
+        "pipe.fit(X_train, y_train)\n",
+        "y_pred = pipe.predict(X_test)\n",
+        "acc = accuracy_score(y_test, y_pred)\n",
+        "print(f\"Test Accuracy: {acc:.3f}\")\n",
+        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n",
+        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Y57wMOiDQPb-",
+        "outputId": "ee638b82-732a-44ed-822d-da7ecc917718"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/sklearn/model_selection/_split.py:805: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "CV Accuracy (mean ± std): 0.843 ± 0.012\n",
+            "Test Accuracy: 0.854\n",
+            "\n",
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.93      0.93      0.93       221\n",
+            "           1       0.81      0.89      0.85       193\n",
+            "           2       0.94      0.94      0.94        94\n",
+            "           3       0.00      0.00      0.00         1\n",
+            "           5       0.65      0.47      0.55        64\n",
+            "           6       0.76      0.79      0.77        70\n",
+            "\n",
+            "    accuracy                           0.85       643\n",
+            "   macro avg       0.68      0.67      0.67       643\n",
+            "weighted avg       0.85      0.85      0.85       643\n",
+            "\n",
+            "\n",
+            "Confusion Matrix:\n",
+            " [[205  16   0   0   0   0]\n",
+            " [ 11 171   0   0   8   3]\n",
+            " [  1   0  88   0   0   5]\n",
+            " [  0   0   1   0   0   0]\n",
+            " [  2  22   1   0  30   9]\n",
+            " [  1   2   4   0   8  55]]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# --- FULL SAFE CELL: rebuild model, then plot CM + Feature Importances ---\n",
+        "\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.preprocessing import OneHotEncoder\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+        "\n",
+        "# 1) Data & target\n",
+        "TARGET = \"nobeyesdad\"\n",
+        "X = df.drop(columns=[TARGET])\n",
+        "y = df[TARGET]\n",
+        "\n",
+        "# 2) Preprocessing (num passthrough, cat one-hot)\n",
+        "num_cols = X.select_dtypes(include=[\"number\"]).columns.tolist()\n",
+        "cat_cols = [c for c in X.columns if c not in num_cols]\n",
+        "\n",
+        "pre = ColumnTransformer([\n",
+        "    (\"num\", \"passthrough\", num_cols),\n",
+        "    (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols),\n",
+        "])\n",
+        "\n",
+        "# 3) Model (inside a pipeline)\n",
+        "rf = RandomForestClassifier(n_estimators=400, random_state=42, n_jobs=-1)\n",
+        "pipe = Pipeline([(\"prep\", pre), (\"model\", rf)])\n",
+        "\n",
+        "# 4) Train/test split + fit + predict\n",
+        "X_train, X_test, y_train, y_test = train_test_split(\n",
+        "    X, y, test_size=0.2, stratify=y, random_state=42\n",
+        ")\n",
+        "pipe.fit(X_train, y_train)\n",
+        "y_pred = pipe.predict(X_test)\n",
+        "\n",
+        "# (optional) quick metrics in text\n",
+        "acc = accuracy_score(y_test, y_pred)\n",
+        "print(f\"Test Accuracy: {acc:.3f}\")\n",
+        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n",
+        "\n",
+        "# 5) Confusion Matrix Heatmap\n",
+        "plt.figure(figsize=(8,6))\n",
+        "cm = confusion_matrix(y_test, y_pred)\n",
+        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
+        "plt.title(\"Confusion Matrix (Random Forest)\")\n",
+        "plt.xlabel(\"Predicted\")\n",
+        "plt.ylabel(\"Actual\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "# 6) Feature Importances (from the RF inside the pipeline)\n",
+        "rf_fitted = pipe.named_steps[\"model\"]              # get the RF from the pipeline\n",
+        "importances = rf_fitted.feature_importances_\n",
+        "# After one-hot, feature names expand; get names from the transformer:\n",
+        "# Build the feature names that the model sees\n",
+        "cat_feature_names = list(pipe.named_steps[\"prep\"]\n",
+        "                         .named_transformers_[\"cat\"]\n",
+        "                         .get_feature_names_out(cat_cols))\n",
+        "feature_names = num_cols + cat_feature_names\n",
+        "\n",
+        "# Sort and plot top 15\n",
+        "indices = np.argsort(importances)[::-1]\n",
+        "top_k = min(15, len(feature_names))\n",
+        "plt.figure(figsize=(10,6))\n",
+        "plt.bar(range(top_k), importances[indices[:top_k]], align=\"center\")\n",
+        "plt.xticks(range(top_k), [feature_names[i] for i in indices[:top_k]], rotation=45, ha=\"right\")\n",
+        "plt.title(\"Top Feature Importances (Random Forest)\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "i0ednziZCWJq",
+        "outputId": "cff465b2-bf1f-4642-b6d4-c89a40ca9827"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test Accuracy: 0.854\n",
+            "\n",
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.93      0.93      0.93       221\n",
+            "           1       0.81      0.89      0.85       193\n",
+            "           2       0.94      0.94      0.94        94\n",
+            "           3       0.00      0.00      0.00         1\n",
+            "           5       0.65      0.47      0.55        64\n",
+            "           6       0.76      0.79      0.77        70\n",
+            "\n",
+            "    accuracy                           0.85       643\n",
+            "   macro avg       0.68      0.67      0.67       643\n",
+            "weighted avg       0.85      0.85      0.85       643\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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WLPnjjz/Mwn5yd+QnzcvLy+z3kydPisiDMP4wMTExDw1o/6R48eJmvyd3uy1WrFia6Tdv3kzz/octo5UrV4rI38u0bNmyacqVL19etm7dmmawtNTt/zcbN26UDz74QI4cOWJ2H23KUJgsdXtFHgSZ5LZdu3ZNYmNj//X2hZMnT8rPP//80NHwr169KiIPBjhbuXKltGjRQooUKSJNmzaVLl26SPPmzR+pbZpqrINkDg4OsmHDBhERuXjxokyYMEGuXr1qFrJFRO7cuSOhoaGycOFCuXTpktn8YmJi0sw39eeTvE4lfz7JyzP1cnd1dU2z/p07d078/f3T/I2U3/eUn3NG1sWkpCSJiYn51++gra1tutuAlG1JvW7a2dlJyZIl02yPihQpInZ2dmbTTp48KcePH//X9aBr167y6aefyquvvirDhw+Xxo0bS4cOHaRTp04ZGiRRVdNdrwEgKyN0A8AzKPlK0+DBg6VZs2bplvH29hYRkQ8//FDef/99eeWVV2TcuHGSP39+sbGxkYEDBz7yFauHHSQnJiaKra1tmumpg1Py35k4ceJDH0f2uKMZp/f3Hzb9YQHwSUvd/n+ye/duef7556V+/foya9YsKVy4sOTMmVMWLlwoy5YtS1P+Ye3NaNuSkpKkSZMmMnTo0HRfL1OmjIiIFCpUSI4cOSJbt26Vr7/+Wr7++mtZuHCh9OzZM91HgSVLDpPpnegQSRsmmzVrJuXKlZN+/fqZDQb45ptvysKFC2XgwIESEBAgzs7OYjKZpFu3bumuv0/q83kcGVkXRZ7e+pgsvfUyKSlJKleuLFOmTEn3PcknDBwdHeW7776TnTt3yqZNm2TLli2yYsUKadSokWzbtu2hbUzt5s2b6Z7oAoCsjNANAE9RiRIl5JtvvpFbt26ZXe0+ceKE8XpKyVd4U/r9998lV65c/+l52yVLlhSRBwMqPewqWLLVq1fLc889J/PnzzebHh0dLQULFjR+/6erTy4uLmlGQxZ5cKUtuS7/JLk7tJOT07/W92l72DJKHhwteZn+9ttvacqdOHFCChYs+EiPBHvY57tmzRpxcHCQrVu3ir29vTF94cKFj1L9NFxdXcXJySndEdhTKlWqlNy+ffuRloednZ20adNG2rRpI0lJSTJgwACZO3euvP/++8bJndSKFy8ujo6O8scffzxSvQsXLiyDBg0yBsSrVauWiDxYf3v16mV0qRZ58Bi69NbHR5G8PE+ePGm27l67di3NCYISJUo8dLmnnFdmSblupmzLvXv35I8//nikZVuqVCn56aefpHHjxv96BdrGxkYaN24sjRs3lilTpsiHH34o7777ruzcuVMCAwP/9f3379+XCxcuyPPPP/8IrQOArIN7ugHgKWrZsqUkJibKjBkzzKZPnTpVTCaTtGjRwmx6RESE2X2nFy5ckPXr10vTpk0f+cpQegoVKiQNGzaUuXPnypUrV9K8nvIRSba2tmmuqK1atcq45ztZcnBML8yUKlVK9u3bZza688aNG+XChQuPVF9fX18pVaqUTJo0SW7fvv2P9X3a1q1bZ/ZZHDhwQPbv328sy8KFC4uPj48sXrzY7LP55ZdfZNu2bdKyZctH+jsP+3xtbW3FZDKZdfs/e/asMWp0RtnY2Ei7du1kw4YNcvDgwTSvJ68LXbp0kYiICNm6dWuaMtHR0cb9xsn3Zqecf5UqVURE/vGxVzlz5hQ/P7906/Awb775puTKlUvGjx9vTEtv/Z0+ffojjYmQnsDAQMmZM6dMnz7dbL7Jo7Wn1LJlSzlw4IBEREQY0+Li4uSTTz4RT09PqVChwmPV4UkJDAwUOzs7+fjjj83aMn/+fImJiUkz8n16unTpIpcuXZJ58+alee3OnTsSFxcnIg8ePZhacq+V5PXgn7YhIiLHjh2Tu3fvPvZTEQAgs3ClGwCeojZt2shzzz0n7777rpw9e1aqVq0q27Ztk/Xr18vAgQPNBrgSEalUqZI0a9bM7JFhIiJjxoz5z3WZOXOm1K1bVypXrix9+vSRkiVLSlRUlERERMjFixeN53C3bt1axo4dK0FBQVK7dm05evSoLF26NM0V6lKlSkm+fPlkzpw5kjdvXsmdO7f4+/uLl5eXvPrqq7J69Wpp3ry5dOnSRU6fPi2ff/55mvY+jI2NjXz66afSokULqVixogQFBUmRIkXk0qVLsnPnTnFycjLu733avL29pW7dutK/f39JSEiQsLAwKVCggFm364kTJ0qLFi0kICBAevfubTwyzNnZWUaPHv1Ifyd5QL233npLmjVrJra2ttKtWzdp1aqVTJkyRZo3by49evSQq1evysyZM8Xb21t+/vnnx2rThx9+KNu2bZMGDRoYj4G6cuWKrFq1Sr7//nvJly+fDBkyRL766itp3bq1vPzyy+Lr6ytxcXFy9OhRWb16tZw9e1YKFiwor776qvz555/SqFEjKVq0qJw7d06mT58uPj4+xr3ND9O2bVt59913JTY2VpycnP613gUKFJCgoCCZNWuWHD9+XMqXLy+tW7eWzz77TJydnaVChQoSEREh33zzzWOPR+Dq6iqDBw+W0NBQad26tbRs2VJ+/PFH+frrr816foiIDB8+XL744gtp0aKFvPXWW5I/f35ZvHix/PHHH7JmzZoM3ctsCa6urhISEiJjxoyR5s2by/PPPy+//fabzJo1S2rUqCEvvvjiv87jpZdekpUrV8prr70mO3fulDp16khiYqKcOHFCVq5cKVu3bhU/Pz8ZO3asfPfdd9KqVSspUaKEXL16VWbNmiVFixY1Bpr7p22IyIPHz+XKlUuaNGli0c8FAJ64pz9gOgA8O9J7zNOtW7d00KBB6uHhoTlz5tTSpUvrxIkTzR4/pPrg8U6vv/66fv7551q6dGm1t7fXatWqGY/8+ifpPSIrPadPn9aePXuqu7u75syZU4sUKaKtW7fW1atXG2Xu3r2r77zzjhYuXFgdHR21Tp06GhERkeZxX6qq69ev1woVKmiOHDnSPPpn8uTJWqRIEbW3t9c6derowYMHH/rIsFWrVqVb3x9//FE7dOigBQoUUHt7ey1RooR26dJFw8PDM/x5JD8iKvVjsZIf25X60UbJj6lKb56TJ0/WYsWKqb29vdarV09/+umnNHX45ptvtE6dOuro6KhOTk7apk0bPXbs2CP9bdUHjy1788031dXVVU0mk9l6NX/+fGMdKVeunC5cuNCYV0rJ61Rq6T3S7dy5c9qzZ091dXVVe3t7LVmypL7++uuakJBglLl165aGhISot7e32tnZacGCBbV27do6adIk49FXq1ev1qZNm2qhQoXUzs5Oixcvrv369dMrV66kqUdqUVFRmiNHDv3ss8/MpqdeFimdPn1abW1tjfbcvHlTg4KCtGDBgponTx5t1qyZnjhxIk2bH7Y+JK+TKb93iYmJOmbMGOM70bBhQ/3ll1/S/RxPnz6tnTp10nz58qmDg4PWrFlTN27cmO7fSL3eZ3QdTe2fPqeUZsyYoeXKldOcOXOqm5ub9u/fX2/evGlWpkGDBlqxYsV033/v3j396KOPtGLFimpvb68uLi7q6+urY8aM0ZiYGFVVDQ8P17Zt26qHh4fa2dmph4eHdu/ePc0j5/5pG+Lv768vvvjiv7YHALIak+pTHoUDAPBITCaTvP7662m6oiNrOHv2rHh5ecnEiRNl8ODBmV2dbKt3797y+++/y+7duzO7KshER44ckerVq8vhw4cfOpgiAGRV3NMNAACyrFGjRskPP/wge/bsyeyqIBONHz9eOnXqROAGYJW4pxsAAGRZxYsXl7t372Z2NZDJli9fntlVAIDHxpVuAAAAAAAshHu6AQAAAACwEK50AwAAAABgIYRuAAAAAAAsJFsMpJaUlCSXL1+WvHnzislkyuzqAAAAAACyOVWVW7duiYeHh9jYPPx6drYI3ZcvX5ZixYpldjUAAAAAAM+YCxcuSNGiRR/6erYI3Xnz5hWRB411cnLK5NoAAAAAALK72NhYKVasmJFHHyZbhO7kLuVOTk6EbgAAAADAU/NvtzgzkBoAAAAAABZC6AYAAAAAwEII3QAAAAAAWAihGwAAAAAACyF0AwAAAABgIYRuAAAAAAAshNANAAAAAICFELoBAAAAALAQQjcAAAAAABZC6AYAAAAAwEII3QAAAAAAWAihGwAAAAAACyF0AwAAAABgIYRuAAAAAAAshNANAAAAAICF5MjsCjyLPIdvyuwqZNjZ8a0yuwoAAAAAYHW40g0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIU8VuieOXOmeHp6ioODg/j7+8uBAwceWnbt2rXi5+cn+fLlk9y5c4uPj4989tlnZmVefvllMZlMZj/Nmzd/nKoBAAAAAJBl5MjoG1asWCHBwcEyZ84c8ff3l7CwMGnWrJn89ttvUqhQoTTl8+fPL++++66UK1dO7OzsZOPGjRIUFCSFChWSZs2aGeWaN28uCxcuNH63t7d/zCYBAAAAAJA1ZPhK95QpU6RPnz4SFBQkFSpUkDlz5kiuXLlkwYIF6ZZv2LChtG/fXsqXLy+lSpWSt99+W6pUqSLff/+9WTl7e3txd3c3flxcXB6vRQAAAAAAZBEZCt337t2TQ4cOSWBg4N8zsLGRwMBAiYiI+Nf3q6qEh4fLb7/9JvXr1zd7bdeuXVKoUCEpW7as9O/fX27cuPHQ+SQkJEhsbKzZDwAAAAAAWU2Gupdfv35dEhMTxc3NzWy6m5ubnDhx4qHvi4mJkSJFikhCQoLY2trKrFmzpEmTJsbrzZs3lw4dOoiXl5ecPn1aRowYIS1atJCIiAixtbVNM7/Q0FAZM2ZMRqoOAAAAAMBTl+F7uh9H3rx55ciRI3L79m0JDw+X4OBgKVmypDRs2FBERLp162aUrVy5slSpUkVKlSolu3btksaNG6eZX0hIiAQHBxu/x8bGSrFixSzeDgAAAAAAMiJDobtgwYJia2srUVFRZtOjoqLE3d39oe+zsbERb29vERHx8fGR48ePS2hoqBG6UytZsqQULFhQTp06lW7otre3Z6A1AAAAAECWl6F7uu3s7MTX11fCw8ONaUlJSRIeHi4BAQGPPJ+kpCRJSEh46OsXL16UGzduSOHChTNSPQAAAAAAspQMdy8PDg6WXr16iZ+fn9SsWVPCwsIkLi5OgoKCRESkZ8+eUqRIEQkNDRWRB/df+/n5SalSpSQhIUE2b94sn332mcyePVtERG7fvi1jxoyRjh07iru7u5w+fVqGDh0q3t7eZo8UAwAAAADA2mQ4dHft2lWuXbsmI0eOlMjISPHx8ZEtW7YYg6udP39ebGz+voAeFxcnAwYMkIsXL4qjo6OUK1dOPv/8c+natauIiNja2srPP/8sixcvlujoaPHw8JCmTZvKuHHj6EIOAAAAALBqJlXVzK7EfxUbGyvOzs4SExMjTk5OmV2df+U5fFNmVyHDzo5vldlVAAAAAIAs41FzaIbu6QYAAAAAAI+O0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQh4rdM+cOVM8PT3FwcFB/P395cCBAw8tu3btWvHz85N8+fJJ7ty5xcfHRz777DOzMqoqI0eOlMKFC4ujo6MEBgbKyZMnH6dqAAAAAABkGRkO3StWrJDg4GAZNWqUHD58WKpWrSrNmjWTq1evpls+f/788u6770pERIT8/PPPEhQUJEFBQbJ161ajzIQJE+Tjjz+WOXPmyP79+yV37tzSrFkzuXv37uO3DAAAAACATGZSVc3IG/z9/aVGjRoyY8YMERFJSkqSYsWKyZtvvinDhw9/pHlUr15dWrVqJePGjRNVFQ8PD3nnnXdk8ODBIiISExMjbm5usmjRIunWrdu/zi82NlacnZ0lJiZGnJycMtKcTOE5fFNmVyHDzo5vldlVAAAAAIAs41FzaIaudN+7d08OHTokgYGBf8/AxkYCAwMlIiLiX9+vqhIeHi6//fab1K9fX0RE/vjjD4mMjDSbp7Ozs/j7+z/SPAEAAAAAyKpyZKTw9evXJTExUdzc3Mymu7m5yYkTJx76vpiYGClSpIgkJCSIra2tzJo1S5o0aSIiIpGRkcY8Us8z+bXUEhISJCEhwfg9NjY2I80AAAAAAOCpyFDoflx58+aVI0eOyO3btyU8PFyCg4OlZMmS0rBhw8eaX2hoqIwZM+bJVhIAAAAAgCcsQ93LCxYsKLa2thIVFWU2PSoqStzd3R/+R2xsxNvbW3x8fOSdd96RTp06SWhoqIiI8b6MzDMkJERiYmKMnwsXLmSkGQAAAAAAPBUZCt12dnbi6+sr4eHhxrSkpCQJDw+XgICAR55PUlKS0T3cy8tL3N3dzeYZGxsr+/fvf+g87e3txcnJyewHAAAAAICsJsPdy4ODg6VXr17i5+cnNWvWlLCwMImLi5OgoCAREenZs6cUKVLEuJIdGhoqfn5+UqpUKUlISJDNmzfLZ599JrNnzxYREZPJJAMHDpQPPvhASpcuLV5eXvL++++Lh4eHtGvX7sm1FAAAAACApyzDobtr165y7do1GTlypERGRoqPj49s2bLFGAjt/PnzYmPz9wX0uLg4GTBggFy8eFEcHR2lXLly8vnnn0vXrl2NMkOHDpW4uDjp27evREdHS926dWXLli3i4ODwBJoIAAAAAEDmyPBzurMintNteTynGwAAAAD+ZpHndAMAAAAAgEdH6AYAAAAAwEII3QAAAAAAWAihGwAAAAAACyF0AwAAAABgIYRuAAAAAAAshNANAAAAAICFELoBAAAAALAQQjcAAAAAABZC6AYAAAAAwEII3QAAAAAAWAihGwAAAAAACyF0AwAAAABgIYRuAAAAAAAshNANAAAAAICFELoBAAAAALAQQjcAAAAAABZC6AYAAAAAwEII3QAAAAAAWAihGwAAAAAACyF0AwAAAABgIYRuAAAAAAAshNANAAAAAICFELoBAAAAALAQQjcAAAAAABZC6AYAAAAAwEII3QAAAAAAWAihGwAAAAAACyF0AwAAAABgIYRuAAAAAAAshNANAAAAAICFELoBAAAAALAQQjcAAAAAABZC6AYAAAAAwEII3QAAAAAAWAihGwAAAAAACyF0AwAAAABgIYRuAAAAAAAs5LFC98yZM8XT01McHBzE399fDhw48NCy8+bNk3r16omLi4u4uLhIYGBgmvIvv/yymEwms5/mzZs/TtUAAAAAAMgyMhy6V6xYIcHBwTJq1Cg5fPiwVK1aVZo1ayZXr15Nt/yuXbuke/fusnPnTomIiJBixYpJ06ZN5dKlS2blmjdvLleuXDF+vvjii8drEQAAAAAAWUSGQ/eUKVOkT58+EhQUJBUqVJA5c+ZIrly5ZMGCBemWX7p0qQwYMEB8fHykXLly8umnn0pSUpKEh4eblbO3txd3d3fjx8XF5fFaBAAAAABAFpGh0H3v3j05dOiQBAYG/j0DGxsJDAyUiIiIR5pHfHy8/PXXX5I/f36z6bt27ZJChQpJ2bJlpX///nLjxo2MVA0AAAAAgCwnR0YKX79+XRITE8XNzc1supubm5w4ceKR5jFs2DDx8PAwC+7NmzeXDh06iJeXl5w+fVpGjBghLVq0kIiICLG1tU0zj4SEBElISDB+j42NzUgzAAAAAAB4KjIUuv+r8ePHy/Lly2XXrl3i4OBgTO/WrZvx/8qVK0uVKlWkVKlSsmvXLmncuHGa+YSGhsqYMWOeSp0BAAAAAHhcGepeXrBgQbG1tZWoqCiz6VFRUeLu7v6P7500aZKMHz9etm3bJlWqVPnHsiVLlpSCBQvKqVOn0n09JCREYmJijJ8LFy5kpBkAAAAAADwVGQrddnZ24uvrazYIWvKgaAEBAQ9934QJE2TcuHGyZcsW8fPz+9e/c/HiRblx44YULlw43dft7e3FycnJ7AcAAAAAgKwmw6OXBwcHy7x582Tx4sVy/Phx6d+/v8TFxUlQUJCIiPTs2VNCQkKM8h999JG8//77smDBAvH09JTIyEiJjIyU27dvi4jI7du3ZciQIbJv3z45e/ashIeHS9u2bcXb21uaNWv2hJoJAAAAAMDTl+F7urt27SrXrl2TkSNHSmRkpPj4+MiWLVuMwdXOnz8vNjZ/Z/nZs2fLvXv3pFOnTmbzGTVqlIwePVpsbW3l559/lsWLF0t0dLR4eHhI06ZNZdy4cWJvb/8fmwcAAAAAQOYxqapmdiX+q9jYWHF2dpaYmBir6GruOXxTZlchw86Ob5XZVQAAAACALONRc2iGu5cDAAAAAIBHQ+gGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALOSxQvfMmTPF09NTHBwcxN/fXw4cOPDQsvPmzZN69eqJi4uLuLi4SGBgYJryqiojR46UwoULi6OjowQGBsrJkycfp2oAAAAAAGQZGQ7dK1askODgYBk1apQcPnxYqlatKs2aNZOrV6+mW37Xrl3SvXt32blzp0REREixYsWkadOmcunSJaPMhAkT5OOPP5Y5c+bI/v37JXfu3NKsWTO5e/fu47cMAAAAAIBMZlJVzcgb/P39pUaNGjJjxgwREUlKSpJixYrJm2++KcOHD//X9ycmJoqLi4vMmDFDevbsKaoqHh4e8s4778jgwYNFRCQmJkbc3Nxk0aJF0q1bt3+dZ2xsrDg7O0tMTIw4OTllpDmZwnP4psyuQoadHd8qs6sAAAAAAFnGo+bQDF3pvnfvnhw6dEgCAwP/noGNjQQGBkpERMQjzSM+Pl7++usvyZ8/v4iI/PHHHxIZGWk2T2dnZ/H393/oPBMSEiQ2NtbsBwAAAACArCZDofv69euSmJgobm5uZtPd3NwkMjLykeYxbNgw8fDwMEJ28vsyMs/Q0FBxdnY2fooVK5aRZgAAAAAA8FQ81dHLx48fL8uXL5cvv/xSHBwcHns+ISEhEhMTY/xcuHDhCdYSAAAAAIAnI0dGChcsWFBsbW0lKirKbHpUVJS4u7v/43snTZok48ePl2+++UaqVKliTE9+X1RUlBQuXNhsnj4+PunOy97eXuzt7TNSdQAAAAAAnroMXem2s7MTX19fCQ8PN6YlJSVJeHi4BAQEPPR9EyZMkHHjxsmWLVvEz8/P7DUvLy9xd3c3m2dsbKzs37//H+cJAAAAAEBWl6Er3SIiwcHB0qtXL/Hz85OaNWtKWFiYxMXFSVBQkIiI9OzZU4oUKSKhoaEiIvLRRx/JyJEjZdmyZeLp6Wncp50nTx7JkyePmEwmGThwoHzwwQdSunRp8fLykvfff188PDykXbt2T66lAAAAAAA8ZRkO3V27dpVr167JyJEjJTIyUnx8fGTLli3GQGjnz58XG5u/L6DPnj1b7t27J506dTKbz6hRo2T06NEiIjJ06FCJi4uTvn37SnR0tNStW1e2bNnyn+77BgAAAAAgs2X4Od1ZEc/ptjye0w0AAAAAf7PIc7oBAAAAAMCjI3QDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFvJYoXvmzJni6ekpDg4O4u/vLwcOHHho2V9//VU6duwonp6eYjKZJCwsLE2Z0aNHi8lkMvspV67c41QNAAAAAIAsI8Ohe8WKFRIcHCyjRo2Sw4cPS9WqVaVZs2Zy9erVdMvHx8dLyZIlZfz48eLu7v7Q+VasWFGuXLli/Hz//fcZrRoAAAAAAFlKhkP3lClTpE+fPhIUFCQVKlSQOXPmSK5cuWTBggXplq9Ro4ZMnDhRunXrJvb29g+db44cOcTd3d34KViwYEarBgAAAABAlpKh0H3v3j05dOiQBAYG/j0DGxsJDAyUiIiI/1SRkydPioeHh5QsWVJeeOEFOX/+/EPLJiQkSGxsrNkPAAAAAABZTYZC9/Xr1yUxMVHc3NzMpru5uUlkZORjV8Lf318WLVokW7ZskdmzZ8sff/wh9erVk1u3bqVbPjQ0VJydnY2fYsWKPfbfBgAAAADAUrLE6OUtWrSQzp07S5UqVaRZs2ayefNmiY6OlpUrV6ZbPiQkRGJiYoyfCxcuPOUaAwAAAADw73JkpHDBggXF1tZWoqKizKZHRUX94yBpGZUvXz4pU6aMnDp1Kt3X7e3t//H+cAAAAAAAsoIMXem2s7MTX19fCQ8PN6YlJSVJeHi4BAQEPLFK3b59W06fPi2FCxd+YvMEAAAAAOBpy9CVbhGR4OBg6dWrl/j5+UnNmjUlLCxM4uLiJCgoSEREevbsKUWKFJHQ0FAReTD42rFjx4z/X7p0SY4cOSJ58uQRb29vEREZPHiwtGnTRkqUKCGXL1+WUaNGia2trXTv3v1JtRMAAAAAgKcuw6G7a9eucu3aNRk5cqRERkaKj4+PbNmyxRhc7fz582Jj8/cF9MuXL0u1atWM3ydNmiSTJk2SBg0ayK5du0RE5OLFi9K9e3e5ceOGuLq6St26dWXfvn3i6ur6H5sHAAAAAEDmMamqZnYl/qvY2FhxdnaWmJgYcXJyyuzq/CvP4ZsyuwoZdnZ8q8yuAgAAAABkGY+aQzN8pRt4FJxYAAAAAIAs8sgwAAAAAACyI0I3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFjIY4XumTNniqenpzg4OIi/v78cOHDgoWV//fVX6dixo3h6eorJZJKwsLD/PE8AAAAAAKxBhkP3ihUrJDg4WEaNGiWHDx+WqlWrSrNmzeTq1avplo+Pj5eSJUvK+PHjxd3d/YnMEwAAAAAAa5Dh0D1lyhTp06ePBAUFSYUKFWTOnDmSK1cuWbBgQbrla9SoIRMnTpRu3bqJvb39E5knAAAAAADWIEOh+969e3Lo0CEJDAz8ewY2NhIYGCgRERGPVQFLzBMAAAAAgKwgR0YKX79+XRITE8XNzc1supubm5w4ceKxKvA480xISJCEhATj99jY2Mf62wAAAAAAWJJVjl4eGhoqzs7Oxk+xYsUyu0oAAAAAAKSRodBdsGBBsbW1laioKLPpUVFRDx0kzRLzDAkJkZiYGOPnwoULj/W3AQAAAACwpAyFbjs7O/H19ZXw8HBjWlJSkoSHh0tAQMBjVeBx5mlvby9OTk5mPwAAAAAAZDUZuqdbRCQ4OFh69eolfn5+UrNmTQkLC5O4uDgJCgoSEZGePXtKkSJFJDQ0VEQeDJR27Ngx4/+XLl2SI0eOSJ48ecTb2/uR5gkAAAAAgDXKcOju2rWrXLt2TUaOHCmRkZHi4+MjW7ZsMQZCO3/+vNjY/H0B/fLly1KtWjXj90mTJsmkSZOkQYMGsmvXrkeaJwAAAAAA1sikqprZlfivYmNjxdnZWWJiYqyiq7nn8E2ZXYUMOzu+VYbKPwttBAAAAPDsetQcapWjlwMAAAAAYA0I3QAAAAAAWAihGwAAAAAACyF0AwAAAABgIYRuAAAAAAAshNANAAAAAICFELoBAAAAALAQQjcAAAAAABZC6AYAAAAAwEII3QAAAAAAWAihGwAAAAAACyF0AwAAAABgIYRuAAAAAAAshNANAAAAAICFELoBAAAAALAQQjcAAAAAABZC6AYAAAAAwEII3QAAAAAAWAihGwAAAAAACyF0AwAAAABgIYRuAAAAAAAshNANAAAAAICFELoBAAAAALAQQjcAAAAAABZC6AYAAAAAwEII3QAAAAAAWAihGwAAAAAACyF0AwAAAABgIYRuAAAAAAAshNANAAAAAICFELoBAAAAALAQQjcAAAAAABZC6AYAAAAAwEII3QAAAAAAWAihGwAAAAAACyF0AwAAAABgIYRuAAAAAAAshNANAAAAAICF5HicN82cOVMmTpwokZGRUrVqVZk+fbrUrFnzoeVXrVol77//vpw9e1ZKly4tH330kbRs2dJ4/eWXX5bFixebvadZs2ayZcuWx6ke8FR4Dt+U2VV4LGfHt8rsKgAAAADPjAxf6V6xYoUEBwfLqFGj5PDhw1K1alVp1qyZXL16Nd3ye/fule7du0vv3r3lxx9/lHbt2km7du3kl19+MSvXvHlzuXLlivHzxRdfPF6LAAAAAADIIjIcuqdMmSJ9+vSRoKAgqVChgsyZM0dy5colCxYsSLf8tGnTpHnz5jJkyBApX768jBs3TqpXry4zZswwK2dvby/u7u7Gj4uLy+O1CAAAAACALCJD3cvv3bsnhw4dkpCQEGOajY2NBAYGSkRERLrviYiIkODgYLNpzZo1k3Xr1plN27VrlxQqVEhcXFykUaNG8sEHH0iBAgUyUj0AT9iz0IX+WWgjAAAAMk+GQvf169clMTFR3NzczKa7ubnJiRMn0n1PZGRkuuUjIyON35s3by4dOnQQLy8vOX36tIwYMUJatGghERERYmtrm2aeCQkJkpCQYPweGxubkWYAAAAAAPBUPNZAak9at27djP9XrlxZqlSpIqVKlZJdu3ZJ48aN05QPDQ2VMWPGPM0qAgAAAACQYRm6p7tgwYJia2srUVFRZtOjoqLE3d093fe4u7tnqLyISMmSJaVgwYJy6tSpdF8PCQmRmJgY4+fChQsZaQYAAAAAAE9FhkK3nZ2d+Pr6Snh4uDEtKSlJwsPDJSAgIN33BAQEmJUXEdm+fftDy4uIXLx4UW7cuCGFCxdO93V7e3txcnIy+wEAAAAAIKvJ8OjlwcHBMm/ePFm8eLEcP35c+vfvL3FxcRIUFCQiIj179jQbaO3tt9+WLVu2yOTJk+XEiRMyevRoOXjwoLzxxhsiInL79m0ZMmSI7Nu3T86ePSvh4eHStm1b8fb2lmbNmj2hZgIAAAAA8PRl+J7url27yrVr12TkyJESGRkpPj4+smXLFmOwtPPnz4uNzd9Zvnbt2rJs2TJ57733ZMSIEVK6dGlZt26dVKpUSUREbG1t5eeff5bFixdLdHS0eHh4SNOmTWXcuHFib2//hJoJAAAAAMDT91gDqb3xxhvGlerUdu3alWZa586dpXPnzumWd3R0lK1btz5ONQAAAAAAyNKyxOjlAADL4VnkAAAAmYfQDQCwepxYAAAAWRWhGwAAK8CJBQAArFOGRy8HAAAAAACPhtANAAAAAICFELoBAAAAALAQQjcAAAAAABZC6AYAAAAAwEII3QAAAAAAWAihGwAAAAAACyF0AwAAAABgIYRuAAAAAAAshNANAAAAAICFELoBAAAAALAQQjcAAAAAABaSI7MrAAAAICLiOXxTZlfhsZwd3yqzqwAAyMK40g0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIXkyOwKAAAAPEs8h2/K7Cpk2NnxrTK7CgBgtQjdAAAAeKKehRMLz0IbATwZhG4AAAAAaVjjiQURTi4g6yF0AwAAAHgmPQsnFp6FNmZ1DKQGAAAAAICFELoBAAAAALAQQjcAAAAAABZC6AYAAAAAwEII3QAAAAAAWAihGwAAAAAACyF0AwAAAABgIYRuAAAAAAAshNANAAAAAICFPFbonjlzpnh6eoqDg4P4+/vLgQMH/rH8qlWrpFy5cuLg4CCVK1eWzZs3m72uqjJy5EgpXLiwODo6SmBgoJw8efJxqgYAAAAAQJaR4dC9YsUKCQ4OllGjRsnhw4elatWq0qxZM7l69Wq65ffu3Svdu3eX3r17y48//ijt2rWTdu3ayS+//GKUmTBhgnz88ccyZ84c2b9/v+TOnVuaNWsmd+/effyWAQAAAACQyTIcuqdMmSJ9+vSRoKAgqVChgsyZM0dy5colCxYsSLf8tGnTpHnz5jJkyBApX768jBs3TqpXry4zZswQkQdXucPCwuS9996Ttm3bSpUqVWTJkiVy+fJlWbdu3X9qHAAAAAAAmSlHRgrfu3dPDh06JCEhIcY0GxsbCQwMlIiIiHTfExERIcHBwWbTmjVrZgTqP/74QyIjIyUwMNB43dnZWfz9/SUiIkK6deuWZp4JCQmSkJBg/B4TEyMiIrGxsRlpTqZJSojP7CpkWEY/W9qYdWWknbQx66KN5mhj1kUb07LGdtLGtGhj1sV2x9yz0MbMklxHVf3HchkK3devX5fExERxc3Mzm+7m5iYnTpxI9z2RkZHplo+MjDReT572sDKphYaGypgxY9JML1as2KM1BBnmHJbZNbC8Z6GNIs9GO2lj9kAbswfamD3QxuzhWWijyLPRTtqYtdy6dUucnZ0f+nqGQndWERISYnb1PCkpSf78808pUKCAmEymTKxZ5oqNjZVixYrJhQsXxMnJKbOrYxG0MXugjdkDbcweaGP28Cy0UeTZaCdtzB5o47NBVeXWrVvi4eHxj+UyFLoLFiwotra2EhUVZTY9KipK3N3d032Pu7v7P5ZP/jcqKkoKFy5sVsbHxyfdedrb24u9vb3ZtHz58mWkKdmak5NTtl/xaWP2QBuzB9qYPdDG7OFZaKPIs9FO2pg90Mbs75+ucCfL0EBqdnZ24uvrK+Hh4ca0pKQkCQ8Pl4CAgHTfExAQYFZeRGT79u1GeS8vL3F3dzcrExsbK/v373/oPAEAAAAAsAYZ7l4eHBwsvXr1Ej8/P6lZs6aEhYVJXFycBAUFiYhIz549pUiRIhIaGioiIm+//bY0aNBAJk+eLK1atZLly5fLwYMH5ZNPPhEREZPJJAMHDpQPPvhASpcuLV5eXvL++++Lh4eHtGvX7sm1FAAAAACApyzDobtr165y7do1GTlypERGRoqPj49s2bLFGAjt/PnzYmPz9wX02rVry7Jly+S9996TESNGSOnSpWXdunVSqVIlo8zQoUMlLi5O+vbtK9HR0VK3bl3ZsmWLODg4PIEmPjvs7e1l1KhRabreZye0MXugjdkDbcweaGP28Cy0UeTZaCdtzB5oI1Iy6b+Nbw4AAAAAAB5Lhu7pBgAAAAAAj47QDQAAAACAhRC6ATxR3LECAAAA/I3QDeCJSUhIEJPJlNnVAAAAALIMQjeAJyI4OFh69+4tSUlJXO0GAAAA/l+GHxmGp0dVJSkpSWxtbTO7KhaXlJRkPGpOVblaamXmz58vCxculB9++EFsbGwkKSmJZQjA4lLvL1LuSwAAyCrYM2Vh586dMwL3kiVL5ODBg5lcI8u4f/++cZD0119/Edas0M2bN8XHx0e8vb1l586dsmjRIklKSsrsallE8lX87Ho1P7ldt2/fzuSaAP/sxo0bxv5i6dKlIiLZOnBn121qStl1u/qsSG//mJ2WaXZqy8M8C9uZzJJ9905W7scff5TSpUvLhg0bZPjw4RIcHCyurq6ZXa0nbtOmTbJv3z4REXn77bflpZdeytYbteS2ZbeNmouLi9y4cUOCgoKkcePGUqRIkWx38Ju87OLi4iQxMVHi4+NFJHsty+Srhlu3bpX33ntPvv3228yu0lOVHbc933//vezcuVO2bduW2VV5or7++mupV6+enDt3TgYNGiT9+vWT8+fPZ3a1nrjkdXL//v2ycuVKiY6OztwKWdAPP/wgX3/9tdy8eTOzq/LEZaf9xMOk7OF29+5d41Yzk8kkiYmJmVy7/y5l+6KiouTkyZOZXKMnL2VPoYiICPn++++fueMAS6J7eRbl6uoqQ4YMkW7duomdnZ0cO3ZMChcunK26zqmqjBo1SiIjI6VOnTqyfft2+e6777Ltle6YmBgREXF2ds42yzBZ7969Zfny5bJixQrp1KmTBAYGikj2uVUguR2bN2+WefPmyZUrV6Rw4cIyYMAAadKkSWZX74kxmUzy5ZdfSo8ePWTkyJFSoECBzK7SE5e8LA8fPiynT5+We/fuSc2aNaV06dJiMpmy1TY2JCREVq1aJXnz5pVLly5JvXr1JDQ0VMqUKZPZVfvPGjduLAkJCVKnTh25deuW7NmzR4oXL56tll/yurpmzRp59dVXZdCgQVKpUiXJly9fZlftiVuzZo306dNHgoODpVSpUuLi4pLZVXpiUq6TCxYskFOnTsmZM2fkjTfeyDbLM2Ubw8LC5Ntvv5Xbt2+Lp6enhIWFSe7cuTO5hv+NqhrtGzlypGzbtk2OHTsmjRo1En9/fxkyZIjkyGHdkSplG0eMGCGrVq2SXLlyycWLF6VFixYyduxYKVmyZCbX0sopsqwZM2aoyWTS3Llz69q1a43pSUlJmVirJ8/Dw0Nz5sypCxcuzOyqWMzo0aO1WrVqWr58ee3YsaMeOXJE//rrr8yu1hORkJCgd+7c0Tx58miDBg20SpUq+tFHH+n169dVNfusr+vXr1cHBwcNDQ3VZcuW6QsvvKAmk0l/++23zK7aE3Py5EktXbq0zp4922x6dlmGyVavXq3Ozs5aq1YtdXBwUD8/P3333XeN17NDe6dNm6aurq76ww8/qKrqxx9/rCaTSffs2ZPJNfvv7t27p6qqISEhajKZtFy5cvrbb7/p/fv3VTV7LL9k+/bt0/z58+vcuXOzzT4jtb1792q+fPl03rx5eufOncyujsUMGTJEixQpon379tWXX35ZbWxsdPz48RofH5/ZVXtihg0bpq6urjpjxgydP3++urm5ac2aNTUhISGzq/ZEjBs3Tl1dXXXDhg16+fJlbdSokZYsWVJ/+umnzK7aEzN16lR1dXXVAwcOqKrqhAkT1GQyaURERCbXzPoRurOQxMREVf37gOGPP/7Q77//XocPH6558+bVpUuXmpWzdnfu3NHIyEj18fHRypUrq7e3t3777bdG+1K205oPombMmKH58uXTqVOn6uzZs7VKlSpaqVIl3bBhQ7ZZlqqqMTExqqr61ltvacWKFXXChAl648aNTK7Vk3H79m1t2bKlTpw4UVVVL126pCVKlNC+fftmcs2erIiICPXy8tKjR48a06z5u5eeo0ePqpubm86dO9fYBo0YMUJr1qypI0eOzOzqPTF9+/bVSZMmqarqihUrNF++fMbJFGsNNqnXxR07duihQ4e0fPny6uPjo4cOHUp3fbXG7WxyOyZMmKCBgYFmgdsa2/NPPvzwQ23atKlx0kQ1+7Vx06ZNWqxYMT1y5Iiqqh48eFBNJpOuXLkyk2v25Pz6669auXJl/fbbb1X1wYlqJycnnTlzplk5a9ynJCUlaVRUlNatW1dXr16tqg+2P7ly5dJPP/1UVR+cDLTGtqUWFBSkYWFhqqq6cuVKzZcvn86aNUtVVe/evZuZVbN6hO4sIuUX9dSpU3ry5Enj9/Pnz+vAgQM1b968unz5cmP6xIkTre7s2j+dqff399eSJUvqt99+a7bzteYzpNu3b9ePP/5YV6xYYTa9adOmWrFiRb1y5YqqWtdOKCkpyajv5cuX9eLFi3rt2jWzMtkteP/555/q6emp+/bt06tXrxpXK5ItWbJET58+nYk1fDzJyzH5Ssv69evV1dVVL168qKrm3739+/frzp07n3odn7Qvv/xSS5cubfTEUFWNiorSIUOGaK1atfTy5cuZWLsnIyEhQatWrapz5szRvXv3ap48eYzA/ddff+mQIUPMek9Zg5T7jrNnz2p0dLSxn7h165aWKVNGfXx8jFCjqjp58uSnXs//Kvk7mRw6Bw0apPXr1zd7Ldn+/fv15s2bT7V+lhAUFKRNmzZVVfP9i6rqkSNH9M8//8ysqj0xS5cu1bZt26qq6rJlyzRv3rxGkImNjbXK/Udq3377rRYrVkxVVb/66ivNkyePzpkzR1UffEcXLVpkdmyX1aX+vt28eVP9/Pz06tWrun79erPt6p07d3Tx4sVmJ6ytQeqTW3fu3NHy5cvr4sWLdc+ePWn2HYMHD9bNmzdnRlWzBUJ3Jhs5cqReunTJ+H348OFavHhxdXNz08DAQD137pyqql68eFEHDRqk9vb2GhISooGBgVquXDmr2YCdP3/e7PfPPvtMhw4dql988YWeOHHCmO7v76+lS5fW7du3a3R0tLZp00b79ev3tKv7RBw6dEhz5sypJpNJFy1apKp/X2G6f/++Fi1aVN97773MrGKGxMbGqurfO6L169dr5cqVtXz58urq6qqff/65WcB+++231cfHR0ePHm11B02pd7b37t3THj166Pjx47V48eLar18/47sXFRWlL730ki5btsyqTp4k++abb7Rnz56q+uCKftGiRbVLly5pyg0cOFBHjBhhFSfB/ukq2fbt29XT09M4YZlc9uzZs2oymXT9+vVPpY6WkPJ7FhYWptWrV1c7OztdsGCBMf3mzZvarFkz/fDDDzOjihn26aefmu0j3n33Xa1ataoWLVpUx4wZoz/++KOqqsbFxWnZsmWNkw3NmzdXLy8vq9lHprRjxw79/vvvVVV19uzZmjNnTv3555/NysTHx+s777yj69aty4wq/mfnzp0zTqTMmjVLHR0djWWZvB2NiYnRoUOH6jfffJNZ1Xws6W1/wsLC1N/fX7/++mt1cnIyAreq6uLFi/XVV181eotZg/T2dWfOnNGWLVvqhAkTNE+ePDp37lzjtf3792vnzp2NZZzVpVyGt2/fVlXV6OhoLVeunHbq1EldXFzMbsM6ceKEBgYG6oYNG556XZ+Eq1evGst0/Pjx6ufnp/b29mb7jj///FObNGmiEyZMyKxqWj1Cdya6fPmyOjg4aKNGjfTatWu6cuVK9fT01FWrVunatWu1SpUqWrFiRePM2dWrV3XChAnq5+enXbp0Me5ry+rdsAYPHqwdO3bUX375RVVVR4wYofny5dO6detqwYIFtXv37hoeHm6Ur1evnhYpUkTLli2rlSpVMtppTZKSkvTWrVs6Y8YMdXNzM0KN6t/3I7Zu3VrffvvtTKphxvTp00eDgoKMg6QNGzZo3rx5dfLkyXrmzBkdPHiw8XvKK4i9e/fWgIAAq7zavXXrVh02bJjx+6BBg9RkMmmrVq3MulgNHz5cy5UrZ5wgsza7du1SW1tb3bp1q6qqrlmzRvPnz6/t27fXY8eOaUREhA4fPlydnZ31119/zeTa/rvk7eHp06f1448/1kGDBunq1auNQHrmzBktVKiQvvnmmxoXF2e87/r161q9enXdvn17ptT7v/rss8+0RIkSRkD9/vvvtU6dOlqrVi3jvu4LFy5oy5YttVatWlYRRjds2KBFihTRt956Sy9duqSrV69Wd3d3Xb58uQ4aNEj9/Py0e/fuun//flV9EEQbNWqk9erV08DAQKvZR6bWvHlz9fHxUdUH+5KWLVuqu7u7/vjjj/rXX39pXFycjhgxQt3d3fWPP/7I3Mo+hnXr1qm/v79OmjRJExMT9dy5c9qqVSutUqWKHjp0SFUfLMv33ntPixQpomfPns3kGj+6lOvaN998owcPHlTVBxce/P391WQy6bRp04wy8fHx2qZNG33llVes8qTt+PHjddeuXaqqeu3aNa1bt66aTCYdPXq0USY+Pl5btGih7du3t4rvYso6Tp06Vd944w2j99fatWs1X7582qFDB1V9cAHl9u3b2qpVK23cuLFVbFdTmz9/vhYuXNjoJbRr1y6tWbOm1qlTxzhuv3z5srZs2VIDAgKsso1ZBaE7k/3+++9asmRJbdGihc6ZM8fszNmtW7e0evXqWqFCBbMuK7dv3zY2ztYwsMrMmTPVz89Pe/furZs2bdKOHTsaAzJs2LBBGzZsqG3btjUL3osWLdLFixcb7bOGdiZbsGCBfvbZZ3rz5k2NjY3VmTNnqqOjoxGwk5dd1apVdciQIZlY00fzxRdfqKurq3GG+saNG9q2bVsNDQ1V1QdXLLy9vbV69epqMpn0o48+0qioKOP9kZGRmVHt/yQxMVFnz56tJpNJQ0JCjOkdOnRQDw8PHTRokP7vf//TV155RZ2dna3m7H1qiYmJevPmTe3YsaO+8847qvpgu/PNN99omTJl1MPDQ728vLRy5cp6+PDhTK7tv0s+WPr555/Vzc1N27Ztq2XKlFE/Pz8dNmyY0dNk06ZNamtrqwMGDNA9e/bo+fPnNSQkRN3d3dP0yrEGa9asMQberFevnp46dcqY3qBBA3V3d9dy5cpptWrV1N/f3wij1nDwlHzFftiwYTpo0CD97LPPjNeWLVum9erV065duxrB+6+//tLLly9b1T4ytd27d6uvr69u2rRJVVV/+ukn7dSpk+bMmVN9fHzU399f3d3dreI7mdqGDRvU3t5eZ86cqceOHTOm79q1Szt37qx2dnZaq1YtrVWrlhYqVMiq2pgyNA8bNkzLlSunixcv1ujoaL17966GhYVp5cqV9cUXX9Rff/1VN23apC1atNAqVaoY66k1Be/Y2Fht27atmkwm3b17t6o+uD2ycOHC+txzz+no0aN17ty5+txzz2nlypWt4iRYys9/8ODB6u7urgsWLDC6///55586duxYNZlM2qZNG+3YsaM2bNjQrH3WsF1N6fbt21q1alWtXLmyEbK/+OILrVOnjhYuXFirVKmi1atX1xo1alhtG7MKQncWkBy8TSaTvv/++6r69xf/9u3b6uvrq5UrV04zSExW3zinrN/ixYu1Zs2a2q1bN23ZsqXZFaZNmzbpc889p+3atdMdO3akmY81fbkHDx6sbm5u+umnnxq3DURHR+uMGTPUwcFBGzZsqC+99JJ27txZS5cubRUHhBMmTNBy5cqp6oP7tAYNGqSffvqpRkZGalRUlJYvX1579+6tqqr9+/dXFxcXHTdunFVe3U65zt6+fVs/+eQTzZEjhw4ePNiYPmTIEH3++efV19dXX3nlFWMnZQ2S25d6MJRZs2Zpnjx5zMaSSEhI0P379+svv/yiV69efar1/C/OnTunZcqU0ZCQEKO9o0eP1qpVq5p13/z666+1ePHiWrRoUfX29lZPT0/jKps1GTZsmHp4eOiUKVP09ddfV29vb61QoYJxkHjixAndtGmThoWF6YYNG4ztaVbf9qSs38SJE9XX11cLFiyo8+bNMyv3xRdfaL169bRHjx66d+9es9ey8sH9P7l586bWrVtXg4KCzKavWrVKp0yZonPmzNEzZ85kUu0eX0xMjDZt2tTsKmhKN27c0KVLl+r777+v06dPt9r7nMeNG6eFChXSXbt2md2Oc/fuXZ0zZ45Wr15dHRwc1NfXV59//nmrCTKpB/tVfdB7plevXmpnZ2dc8T5+/Li+8MILWqVKFQ0MDNRXX301y19ASb59LtmyZcvUw8PD6CWk+mD5JPfk27Fjh/bo0UP79++v48ePz/LtS5Z6m5i8LOPi4tTX11fLli1r9Gj79ddfddWqVTpx4kRdu3at1ew7sjJCdyZIucFK3tiePHlSK1asqH5+fkY3lpTBu2jRovriiy8+/cr+B6lPCsybN09Lly6thQoVSnNlcPPmzRoYGKj16tWz2quGS5Ys0cKFC5ttpJNFR0frzJkztWjRolqxYkWzNmb1DdiBAwe0bNmy+txzzxn3vCbveMaOHatNmzY1uu6OHj1aixYtqvnz5zfrZm4tUndjjIuL0zlz5miOHDnMeiXcvXtX7969m+UPktLz7bff6uuvv57mPsnAwEDt2bOn3r17N8uf0HuYxMREnTVrlrZr104vX75sdt+9m5ub7tu3zyin+mAU+gMHDujOnTutcgC1X3/9Vd3d3fWrr74ypp05c0arVaumFStWNDuJklJWX2/TW/9mzZqlRYsW1TZt2qRp14oVK7RcuXLGSWtrc/jwYV2zZo3ZtPDwcHVycspWgxZdvXpVixcvrp988km6r2f19TI9S5YsMbsF7tKlS+rn56dffPGFqj7olrt7924dNGiQWU/GH3/8Ua9du2aVPTJSPw70woUL+tJLL6mdnZ0xcvmdO3c0Li7O7KRDVm1j3759dfTo0WYDwo4aNUpbtmypqqq//PKLhoWFaYUKFbRQoULG7QGpA6w1rb+LFy829nkpg3f16tW1fPnyevTo0XS3w9bUxqyI0P2UpfyShoaG6qxZs4yrL7///ruWKFFCn3vuOaNLbvJKf+fOHata2VN+WVM+F3bp0qVasWJF7dWrV5pRHteuXatvvvmm1V6deOedd7RTp05mbU89GMesWbPU1dXV7MqpNbR3wIABajKZNCAgwJiWlJSkQUFB2qlTJ2NnGhwcrDt27LDKEXUvXLigLi4uxmPBkt26dUunTp2qJpNJx48fn0m1+29SrpMLFizQpk2bqqOjo/bp08c42J83b57Wrl1bL1y4oKrWsV6mZ/ny5caIucmuX7+uBQoUsLoBmf7NwYMH1cXFRY8fP66qfy+zo0ePqouLizZs2NDoam4tyzPlurps2TKz+1+nTZumVatW1bfeeivNVdBvvvnGqvaRqg/aevv2bX3uuee0VKlS2rBhQ/3uu++Mp1q0b99eBw8erImJiVk2sDyKH3/8Uc+fP68xMTHq6+ubZhur+mBd/uijj6yqnXv27FGTyaRDhw416h0TE6N16tTR9957Tzdu3KjdunXTmjVrau3atdXJyUmHDx+uqubfx6z+3UxZv3Xr1qmDg4Nxa0Dy9/X8+fParl07zZUrl3Efe0pZ+UTua6+9pp6enjp58mTj1rglS5aoq6ur9uzZUytUqKDdunXT0NBQ/fDDD9VkMpn1NMnKbUuWchlGR0erm5ub1qlTJ03WiIqKUg8PD23cuHG6F5Dw3xC6n6KUK/3Vq1e1QYMGWrBgQV28eLExOmJy8G7UqFGaL4OqdZxlSj2QSKVKlcwOnObPn6/Vq1fX3r17P/TxCll9J5RaYmKitm7dWjt27Kiq5o89+euvv3Tnzp0aExNj3OPt5uamr732WmZW+ZElD0706quvaoUKFfSFF14wXgsNDVV7e3sdNGiQduvWTfPmzWsEgKzu/PnzumrVKlV90EW1R48e+v7772v+/Pn1448/Nit75swZLVy4cJoBYrK6lOvh7t27dc6cOcaVh2+++UabNGmiFStW1NatW+uWLVs0d+7cxkGhtUq57Ui57axUqZJx36Hqg6uj1tRtPj337t3TYsWKpRkb4ubNm1qzZk11dnbWatWqGdOz+sFhymV34MABDQwM1IoVK+rSpUuN6ZMnT9Zq1aqlG7xVrWMfmdqlS5f0yJEj2qRJE61WrZrWrVtX9+7dq6GhoVqkSBGrHaRR9cEj+jw8PIwndbz22mtasGBB3bt3r9n6OGLECA0MDLS6J12sXLlS7e3tdciQIfrXX39pYmKivvnmmxoQEKC2trY6ZMgQ3bFjhyYmJmrPnj2NsTOs0ebNm3XdunXaunVr9fT0THOyb+XKlWoymdRkMlnF42xTrn/Dhw/XEiVK6KRJk/TmzZt67do1nTZtmjZs2FDnzJljnLw8fPiw1qlTx+rG/khua0hIiE6aNElPnTqlpUuX1gYNGhgn+VQfXGRI7tXYo0ePzKputkXozgTBwcFau3Zt7dKli5YuXVodHR11/vz5ZsG7ZMmSWrlyZavbAaU8aFq6dKn27dtXCxQooF5eXjpjxgzjtU8//VR9fX21T58+VtudXPXBCMkpz4w6OjqmuZp24cIF7dGjh3G/U3R0tE6aNElLliypUVFRWf5AWFWNe/Dnz5+vZcuW1e7duxuvhYSEaJ06dbR58+ZWsaNVfRBWunXrprVr1zZGJV+yZIneuHFDR48erXnz5jUL3jExMfrKK6+keXxRVpX6/rTVq1ers7OzhoSEmA1MdOPGDT1w4IA2atTIGHW2YsWKeuvWradd5cf2sO9P6itJ5cqVM8aMSH6CgjXeF7t9+3b98ssvjedsh4aGas2aNXXSpElGmfj4eH3xxRd19+7dWrRoUbPBAK3B8OHDtW3btlqvXj11cnLSihUr6qeffmq8PmXKFPXz89OePXuaPXIzq0t5Euzy5ct66dKlNCd+Nm7cqAMGDNA8efJot27d1GQy6aBBgzKjuv/Zxo0b1dHRUefNm2f0oFFV7dSpk7q6uuqoUaN0/Pjx2qdPH82bN6/V7D9SW7FihebIkcMI1H/99Zf+9ttvaZ72UL9+fX333Xczo4qPJeV4JSNHjtSKFSvq8ePH9fDhw9qqVSstVqyY2WB4u3fv1r59++rUqVOtpsdCypN0w4YN0+LFi+ukSZOMfWjy+CeJiYl6584dbdmypTZp0sRqLgzNmzfPWI5fffWVlipVyuh9eurUKfXy8tIGDRoYg08mJiZq37599eTJk1Z5AjOrI3Q/ZcuXL1cnJyc9fPiw3r59WxMSEnTAgAFqZ2en8+fPNw52jx07ph06dLCalT71BigkJERdXV119uzZOmvWLPX391dfX1+dMmWKUWb+/PlarFgxYxRsaxMSEqIVK1bUAgUK6JAhQ3TVqlXat29fLV26tG7cuFFv3bqlf/zxh7Zq1Upr1qxpthOKiYmxuhMqqg/Ogi5YsCBN8E4endWa3Lx503iES8peBxcvXtTRo0dr7ty59YMPPtCffvpJQ0JC1N/f3yoGh+vTp4++8sorxrbj4MGDWrBgwTRdrlOH1S1btujIkSOtamA41b/b8dNPP2l4eLjxZISUr0dHRxsDG40fP17t7e3T7QKZ1Q0fPlyLFCmi1apVUwcHB+Pe/IEDB2qFChW0ffv2OnHiRK1bt67WqFFD79y5o02bNtU+ffpkdtX/Ucr9x4IFC9TJyUkjIiI0JiZGf/zxR23Xrp3WqlVLFy5caJQbO3asvvzyy1Zx8Jt8AJ+8rq5fv14rV66s5cuX14IFC+rnn3+eZtuyY8cOHTZsmFapUsUqHtWX2p07d7Rz5846YsQIVX1w4vb333/XSZMm6bZt27RTp07apk0brVSpkrZv3z7Nc8itTXLwDg4ONlsnb926pUePHtVmzZqZjVKe1c2ZM0e9vb01NjZWz5w5o926ddMtW7YYr//444/aunVrdXd312+++UZ//fVXbd++vdm+NCu39WG3AQ4ZMsQI3sknxG7fvq3Lly/Xhg0bqo+Pj1WMwq6q+sknn6jJZNI9e/boN998o/369dNx48ap6t/L5uTJk1qyZEktV66c9uvXT+vXr68+Pj5G26wlg1gLQrcFjRkzJs1VsZkzZxoHQym/sK+++qo6Ozvr4sWL01xlyuorfeqraqdPn9ayZcsaXXdVH5xR69Wrl1aoUEFnzZplTP/qq6+yfPvSs3LlSi1SpIh++eWXOmbMGA0ICNBu3brplClTdNiwYZojRw4tVqyYli1b1uoe0fNvbt++rQsWLNBKlSpp69atM7s6j+3evXvaqFEj9fHx0SZNmpg9iujy5cv68ccfq52dnZYsWVILFy5sFY+uSf14N1XVhQsXap06dYzHZak+/GDBGnpdqKp++OGHOmLECKMda9eu1dy5c2uZMmWMx7ylHKn87t27xn2Vjo6OVnmv2kcffaSFCxc2Ho01ffp0NZlM+sorr+iuXbt02bJlWrduXa1fv7527NjRuI2gVatWxi0DWW35jhw5Ms2B+aBBgzQwMNBs2pEjR7ROnTpatmxZs+9p8vY0Kx/89unTR4OCgox2btiwQfPmzauTJ0/WM2fO6ODBg43fU4+FkZCQoPHx8ZlQ6/8uPj5e/fz89M0339QbN27oG2+8ofXr11d3d3ejG298fLzGxsZaVRv/aV1bvny5Mehm8vdvyZIl2qJFC23atKnVHAfMnTtXTSaTrlu3TpcuXar58uVL9+TPr7/+qi+88IKaTCb19vY2C6RZbVuTUuqTIqlPeA0ePNhYR69fv67Xrl3TSZMm6dtvv201o5TPnz9fbW1tdePGjXrjxg0tV66cOjo66ptvvmmUSV4P4+LitEePHtqhQwft0aOH1ZxUsEaEbgs5ePCgNm/ePM0Xc9q0aers7GxcFUw+EN6/f7/a2NhogQIFjIGNsvqGWVW1X79++vrrr5tNu3HjhpYoUULnzp1rNv38+fNaokQJLVmypIaFhZm9Zg1tTfbtt9/qW2+9pfPnzzemrV+/XgMDA7VTp04aHh6ux48f1xUrVujmzZuz5WMWkgeFq1mzplV170zt7t27euXKFW3VqpU+99xzZgf0qqp//PGHHjlyxGqeNZ7y8W7r1q3TqVOn6pQpU7Ry5cpmj+lL9t1331llj4uPP/5YTSaT/u9//9OoqCj18/PThQsX6qlTp3TFihWaM2dO7d+/v9G2O3fuaOnSpdXJyUmPHDmSybXPuEuXLmmvXr10+fLlqvrg+dsuLi763nvvqZOTk/bo0SPNyPuqfz/C8Pfff3/aVf5X69ev127duqXZLn744YcaEBCQZoTk1atXq6Ojo9atW1c///xzo3xWPrhPfRLsxo0b2rZtW6N317lz59Tb21urV6+uJpNJP/roI6voTfOoFi9erI6Ojurk5KTt27fXxYsXq6rqW2+9pc8995zV7RNThpBVq1bpzJkz9aOPPjI7oZkcvIcOHaqqD/b7yfd0J/+ela1YsUJNJpPxRIQ7d+5ojRo11GQy6YoVK9INYvv27dOIiAirONZJeaz5v//9T5s0aaKFChXSESNGmI35MWTIEC1RooROnjxZY2JizEaoz+rHq8uWLVOTyWT0MlFV3blzp/r7+2uFChV069atxvSU7UopKy9Da0botqDkg4F169bpgQMHVPVBt+IqVapoq1atzB6l8NNPP+nQoUP11Vdf1YIFCxr3CWdliYmJunnzZuNLm7zjiYqK0oCAAO3fv7/GxcWZ3cfWrVs3rVu3rgYGBurXX3+daXV/XFeuXNFSpUqpk5OTTp061ey1r776Shs1aqTt2rVL08U1q2+kH0dcXJxGR0dndjWeiNOnT2urVq20cePGumTJElV90JW3f//+WfqgPrXkx7s1atRITSaTfvnll7pmzRrNkSOHhoeHm5W9f/++vvnmm7pgwQKramNyXefNm6c2NjY6bNgwfeWVV8x63Hz99ddqZ2en/fv3N64eTpkyJUuGz0dx584dXbt2rd68eVN/+OEH9fT0NAannDRpkppMJm3QoIExuM+RI0f0zTffVC8vryzbQyMhIcGsp0Ly/nDr1q1qb2+vYWFhZgeEmzdv1rZt22rnzp21bdu2Zj0ZsqqUJ8G++uorHTRokH766acaGRmpUVFRWr58ee3du7eqqvbv319dXFx03Lhx2Wa7qvrgaui2bdtU9e/Q+vrrrxuPJ7RGw4YNUw8PD23UqJF6eXlpjRo1dO/evUZQWbFihTo4OOirr75q9r6sfuUw+Qq3yWTSH374wThuuXfvnlatWlXLli2rBw4cMLbB1vxIqXfffVcLFSqkc+fO1eXLl6u3t7e2adNGN27caJQZNmyY2tnZ6bJlyzKxphkzZ84ctbGx0WLFimmTJk1048aNxnq3c+dODQgI0A4dOujOnTuN96ReZtZ0PGBtCN0WlJSUpGfPntW8efNqt27djLPda9euVV9fX61fv77++OOPunfvXm3evLm+8MILGhkZqQUKFNBFixZlbuX/ReovZXIX1uSrE2vWrFGTyaRjxowxDiDu3r2rXbp00QULFmjVqlXNurlYk59++knLlCmjTZo0SXMf2qZNm7RSpUpWPwL0s+jMmTPavn17rVSpktaoUcO4r9TaJD/erVatWsa0zp07a4ECBXTr1q16/fp1vXnzpg4fPlzd3NyMUVmtQcoTeElJSbp06VK1tbVVNzc3vXjxoqr+fWCbPBp7yu5y1iy5DaGhodqqVStjuzp9+nR96aWXtHnz5mYH9du3b8+yI+ym3H8cPnxYS5YsqZ06dTKC90cffaQ2Njb6v//9T/ft26cXL17Uli1b6v/+9z8NDw9Xk8lkFd/N5JNgyaMBr1+/3thHjh07Vps2bWr0xhg9erQWLVpU8+fPb5TJbo4fP64jRoxQZ2fnhz65JCv6/fffjdv+wsLC1MPDwziZ9dVXX6nJZNIqVaro7t27jeC9cOFCrV+/vtUEmNmzZ2uOHDl0zZo12rt3b82XL5/u2LHDqH9CQoJWrFhRK1eurD/88IPVtCvZqlWrjDEhNm/erGXLltW9e/eq6oMr9Tly5NDSpUtrYGCg2ZXgmTNnWs2JhDlz5qjJZNJdu3bp3bt31d/fX+vVq6ebNm0y9g3btm3TgIAA7dixozG4L54eQvcTlt4ZwG3btqm3t7d2795djx07pklJSbpt2zatW7eu5sqVS0uUKGEMtPXnn39qmTJlzAassAbz58/XmjVrauvWrfXatWvGtBw5cmjLli21e/fuWrt2ba1YsaKqPuheVr9+favtwnLkyBGtVq2a9unTJ83AU3v27LGajTTMXbx4UefPn5/ueAzWIPXj3ZIHu0tISNBevXqpvb29lipVSn19fc0OHK1F8nZ1+/btOmjQIP3ll190xYoVamNjo6NGjTIOLJLLffXVV1qoUCG9fPlyptX5SUluU1BQkNatW1djYmL0zp072rp1a6PbuerDuwtmFakP1uPi4nT27Nlao0YN7dKlixG8p02bpkWLFlU3NzctXry4VqpUSe/evavnzp3TsmXLWk1oSz4JFhAQYExLSkrSoKAg7dSpk7EPDA4O1h07dqS5rzu7OHjwoHbv3l3Lly9vVbd4fPzxx1qhQgW9cOGC3rx5U4cMGWJ0k1+zZo06Ozvr7Nmz1c/PT6tUqaLfffddmu9gVg+oGzduVAcHB+OJCH/99Zf26NEj3eBdqVIl9fHxMUbAtgazZ89Wk8lkPL3i8OHDxi2OmzdvVhcXF128eLEeOnRIc+fOrS1atNCVK1eazSOrH9NdvnxZO3bsqF9++aUxLSoqSmvVqpUmeG/fvl3r1q2rDRs2tLpjAGtH6H6CUj+H+88//zQeA7Z9+3b19PTU7t27mw1G8cMPP+ipU6eM94aEhGi5cuXMHq+R1aTXRer+/fu6fPlyrVWrlrZo0cI4Ux8eHq5vv/22dujQQd944w3jgKpdu3bat2/fLN/d6p8cPnxYq1evrn369El3dNmsvpFG9pTy8W5lypTRF1980Xht06ZNunjxYl22bJnVPvt3zZo16ujoqOPGjTMGRPvkk0/UxsZGP/jggzTBO3kbnF1ERERozpw5tVKlSlq6dGmtXLmy1Zy8TP0Yt+Sr9QkJCbpgwQL18fHRrl27GvuJX375Rffu3avbt2833vvOO+9ouXLlrOIWrNQnwV544QXjtdDQULW3t9dBgwZpt27dNG/evMZzj7Oj+Ph4/e6777Js74v0zJ07V3PkyGEMCpuUlKTh4eF66dIlPXr0qJYuXdq4zWPdunVqMpm0cOHCVvfos99++80I0cnbzcTExIcGb1dXV33ppZcyrb4ZMXfuXM2ZM6dxQkH1wbp4/fp1vXXrljZu3Fj/97//Ge3z8/NTNzc3q3vMoqqa9ZBJPv58WPDesGGD9uvXz6qPwa0RofsJSXkmMzQ0VOvWravVqlXTmjVrGt3Kd+zYoZ6envrCCy+keVzNwYMHtX///povX74s/dzqlF/QLVu26BdffKHLli0zul6tWrVKAwICtEWLFsYV75RnfSMjIzUkJEQLFChglY9BSe3w4cNao0YN7dSpk1U+8xfZ18Me72bNfvvtN/Xy8jJ7AkKyuXPnqo2NjX744YfZ/kDi0KFD+u677+pHH31kNaPpplwmEyZM0A4dOqiXl5eOGjVKDx8+rElJSfrpp5+qr6+vWfBOdvjwYX3hhRe0YMGCWXofmVrKk2Cpv4shISFap04dbd68udUFtexu7ty5am9vbwxsm9rChQu1bt26Ri+atWvX6jvvvKN9+/a1qhPu/3QV/mHB+6+//rKKNib3tly/fr3Z9HXr1un9+/f1+vXrWqFCBf3kk09U9cGYS0FBQfrll19a1T7kYcswdfCuX7++bt68OU3brKmt1o7Q/YS99957WrBgQV2xYoUeOHBAK1asqCVKlDA2zDt27NBSpUppy5YtzQb1OXTokI4fP95qznQPHTpUixYtqo0bN9YiRYpogwYNdNu2bcZ9lnXq1DHraq764Is/bNgwLVmypFUdNP2b/fv3a1BQEBsuZDkpH+/Wpk2bzK7Of7Z9+3YtU6aM2UjdKb93n3/+uZpMJp04cWJmVC/TZPXAnVJISIi6ubnp9OnT9YsvvtD8+fMb96jfuXNH582bpzVq1NCmTZsa7bp//74ePXpU+/TpYzXdylN72Emw6Ohoqx1QLLs6cOBAutuRpk2b6jvvvKOqD+7BL1asmJ4+fVqvX7+urVu31v/9739GWWsIpY8iMTFRX3zxRS1QoIBu3rzZ7LWs3Ma4uDj18fFRd3d3s1s2WrVqpeXKldPo6Gi9ePGi+vv760svvaQzZszQ5s2ba0BAgBFis3L7HlXK4F2nTh0tX768cS87nj5C93+U8h7uS5cuaa1atYwN01dffaX58uUzrsokr/wbN27Ujh07pglpWf1evGSffvqpFi5c2LhaP2fOHLW1tTXanZiYqMuXL9fSpUvr4MGDzd576dIlq37E1MOk7JIFZCXZ5fFuqqpffvmlFitWzAjdiYmJxndv586devz4cV25cqUeO3YsM6uJhzhy5IiWK1fOeDTPgQMHNEeOHGYDhyYkJGhYWJi+8sorVruPfJiUJ8Fat26d2dXBQxw8eFBbt26tnp6exsWRDh06aOXKlfX06dOq+uBkScmSJTV//vzq6empVapUsfr182ESExO1ZcuW2qxZs8yuSoacOXNGy5cvr/Xq1dOYmBjt1KmTVq1a1axX4ldffaUBAQHq4+OjTZo0sYrnjGdUcva4cuWK1fXEyG4I3U/I7du39fLly5ovXz69ffu2btmyRfPkyaOzZ882Xp8wYUKaR4FYY0gbPHiwMfL48uXL1dnZ2TixcPv2bY2NjdXExETdtm3bM/Xlzk4baWQv2eXxbmfOnFFHR0ez548mGzhwoL7//vvP1DYnq0u9fzty5IhWr15dVVVXrlxpto+8deuWcc9hQkJCtj2RmZ1OgmVnP//8s7Zt21aLFi2qDRs2VF9fXyNwJ29j4uLidM6cOfrZZ59ZzW0ejyspKckqv4tnz55Vb29vzZEjh1aoUMEYUyBlW65du6Z//vmnWff57Cb1fpH9ZOYgdD8BX3zxhfbv319jY2O1bdu2OmDAAM2TJ4/OmzfPKHPixAlt2rSp8SgCawloqeuZmJioXbp00SlTpuihQ4fMDpoSExN12rRpOn/+fLP38OUG8KTMnz9fc+bMqUOGDNGjR4/qsWPHdOjQoZovXz6ruT3nWZM8mN3+/fvV3d1dw8LCNF++fDpz5kyjzLfffqtt2rQxewyjtewnMyq7nATLjlKucz///LN2795dTSaTfv3116r69/FMesc1z8KxjrUG75o1a6qPj49evXrVmJ5eW7J6+7J6/fDPbAQZpqpmv//+++8SEREh586dEy8vL5k3b5688MIL8uqrr4qISHx8vAQHB4vJZJLAwEARETGZTE+93hmVmJho1PPMmTNy9epVsbGxkY4dO8qIESPEz89PPvnkE3nttddE5EE7N27cKGfOnDGbj62t7VOvO4Ds6eWXX5alS5fKp59+Ki1atJDnn39e1q9fLzt27JBy5cpldvWQyhdffCFlypSRuLg4qVmzprRq1UoGDRokAwYMkAEDBoiISEJCgkycOFFsbW2lYsWKxnutYT/5OHLlyiXOzs6ZXQ2kI+U6V7lyZRk6dKi0b99eevfuLT/99JPY2tpKYmJiusc1WflYJ/Vx68Om/RsbG+uLDSVKlJCVK1dKXFyctGvXTi5duiQi6bclK7cvKSnJqN+RI0fkypUrj/S+1Ms5KSnpidcNj8akj/Ote4apqrFR/vPPPyV//vwiIuLn5ydeXl6yatUqadu2rZw9e1bKly8vXl5esmfPHomOjpZDhw5Jzpw5zb44WdHs2bMlICBAfHx8REQkJCRENmzYIJGRkfLKK69IvXr1ZPfu3bJ06VL57LPPJCAgQC5fvixvvPGGXLt2Tfbt2yc5cuTI3EYAyNYuX74s586dE5PJJF5eXuLm5pbZVYL8fWCYvK/89ddfpV+/ftKmTRsZOnSoHDx4UMaNGyeHDh2Sd999V27duiU7duyQS5cuyY8//mgV+0g8W44ePSojR46UAwcOyNdffy1VqlSxqnU0ZV3//PNPiYuLk2LFihmvpzyuTSnl9Hv37omdnd3TqbCFnDt3Tpo2bSpubm6ydOlSs88gq0u5DN9991355ptv5L333pPGjRtLrly5Hvq+lMvw2LFjUqFChadSX6SP0P2YPvzwQ9mzZ4/0799fWrduLSdOnJA2bdrIiBEjpFu3bjJz5kzZtWuX5MqVS0qWLCkffPCB5MiRQ+7fv5+lA+kff/wh9evXlxYtWsjQoUPl2LFjMmDAAJkxY4b8/PPPsmXLFilevLhUr15dLl26JLNmzRIPDw9xcXGRvHnzyo4dOyRnzpwPPRMMAMj+Ll++LB4eHnLv3j0ZO3asfP/99/L5559L0aJF5eDBg7J8+XL58ssvxdvbW0qWLCnTp0+3in0knk1Hjx6VMWPGyNq1a+XkyZNSqlSpzK7SI0kZusaOHStbtmyR06dPS40aNaRTp07SvXt3sbe3/8f3LV26VO7fvy8vvPCC1X83z507J1WqVJHu3bvLnDlzMrs6Gfb+++/LvHnzZOHChVK7du00PWZShvOUy3D27NmycOFCWbFihXh5eT31euMBQvdjSExMlO7du8vq1asld+7c8tZbb0mnTp1k9erVcubMGfnoo4+kePHi6b7PGoLokSNH5NVXX5V69eqJjY2NVKhQQXr37i0iIl999ZVMnz5dXFxcpE+fPuLh4SHHjh0TV1dXqV+/vtjY2HDQBADPsKlTp8o777wjixYtkpYtW0quXLnE19dX/P39ZdGiRUa5mJgYs4NG9h3Iyg4fPiyrV6+WcePGWcWxXEpjx46VmTNnyowZM6R27drSokULMZlMsmLFijS35aQMa8m3EG7atElatGiRGVV/qMftbRAZGSmurq5Wtwx//fVXad++vcycOVOaNGki0dHRcuXKFdmzZ48UL15cmjZtapRNvQzfeecdWbhwoXTq1Cmzqg8RYe/2GGxtbaV///7i6OgotWrVkpUrV8qNGzfk5s2bcuDAAdm4caMMGDAgTZcda/mC+/j4yCeffCL9+vWT06dPy8iRI43Xnn/+eTGZTBIWFiazZs2SYcOGSefOnY3XExMTOWgCgGdI6n3dzZs3RURkxowZcujQIQkICJDly5dL+/btZenSpfLCCy+IiEiePHnM5sG+A09Det2pH9bFOqXq1atL9erVRSRrnyC6ffu28d1KSkqSqKgo2bRpk8ydO1fatWsn3333nZw5c0bCwsKkXLlykpiYKDY2NmIymcyC7Ny5c2XYsGGyevXqLB24jxw5Im5ublK4cOFHep+7u3u688nqcuTIIY6OjpKQkCDff/+9LF26VHbv3i0JCQny119/yaRJk6RTp05m6/LcuXNl6NChsnjxYunQoUMmtwDWsaZlEVOnTpUpU6aIiEiDBg3E1tZWDh48KNu2bZPatWuLk5OTnDt3Tt544w355ZdfrHoQmOrVq8uCBQvExcVFNm/eLEePHjVea9Omjbzzzjty6tQpWb9+vYj8PVCDtZxYAAA8Gcn7uvj4eBF5cFWtV69e4u7uLt7e3jJjxgx54403xMfHR7766iu5fPmyiJjvL6x5fwnrkZSUZDYuz4ULF0Tk7/XvYZ0/ExMTjf8nJCRk2cD92muvSVhYmNy4cUNE/h4YLCEhQVq1aiUbN26UVq1ayaRJk+TVV1+V+Ph4+eKLL9IMLpYc1ubPn5/lwlrq+5v79esnBw8eNLY/D6OqxvuOHTsmIll34LT0BjtzdXWVPHnyyKhRo6Rhw4aSI0cOGT9+vISHh0vhwoXl2rVrIiJmXcqHDx8uCxYsyHLL8Jn1VMZIzwbu3bunH3zwgdra2mq3bt10+/btev/+fa1evbpOmDDBKDNo0CBt2rRptnl0xJEjR7RatWrap08f/eWXX8xe27NnT7ZpJwAgY1I+vmbixIk6YMAA3bx5s6qqbtiwQV999VX9/vvv9dq1a9quXTt1d3dXk8mk69evz6wq4xmW8nFgY8aM0YCAAC1UqJC2atVKFy5cqHfv3v3X933++ee6aNGiLPss5969e6uXl5eGhYXpjRs3VPXBI/tKly6tPXr0UGdnZ50zZ45R/vjx4/rcc88Zj7NVVZ05c6Y6Ozvr6tWrn3r9M+K9995TNzc33bx5c7qP4Eu5fUq5DGfNmqU1atTQM2fOPJV6ZlTKem/ZskUXLVqkK1as0Fu3bmlcXJxu2bJFd+/ebfYef39/nTVrlvH7V199pfny5dNVq1Y9tXrj3xG6M+iXX37R9u3ba82aNTUoKEg///xz7dSpkx46dMgok/zlzi6B9PDhw1q9enXt06eP/vrrr2lezy7tBABk3Pr163XEiBEaGBiotWrV0nfffVdv3rypHTp00KFDhxrlvvjiC3377bezbGDBs2HMmDFaqFAhXblypV68eFErV66sVapU0ePHj6cpmzKszZ07V00mk3FiKStJWc/Bgwerp6enTp06VSMjI1VV9ZNPPtGCBQtqp06djPLx8fHaqlUrbdKkiXEcd+vWLW3YsKGuXLny6TciA3755RctXbq0btu2TVVVb968qceOHdN58+aZnUBQTbsM8+TJYxVhdOjQoVqkSBENDAzU8uXLa0BAgNkJy7i4OD1//rw2b95cq1WrZrZd3b59u3777beZUW38A0L3Y7h27ZquXbtW/fz81M7OTgsUKKDjxo0zK5PyS54dHD58WGvUqKGdOnXKsmcHAQCWl/JKTGhoqDo5OenVq1f19OnTumjRInV1ddV27drpuHHj1NbWNt0DXII3noZbt24Z/09MTNTLly9rzZo19csvv1RV1W+//VZz586t8+bNU9UHFxGSj99Srudz5sxRZ2dnXbNmzdOrfAalvAASHBxsBO+YmBi9fv26BgcHq5OTk3bt2lV79+6tDRs21EqVKum9e/dU9e/vZHx8fKbUPyNOnDihVapU0Q0bNuju3bv1tdde04oVK6q3t7eWKFHC2OakPBafM2eOOjk5ZellmGzRokXq4eGh+/btU1XVqVOnqoODg27YsMEoM2XKFK1Tp47Wr18/zTJE1pQ1b2bI4goWLCjt27eXH374QYYMGSLx8fESHh5uVia73Z9WrVo1mTFjhuTNm1dKlCiR2dUBAGSS5PsgT5w4IXfu3JGFCxeKq6urlCxZUnr16iUnT54UEZFDhw5Jjhw5ZPLkyXL+/HmzeWTVe2KRfTwL9zeLmI+pk3wv8OTJk6V9+/YSFhYmCxYskFy5csmoUaNk4cKFcvPmTUlMTJQGDRrIjz/+KDlz5jQbGM7R0THT2pKeZ/H+5l9++UXatGkj/v7+snr1ahk1apRMnTpVWrduLfHx8XLjxg0JCgqSfv36GY/qzcqD++EBls5j0v8fHfCDDz6Q559/Xnx9fc2mZ0c1a9aUGjVqpBnhEgDwbPn222/lueeek1y5csmSJUuM6ffv3xdnZ2dZvXq1bNiwQRISEiQmJkaKFi2aibXFs+j+/fuyYMECyZs3r7z00kuSP39+cXJykvj4eHn55Zdl06ZNMmnSJOnXr5+IiJw/f14WLFgghQoVMtbXWbNmyYgRI2TBggXSsWPHzGxOulIei8XFxcm9e/fExcVFRESmTJkiqipTp04VEZFevXpJhw4d0oTOrPzUmZTt27p1q0RGRoqjo6O0bNlStm/fLrt375bcuXNL3bp1jfdoqsHwNmzYICNGjJB58+ZlyWWYUvKjhePi4qRSpUoSEREhQUFBMnHiRHnttdckKSlJli1bJomJidKvXz956aWXjPdl1WWIFDL1OruVS92F/Fm5tzm7dZ0HAGTc+PHj1WQy6ahRo8y6NabcFyYkJBjddFN21wUs5Vm5vznl9+l///ufNmvWTN3d3XXkyJG6Z88e47WBAweqp6enTps2zfgMVK3rWC673t/8sG3irFmz1GQyqa2tra5YscKYHhsbq4GBgfruu+8+rSriCeK0yH+Q+or2s/K4rOx6JR8AkFbKq03JV2JERIYNGya3b9+WDz74QDw9PeXll18WkQf7Qv3/Xl92dnZp5gFYkslkMtbTiRMnSlJSkkybNk1ERF555RXp0KGDnDhxQj799FPp1q2b5MmTR06fPi3Xr1+Xw4cPi62trdy/f1/y5MkjmzdvznLdrZMlf5/ee+89mTdvnowZM0ZeeOEFGTNmjPz888/Sr18/ad68uUydOlVsbGxk2LBh4u7uLl26dBER6zmWW7x4sXz++eeydu1a8ff3l7CwMAkJCTHbnsydO1fWrFkjtra2sn//fsmRI4fR3TowMDATa/9wqa/i37lzR8qWLStly5aV/v37y9GjR2XRokVStGhRiYqKklu3bskbb7whN2/elNGjR2du5fFYCN0AACBdKQ8MZ8+eLQcPHpRbt26Jr6+vBAcHy7hx40REpE+fPmIymaRXr14ikvaAnsCNpyH5ZE/y/c02NjYyefJkUVUJCwsTEZF+/frJqFGjpE6dOjJ37lxxdHSUBg0ayHvvvWcW1kSy3v3NyZJPKmzevFlWr14t69evl1q1asn+/fvl7NmzYjKZJCwsTHLmzCmNGzeWyZMnS4kSJbJ89+r0/Nv9zXfu3JGgoCApWLCg9OjRwzhpktW7WydvE4cMGSJLliyRhIQEKVu2rLRt21aGDx8uISEhEh0dLc8995y4u7tLgQIFJHfu3BIRESE5cuQwOwEK65C110gAAJBpkg8Mhw0bJosWLZK33npL4uLi5OOPP5a9e/fKunXrZNy4cWJjYyP9+vWTuLg4GTBgQCbXGs+i7H5/s4jI/v37pUaNGmJrayuJiYni5uYmr732mtSqVUs2b94sL774oixYsEDKli0rjRo1kpw5c0pMTIx06NBB3nrrLRERqwlr2fX+5uQTQ6oqZ86ckf3798u2bdskb968Mn36dFm/fr3cuXNHxowZI8uWLZMdO3ZIfHy85MuXT2rXri02NjZWcVIBabHEAADAQ+3bt0/Wr18v69atk4CAAFm3bp1Mnz5dWrdubVzRHjNmjNy8eVOWL18u/fv3t5quq8geUgbuDz/8UL777jv56aefpG/fvtKsWTOpXbu2EbinTZsmNjY20rVrV3FzcxMR8yvkWdX69eulffv20rZtW1m9erXY2tqKt7e3lChRQm7duiWTJ0+WIUOGSM+ePUVEpFy5cnLgwAGpWrWq2cmFrNrG1LegJNezcuXK8vrrr4uNjY0sW7bM6B4fFxcnK1asEH9/f7P5ZNX2iaQ9MaSq4ubmJmXLlhUHBwcZM2aMjB8/XrZu3SqJiYkyduxYadSoUZp5ELitE/29AACAQVON/nvlyhXJkSOHBAQEyJdffik9e/aUiRMnSp8+feT27duybt06UVX5+OOP5dtvvzWu4gBPS8r7m6dNmybt2rWTCRMmyNKlS2XixImyZcsWERGZOnWqdOjQQYYNGybffvut8f6sfpIoKSlJTp48KXnz5pXff/9dWrduLYmJieLs7CwFCxaUu3fvypUrV8Td3V1ERKKjo6Vq1aoye/ZsGTt2bCbX/t+lvr953bp1cvz4cUlKSpL+/fvLa6+9JnZ2dsb9zadOnZLOnTtb3f3NyW0cPXq0BAQESLdu3eT8+fPi4OAgIiJOTk4yfPhwCQwMlF27dsnbb7+d5pFp3KpjvThVAgAADMkB5Ny5c1KiRAlxdXUVb29vWbJkibz++utmj1n64YcfZNOmTVK5cmUpVaqUEbizeohB9vEs3N9sY2MjAQEB4uDgIO3bt5eIiAhp06aNbNiwQWxtbeXWrVuSJ08e+e677yQhIUHWr18vsbGxMn/+fLOB5bKq7H5/c8qTCp9//rl8/PHHMmrUKImIiJBvvvlGgoKCZOHChSLyIHiHhIRIbGys3Lt3j21pNmJSTkcDAIAUFi5cKNu3b5dly5bJxYsXJSAgQC5duiRTp06Vt99+W0RE7t69K+3btxcXFxdZunQpB4d4qpLvb7axsZHExEQ5cuSI7N69WwYOHGjc3xwWFmbc39yoUSMJCgoy62qd1cOaiJjdvzto0CD5448/pF27djJt2jQpWrSorFu3TmxtbWXlypUyZcoUuXPnjhQqVEg2b94sOXPmzNInwVLf3xwUFCTTp0837m/eu3evNG3aVMaMGSM2NjZWeX9zys9/7dq1cuPGDXFycpKuXbtKXFyczJ8/XxYuXCh+fn4yb948433x8fHi6OjIicxshD4KAADATK5cuWT58uWyf/9+KVq0qKxfv17y5Mkje/fulUWLFsmaNWukdevWcvHiRVmyZAldyvFUrV+/XgICAqRjx45GcPb29pYXX3wxzf3N/v7+xv3Nhw8fNptPVg7cBw4ckJiYGLNA+dxzz0liYqLUr19fRo8eLWfPnpXnn39e7t+/L126dJG1a9fKjh07ZNu2bf/X3p3H1Zz9fwB/3dtGIUvTGGVf52sY26jBMMOM7dvYUmms8Q0R6UulECJjyVKSIqmYLCll3xrZ85UYe5aZMAhRaFO3e35/+PWZbpgZM6N7b72ef+nez+c+zunervP6nPM+H+jp6UGhUGhsWFMqlVLbStc3N2rUCHPnzkWPHj2wf/9+zJw5EwqFAt27d4eVlRW6dOkCuVyu0fXNPXr0wIULF6Q+Xrt2DePGjcP48eNRWFgIADAyMsKoUaPg4OCA5ORkaQUR8Oo7mIG7fGHoJiIiqsCKw7IQQvq3nZ0d7O3tERgYiGfPnqFdu3bYv38/bt++jXnz5mH58uX44IMPkJKSIi3v5MCQykJ5r28GgB07dsDS0hKdO3dGQEAADhw4AADo168fsrKyEBAQgP79+8Pb2xv379/HwIEDoVAoUKdOHdSqVQsymUyjAylQvuubs7KyYGFhgebNm0uPmZmZITAwEI0aNcKGDRukx6tVqwYHBweMGTMGO3fuxKJFi1Rei9+r5YdmflqJiIioTBQP6vLy8lRmrDt16iTdlxsAPv/8cyQkJCApKQm7du1CVFSUNJumyTOGVL6Urm8uKCjAt99+i6KiIgBQqW8ODg6Gvb09UlNTMXDgQGkpuiYrLCzEL7/8go8++ggPHjzA06dPMWrUKIwfPx6JiYmYO3cubt++jfv376Nfv37w8vJCSkoKvLy8VF5HUwNpyeBcXN/8n//8B02aNMGtW7fg4OAgPV9c39yuXTsUFhZqTQCtXr06FixYAAMDAyxatAiJiYmoWrUqrKys8P333+Py5csYPHiwdHzVqlUxfPhwBAQEYNq0aWpsOb1PrOkmIiKq4CIjI+Hn54dly5ahdevWMDU1BQC0adMGLVq0wObNm994Hpc+Ulkqz/XNJWVkZCAmJgbz58+Hra0tXFxcMGvWLPz666+4dOkSnj17hsjISNja2uLly5c4ceIEunXrpvEXvypafXNBQQGsra1x4MABHD58GJ06dUJOTg727NkDNzc3fPbZZ4iOjn7tPG3Ya4DenWZeBiMiIqL3puSScuBVbWH79u1hb28PR0dH+Pv7AwA8PT2RmZmJpKSkN76Otgx+SbuV9/rm0kxMTGBjYwMPDw+EhoYiOjoaERERiI2NxXfffYf27dujWbNmAAADAwN0794dOjo6GjuLX1Hqm69evYqXL18CeHV7uvT0dKxfvx5DhgxBz549ceLECRgZGaFv377w8/NDSkrKa/fhBjR7rwH6GwQRERFVGEVFRdK/CwoKVJ7bs2eP8PT0FFWrVhX9+vUTzs7Ool69esLPz6+sm0kkhBAiPj5eyGQy0bJlS+Hv7y/2798vPdelSxfh4uIihBBi69atok2bNsLKykoUFhaqvEbJz7w2efr0qQgICBDVq1cXHh4e0uNZWVlCCCGUSqW6mvanZWZmCk9PT5Gfny899vz5c7F582bRpEkT0bNnT5Xjnz9/LlauXCk++ugjsXDhwrJu7l925swZ0bp1axEQECAmTZokZDKZuH79uhBCiEePHomhQ4cKIyMjcfz4cSGEENnZ2SIiIkIMGjRIaz+f9G64vJyIiKiCKHm/2KCgIJw6dQpFRUX417/+hZkzZwJ4Nft9584d+Pr64vHjx4iPj0fnzp1x7NgxdTadKqDCwkIEBQVh8eLFyM/Px6RJk7BmzRr069cPQ4YMgVKpxMqVK7Fq1SrUqlULO3bswJQpUzB06FAsXrxY3c3/R2RlZWHDhg2YPXs2HB0dpY22tHEJ8qJFi2BhYYEvv/wSOTk52Lt3L6ZMmQJLS0ts27ZNOu7Zs2c4ePAgBg4cqDV9VCqVmDx5MmJiYpCdnY1Dhw7BwsJC+s59/PgxXF1dER8fj/3796NTp07Iz8+XNo4r+d1M5RPfXSIiogqieFA3ffp0zJ49G6ampsjNzcXGjRvRsWNHaRfy+vXrIzAwEGvWrEFISAgOHz6s5pZTRaSnp4ehQ4fC29sbhoaGePHiBZKSkpCXl4d58+bB3t4ee/fuxfHjx2FgYIB+/fphw4YN+P7779Xd9H9M9erVMXz4cPj4+GD9+vWYOHEiAO1bglxQUIDjx4+jV69eOHnyJIyMjNCnTx8sX74cycnJsLGxkY41NjbG4MGDNXrJfElFRUWQy+X47LPPUFBQgHr16uH06dN48eKF9J37wQcfYPny5Rg0aBC6dOmCixcvSoEb0NyN7+ifw5luIiKiCuT8+fMYOHAg1q1bJ9UTJiUlwdHREbVq1UJiYuIbzyu5iRVRWXr69CmioqIwY8YMeHt7Y+rUqXj27BnmzJmD//3vf1i1ahXatGmjco6mzwS/68xmVlYWgoODceTIEezZs0eja5uBV/XNjRo1goGBAZYvXw5ra2sYGhpi6tSpiImJwf79+9G5c2dpxtvDwwP169fHjz/+qO6m/2ml38Nbt24BAFasWIFTp07Bzs4OEyZMgJGRkXTMkydPEBgYiBkzZvD7tIJh6CYiIqpAEhISYGNjgwsXLsDc3BzAq4CSkJAAFxcXrFy5El9//bWaW0mkKjMzExs3boS3tzfGjRuHhQsXAni1FNnY2FjjN9l6m9jYWFhaWqJOnTp/eGzx7dCK78OtqbOjycnJGDNmDP7zn//gxo0bCAwMRGpqKpo2bSots46Li1MJ3jExMYiPj0d0dLTG9qukkr//M2fOSLdCs7CwQGFhIaZMmYL//e9/sLe3x4QJE1CpUiVMmzYNrq6uMDMzA8ALmRUNQzcREVEFcvfuXXz11VeYMWOGyj1xHz16hFatWuH777/H6NGj1dhCojcrD/XNJcPavHnzsGDBAvz0009o2rTp71400OSQXVp5r28ueYFnxowZ2LZtG/T19XHv3j0MHjwYS5YsgZGRESZPnozk5GQ0btwYT58+RXJyMh4+fMigXUFp7ieaiIiI/nHGxsZo1aoVoqOjcejQIelxAwMDmJubw9DQUI2tI3q78lDfXBwmb968CT09PcTExKBZs2a/G7iFENJ5R48exZUrV8qkrX9FRahvLn6vli5dijVr1iAiIgIXL16Ei4sLQkNDcfXqVejq6iIgIAD9+/eHrq4uatWqhfT0dOjq6mpFnTr98zjTTUREVEEUz9CkpqZi1KhR0NfXR7t27dChQweEhYUhIyMDKSkpWhViSPuV9/rm0vbv348+ffqgZs2a2Lx58++Wc5ScVQ0KCoKbmxuOHTuGdu3alVVz/5SKWN88fPhwWFpaYuLEidi2bRscHR2xYMECODk5ITc3F4aGhhBCQKlUSt+pXFJecWn2pSQiIiL6xxTXgjZv3hwbNmyAhYUFEhISEBgYCGNjYyQnJ2vNjsFUfhSHtdjYWNy/f/8Pj69evTomTpwoBe7ielpt0b59e7i7uyMrKws3b94EgDf2oWTgDgkJwcyZMxEeHq7RgfvMmTM4ffo0MjIy0LhxYyxbtgwWFhbYunUrQkJCkJ+fDwCYNm0a8vPzMXv2bOjq6kKhUKizC+9ECIHc3FwkJSXho48+wsmTJ+Hg4IDvv/8eTk5OKCwsxOzZs3H48GHIZDIpcAshGLgrMM50ExERVTAlB8kKhQI5OTkwNjaWfubAkMpCRalvflNb8/Ly4ObmhpCQEGzfvh1WVlYqIbt04HZ3d0dYWBisra3LtP1/pCLUN7/tPZwzZw727t2Ln376CatXr5b2yHjy5Ans7OxgZWWFKVOmlHFrSVNpxzcWERER/a53uYZecgCpq6srBW6lUqkVg2AqH8p7fXPJsLZ27Vq4urpi5MiRiIqKglwux8qVKzF27FgMGjRImrUv/jsuGbg9PDw0MnAD5b++ueR7mJqaiosXL0rPff755ygoKICFhQW+/PJLAK82pBwxYgTy8vIwadIkdTSZNBT/ZyUiItJyJQeGeXl5EEL8qQ3RSt9mSVtmD6n8KF3f/HveVt+sqYr/ntzd3REREYGxY8fi6dOn8Pb2RmJiIkJCQuDr6wu5XA5ra2ts3LhRJVgnJyfDw8MD69at08jAXdL58+cxZ84cWFpaYtu2bVixYgVWrVoFS0tLqb7Zy8tL6+qbi99DDw8PbNy4Efn5+WjXrh2CgoLQq1cv3L9/HyEhIfjiiy9gZmaGoqIiyGQynDx5UirV4R4ZBHCmm4iISGslJCQAgMoS3W+//RZdunTBpk2bfnf2u2SAiY2NxZYtW95/g4lKKW/1zYDqqpOEhATExsZi586dmDdvHgYOHIh79+6hS5cukMlkqF69OhYvXoxBgwYhICBA5XU6dOiAU6dOaXTgLq/1zSXfw0OHDiE+Ph7BwcHYsmUL0tPT0a9fP5w7dw4ODg5Ys2YNfH190b9/f0ybNg1JSUnQ09ODQqFg4KbfCCIiItI6W7ZsETKZTKxbt04IIYSfn5/48MMPhbe3txg1apSQy+Vi7ty5Ijs7+7VzlUql9O/Vq1cLfX19kZCQUGZtp4qpqKjojY/n5uaKiRMnCl1dXbFz504hhOpntOS/g4ODRbVq1cS2bdveb2P/giVLlojLly8LIX5rc1RUlLCwsBBCCBEdHS2qVq0qVq9eLYQQ4sWLF+LHH38USqVS5OTkqPx+3va7Ure3tWv27NmiY8eOwsDAQISFhUmPZ2RkiB49eojly5eXUQv/vtJ9TElJEYsWLZJ+LiwsFK1btxbNmzcXycnJKp/PYgqF4r23k7SL5l5iIiIioreytbVFamoqxo8fD319feTm5iIiIgK9evUCAHzxxRf4z3/+AyEE3NzcpOXmJZeih4SEwNPTE1FRUejevbva+kLlX+n65itXruDp06fo1asXrK2tsXLlSgghMGjQIMTFxaFv377S7LY21DcnJiYiPDwcZ86cwfz589G0aVMAr+4hXqdOHezatQsODg5YvHgxxo8fDwA4fPgwEhIS8PHHH6N27doAfvs9aWKpR+n65oKCArRq1QrAq/rm+Pj41+qbHRwctKq+WZTYM2DZsmU4f/48Tp06ha+++ko6RldXFykpKejQoQNGjRqFoKAgaeVCMc5w02vUm/mJiIjoXSiVSlFYWCj9PHfuXCGTyUSNGjXEjh07VI4NCwuTZrxfvHih8pwmzxhS+eXm5iZMTU3FzJkzxYgRI0Tjxo2Fo6OjUCqVIjMzUzg7O4tKlSq99rk8c+aMMDY21ujPa0REhOjWrZsYPHiwuHbtmhBCiDt37ohq1aqprEoRQoi8vDzRu3dvMWLEiDfOlGoyd3d3UadOHVGzZk3x9ddfi+vXrwshXn3fWFhYCDMzM9GxY0fRvn170aFDB1FQUCCE0PzZ35Lvw/Lly0XlypXF+PHjRcOGDUWdOnVEdHS01BchXs2If/TRR2Lo0KHqaC5pGYZuIiIiLZKTkyP9++rVq0KIVwNEmUwmFixY8NoAPjw8/LUBf0hIiKhSpYqIiYkpm0ZThVXy83jo0CHRuHFjcfr0aSGEENu3bxeVKlUSERER0jG5ubniu+++E127dn3tta5cufL+G/wXvHz5Uvr3qlWrRI8ePYSNjY0URvfs2SOqVasmHB0dxd69e8WePXvEN998I1q1aiVdQNPk4F2ybQcPHhTNmzcXO3bsEAcPHhSffPKJaNGihUhJSRFCCPHTTz+J8PBw4evrKzZt2iQF7ZIXCjXd//73P+Ho6CiV3CgUCtG9e3dhYWEhtm/frtIXpVKp8RcTSDMwdBMREWmJQ4cOiREjRgghhHB2dhbt2rWTZrDnzZsndHR0RGho6Gvn7d69Wxoo3r59W1hYWDBw03tVEeqbhVANpEuXLhUjR44UjRo1EnK5XCV479y5UzRu3FjUq1dPdOjQQQwcOFArZoArWn1zbGysaNmypWjYsKF0IUGIV5/P7t27i44dO4q4uLjXLiJoUx9JPTSvYISIiIheI4TA+fPncf36dbRt2xZRUVHYvHkzqlSpAgCYOXMmvL29MW7cOISFhamc27dvX+jq6kKhUKBevXqIi4vDoEGD1NENqgCK65vnzp2LGzduSLWupeubFy1apFLfHB8fj4cPH8LQ0BByuVzaxVwT65uLFffNz88Pc+bMga2tLbZv347Zs2fj559/xowZM3Dz5k1YWVnhzJkzSExMxM6dOxETE6PxO1yLUvXNI0aMgK2trbTLPPBbfXPlypUxatQoHD9+/LW7Jmhq/96kW7duaNu2LZ48eYK4uDjpPuJVqlTBjh07YGxsjClTpuDUqVMq52lTH0lN1Bz6iYiI6HfY29uL/fv3Sz/369dPyGQyMXDgQJGbmyuEUJ1l8fHxEQYGBsLf3/+119LkJaxUvlSU+malUiny8/NFnz59hIeHh8pzISEhomHDhsLOzk7cuHHjtXO1ZQa/vNY3v+33//TpUzF06FDRsWNHsXr1apXv1xcvXojJkydzZpvemUyI37mJJxEREanNzZs38cMPP8DLywt6enoAgIULFyIzMxNJSUlo3LgxFi5cCFNTUxQWFkrHeHh44OTJkzh69KjKjrpE71tBQQH09fUBAEFBQYiNjUXNmjXh6+uLpk2bYu/evRgyZAjs7OwwaNAgCCGwfPlypKenIyUlBbq6uir35NYWNjY2MDQ0REREhMrjo0ePRnR0NCwsLBAWFoZ69eqpqYV/zZkzZ7B27VoMGTIE3bt3R1FREXr27ImcnBxMnz4dVlZW0v22hRBQKpVaMetbcif26Oho3LhxA0ZGRrCwsIClpSUyMzMxceJEpKWlYcSIEXB0dHytX0VFRVrRV9IMDN1ERERaICgoCLVr15aWhfv5+WH79u1o3rw5Fi1ahA8++AAAkJKSgnbt2knBRRsDDGmnkp+1ZcuW4cKFCzh27BjS0tJgbW0tBe9du3ZhypQpKCwshKmpKerWrYstW7ZAT09P44NMybBWkqenJ7Zu3Yrt27ejdevW0uMLFy7Erl270K1bN8ybN0+jl8qXtn37dsyaNQu5ubmIiYlB27ZtAQDZ2dno378/srOz4eXlhX//+99S8Aa0K4xOmzYNP/zwAxo3boz8/HykpKRg5cqVmDhxIp4+fQpnZ2f8+uuv6N+/P1xdXbXq/SPNwtBNRESkgUoOXJ89e4YxY8bg/PnzmD9/PoYMGQIAWLp0KeLj41G3bl14eHhg6tSpkMlkOHDgAAAwcJNa+Pn5wcfHB5s3b4a5uTni4uKwY8cONGrUCAsWLECTJk2QmZmJrKwsVK5cGR9++CFkMhkUCoVKeNM0JQP3gQMHIJfLUblyZXTu3BkA0KFDB+Tl5WHt2rVo1qwZqlatKs0QOzs7QyaTvTW0a6KnT5/CxcUFO3bswJQpU+Dt7S19J+Xk5GDgwIG4ceMGIiMj8cUXX6i5te9u586dGD16NHbv3o3PPvsMz549w5o1a+Dl5YXQ0FCMGjUKT58+xdChQ1G/fn2sXr2a36f0lzF0ExERabAjR46gY8eO+PnnnxEQEICjR4/C29sb9vb2AIDAwEBs2rQJaWlpaNCgAQ4fPiwt7yUqS0IIFBQUYODAgWjdujUWLlwoPbdmzRosXLgQHTt2xPz589GkSROVczU9jJa8gDVt2jSsX78eRkZGAIBx48ZhxowZePnyJbp3744HDx5AJpPB0NAQL1++xJUrVzR+2fzbfv+ZmZmYNGkSbty4AQcHB5Vl1tnZ2ZgxYwaWLVumNTPbJYWEhCAiIgInT55UeW9mzZqF0NBQnDhxAo0aNUJ2dra0uZ8mv4ek2TT3ciIREVEFl5SUhDFjxmDNmjXo3r07xo4dC6VSCR8fHwCAvb09nJ2dYWVlhUePHqF9+/bQ0dHR+BlDKp9kMhkMDAxgZGSEBw8eqDw3duxYJCUlITo6GhkZGa/VN2tL4E5LS8PBgweRkJAApVKJI0eOwM3NDfn5+Zg3bx5OnDiB6OhoPH78GEqlEuPHj4eurq5GL7n+o/rm4uXWkZGRACAF7ypVqsDf3x+A5i8pT0xMxJ07dzBgwABUq1YNAGBkZISLFy/i3r17MDMzk34PvXr1QmhoKF68eAEA0h0iNP3CEGk2/o9MRESkIUoP6iwtLfHxxx/Dzc0NZ8+eRfv27TFhwgQAwPz58yGXy2FnZ4cGDRqgQYMGAF4Nfhm4qSy8LYQ0adIEW7duxYULF1Tqm5s1a4ZPP/0UFhYWMDc3L8um/i3FgXvp0qX46aef0K1bN7Rp0wbAqz7p6+vDxcUFQgjMnz8fNjY2KudreiAtfg9L1ze7urpKgTswMBDOzs6IiopCTk7Oa/XNmty/8PBw+Pj4oEOHDvj000/x6aefAgA6duyI1q1bw9fXFx4eHqhfvz4A4MMPP4SxsTHy8/NVXoeBm/4OfnqIiIg0RPGg7ujRo7hy5QoAICIiAtnZ2XBzcwMAtG3bFs7OzujSpQsmTJiAH3/8UeU1NHnwS+VH6frmQ4cO4cSJEwCA77//HjVq1IC9vT1OnjyJjIwMvHz5EqdPn4adnZ10waj4PtzaIDs7Gw8ePEBsbKzKfaqrVKmCkSNHwt/fH0uWLMF///vf187Vhr/JnTt3IiIiAvHx8Th27BgOHTqEhQsXwsXFBeHh4ahZsyYCAwNhZGSkcu91TffDDz9g4sSJ8PX1RVBQkBS4gVcXTGxtbXHu3Dm4ubnhyJEjOH36NCZPnoxatWrhs88+U2PLqbxhTTcREZEG2b17N7799lt8+eWXsLe3h6OjI/z9/XHo0CF4eHigS5cuAICzZ8/i4MGDcHNz04pBPZUf5b2+GXjzLP7du3cRGhqKefPmYdWqVXBycpKey8nJQWBgIHbv3o0jR45odN/epDzWN9++fRsDBgyAs7MzxowZo/Jcamoq6tatC0NDQ2zevBnh4eE4cOAAWrdujWrVqiEhIQF6enpcUk7/GK4/IyIiUqPiQV3xANbMzAydOnVCzZo1MXfuXCQnJ6NHjx5IS0vDvn37pNDdvn17tG/fHoDmL1+l8qO81zcDqoE7NTUVT548QYsWLVCnTh14enqioKAA7u7ukMvlGDduHIBX9cEuLi5wd3fX+Fv1VZT65pycHOTm5qJbt27SYxEREThw4AA2bdqEFi1aoH///vD19cWQIUNw6dIlGBoaokGDBpDL5dwbg/5R/CQRERGpUfHA9erVq/jXv/6FNm3aoHv37jh//jwuXLiASZMm4eTJk9DR0cGCBQvQqVMn9O3bV+U1NDnAUPlS3uubhRDS3+SMGTOwfft2ZGZmwtzcHB06dMDcuXMxbdo06OjoSMHb0dERAFCpUiXpNTQ1cFek+ubKlSvjzp07iI2NhYODA/773//iypUraN68OWJjY3HkyBHs3r0bX375JXr16oVPPvlEOlepVDJw0z9LEBERkVodPHhQGBsbCw8PD5Gfny+EEKJDhw5i+vTpQqlUip07dwpHR0chk8nEmDFj1NxaquhevHghpk6dKoyMjESfPn1eey4wMFDo6+sLV1dXNbXw7/Pz8xOmpqYiISFBCCHEsGHDhImJiThx4oQQQogHDx6ImTNnCplMJuLi4tTZ1D9t48aNwtDQUERFRYnHjx+/9vyKFSuEpaWlsLGxEYmJiSIpKUn07t1bdOrUSRQVFamhxX+dUqkUQgixZMkSoaenJ+rUqSOaNWsm4uLixIMHD4QQr97D6tWri6CgIHU2lSoI1nQTERGp2Z07d3DixAm4uLigTZs2GD58OJo1a4aVK1fCwcEBPXr0AABs3LgRQ4YM4QwMlamKVN+sVCqRl5eHIUOGoG/fvnBycsLevXthZ2cHPz8/jB07FgUFBZDL5Xjy5Ani4+MxevRojf+brKj1zQqFAj///DPu37+Pbt26qXwW79y5A3t7e3h6esLKykqNraSKgKGbiIhIQ9y/fx9z587F1atXkZmZiSZNmuCTTz7BvHnzVI5jrSGVlbfVNxsbG6OwsBBz585FYGAg/Pz8pPpmAMjPz4eBgYHG1zcDb14O/tVXX8Hf3x/p6emwtraW+ldQUICIiAi0aNECX3zxhXS8pv9NXrlyBQMHDsTu3bvRpEkTAG+vb5bL5eW+vvn58+cYNmwYnj9/joSEBI0ueaDyofz89RAREWmxoqIi1KlTB0uXLsX58+exbNkyxMXFIT4+Hn369EGnTp2kY8vT4Jc0lyjn9c2Aavs2b96MjIwMODs7o3r16rC1tUV6ejr8/f0xevRoAMDjx4+xadMmDB06VCV0a/rfJOubX8nKykJMTAxiY2Nx7949nDlzBjo6Ohq/1wBpP850ExERaYjSAWXFihVISkrCxo0by82gl7TP0qVLsXjxYmzatAndu3fH8OHDsW/fPsTHx6NTp05IT0/HqlWr4Ovri+3bt6N///7qbvKfUnIW//Llyxg+fDgAwNvbG82aNYODgwPy8vJw4cIFvHz5Enl5efjuu++QnZ2Nw4cPa01IK/5e8fPzg5eXFz744ANUqVIFixcvhoWFBWrXro309HR8/PHHWLBggUqpQHmiVCoRGxuLjRs3wsTEBMHBwdDV1S13s/ikmRi6iYiI3rN3rYV80/EcGFJZK6/1zaW5ubnhl19+wYMHD3D16lWYmppiypQpqF69Otzc3GBoaAgTExMAQF5eHk6fPg09PT2tmx0tb/XNf6XG/Pnz58jKykLdunUhk8m07j0k7cXQTUREVEb27NmDrl27Sve6/T0lB5TauIERaaeKUN9cUnh4OFxdXZGQkICGDRvi5cuXGDFiBAoKCjBy5Eh888032LBhAwoLC2FmZoZRo0ZBR0dHq/r4R7Sxvrnkd+KePXtQv359tGzZ8g/PK/355ncrlZXy8W1BRESkwYQQuH37NqysrBAdHQ1ra+s/PL7kQJCDQioLFaW+uaSbN2/ik08+ke41LpfLERYWBmtra/j6+qJq1arw9PQE8Nvvp6ioSKv6+DbaWt9c8vvRw8MDO3bswNChQ1G/fv3fvaBZ8vN97tw5tG3blt+tVGb4SSMiInrPZDIZGjRogHHjxmHLli149uzZW48tOTBcvny5NOAnep+USqX0ubt8+TIWL16MsLAwxMXFwdfXF8bGxqhXrx5Gjx6Nly9fIisrC46OjlAoFBg1apR6G/8XFC/0NDAwQH5+vrRMvrCwEObm5li4cCEePHiAoKAgbN68WeVcTQ6kf5ZSqcShQ4ewc+dOfPTRR0hOToaenh4UCoXG9q/4PSv+nPr5+WHdunXSaoU/G7hDQkLQo0cPpKamvv9GE/0/hm4iIqJ/mFKpVPm5eLDYuXNnJCUl4fHjx289rnhguGbNGsyePVtlF2Gi96V4xs/NzQ2zZ89G5cqVkZaWhunTp+Po0aNwdXVFZmYmmjdvju7du+Pf//430tPTpeXIRUVFau7Buyn+OxswYADOnTuHRYsWAQD09PQAAAUFBejTpw9kMhnWrVuHgoICjd6FvfR3yR+Ry+Xo2bMnAgICsHbtWujq6mr0DH5OTo60ygAAsrOzcfToUcyaNQsWFhaoXLkygDf/HkoHbg8PD6xduxbNmzcvuw5QhcfQTURE9A8qufTx+PHjSE1NlQZ8w4YNQ6NGjTB9+vTXlpCXHhi6ubkhPDwcQ4cOLftOUIUUHh6O0NBQeHl5YdeuXbhy5Qrq1auHzZs3Iy8vD6dOncKoUaPQq1cvjB49GmfOnNH42dE/0qpVK4SGhsLX1xfu7u44e/Ysfv75Z6xcuRLt2rVDYGAgEhIScPToUXU39a1K1zdfvnz5T51XtWpV1KtXT/re0dSLCl5eXujRowcyMzOlz5lMJsPVq1fx/PlzAL9dNJLL5cjPz8ft27cBqK7gCAkJgbu7O9atW/eHJT5E/zSGbiIion9IyQHehg0b4Orqii+//BJLly7F6dOnAQDOzs54+PAhrl+/DuD1JZNr1qyBu7s7wsLCMGjQIDX0giqqkvXNxsbGqF27NsLCwpCXlwdfX18kJSXB09MT3t7eGD16tDTDramzo3/WqFGjsGnTJkRERGDAgAHo2rUr7t+/j//+978wNDREkyZNYGpqqu5mvlHp+uapU6di+/btyM7O/sPzStY3A5q5d4QQAo0bN4aenh5GjBiBzMxMAEBhYSHq16+PW7duITs7GyX3hb527Rrmzp2Lu3fvSn0KCgqCp6enVK9PVNa0+1uSiIhIgxQP8GbNmoV79+5hyZIlSE1NxYoVK7Blyxa0atUKDg4OuHTpEnbu3InmzZurzC4FBQXB1dUVmzZtYuCmMlMcwErWN1eqVEmlvtnKygpBQUFQKBQYMmSIdK62znCXZm1tDUtLS9y9exeFhYXo3Lkz5HI5goODoaOjo3Ghu/g9K13fvHv3bnzyyScwMjL6w3OBV7O/np6eOHXqlEYut5bJZHBwcIChoSECAwMxbNgwbNiwATVr1oSTkxNsbGzQqFEjTJgwASYmJsjKysKsWbMAAGZmZgCAxMRETJ06FZGRkQzcpDa8ZRgREdHfVHIQe+TIEUyaNAkRERFo27YtAOCXX37BzZs34e7uDnNzcxw4cABmZmbYv38/mjZtCgDIz8/HggUL0KpVK9jY2KitL1RxXbx4EW3btsWsWbMwe/Zs6fH9+/dj7dq1yMzMhFwux+7du6Gvr6/Glr5/ly9fxqJFi7Bnzx4cOnRI2t1cE+Tk5MDIyEjaZTw7OxvfffcdevToARcXF2m5+Ztuh/Wm+mZNXm5d3F6lUonNmzdj1apVMDY2RmRkJExMTLBu3To4OTmhU6dOUCgUUCqVyM7OxtmzZ6GnpwchBC5duoSCggK0b99e3d2hCowz3URERH9T8SA2PDwcZ86cQadOndC2bVup1rVhw4Zo2LAhzp07J81ELV26FCkpKWjatCmEEKhUqRJmzJgBAwMDNfeGKqri+uaxY8ciJycHdnZ2qFGjBlauXIlOnTph4MCBaNmyJY4ePYqvv/5a3c19bxQKBQoKCmBqaoojR478qfs/lxUvLy/8+OOP2Lt3L2rUqAHgt/rmzz77DMDr9c0PHz5E/fr1VUJ4cX2zpi63Lj2TL5fLYWdnByEEgoKCMHLkSERGRmLMmDFo1qwZTp8+jbt376JRo0aYOHEidHV1pXupt2rVSs29IeJMNxER0T/GxsYGMTEx6NChAxISElC1alWVe/uWXIo7bdo07N+/H6dPn4ahoaEaW02kKiYmBhMmTIC+vj6EEDA1NcXJkyfx8OFDfPPNN9i2bRtat26t7ma+d4WFhdJu5ppACIGwsDCEh4ejevXqiIyMRI0aNZCVlYXBgwfD3NwcgYGBMDIyksLq+fPnERAQgLlz56Ju3boAXpWxzJw5E2vXrtXIwF3y4kBaWhoMDAwgk8lQu3ZtKBQKbN68GatXr1aZ8S49q6/p9xqnikfzdkwgIiLSAm+6Zh0dHQ1nZ2f88ssvWL9+PZ4/fy4NfosHgMW3tOnduzcMDQ2Rm5tbdo0m+hOsra2RkpKC6OhobNq0CcnJyahUqZLG1je/L5oUuIHf6psnTJiAp0+fYtiwYXj69CmqV68OJycnREZGYtmyZXjy5AkASPXNjx8/fq2+OSQkROMDt4+PD2xtbfH555/DwcEBO3bsgK6uLoYMGQInJyc8f/4cDg4OyMjIeG0ZPQM3aRrOdBMREb2jkgPD27dvQ1dXFzo6OqhduzYAYOTIkTh16hTc3d1hZ2enMuNdbNasWVi5ciVu3bqFWrVqqaUfRH+GJtc3VyQVqb7Z29sbwcHBCA0NRdWqVbFixQocPnwYoaGhsLW1hUKhwJYtW+Dj44N+/fphyZIl6m4y0e9iTTcREdE7KHmLHm9vb+zevRtpaWlo2bIlevfuDS8vL0RERGDEiBHw8/ODTCaDjY0NqlWrJr1Gbm4uqlSpgoSEBAZu0miaXN9cUVSE+uaSFyWPHj2K3bt3Y/v27ejcuTP27duHw4cPo127dhgzZgx0dHRgbW0NW1tbmJiYlOv9Baj84Ew3ERHRXzB//nwsX74cYWFhyMnJQWpqKpYsWYKJEydKsy6jR49GbGwsNmzYgG+//Vbl/DftLEykqTStvrmiqAj1zSXbm52djby8PKxYsQLz58/HwYMHMXz4cPj4+KBnz54YMGAAbt68KV1sKKbpfSRi6CYiInpHz58/x+DBgzFo0CCMHz8ewKvZ661bt2Ly5Mnw9/eHg4MDAGDBggXw8PDggJCI3knp+uZdu3bh0aNH+Pjjj+Hk5IR+/fpJwTs4OBg1atTA+vXrYWJiouaW/3kl+7h06VLcvHkTnp6eMDU1RaVKlWBra4uGDRti4cKFkMlksLW1xeXLl2Fubo59+/YBgErZDpGm4iV2IiKid6RQKHDp0iU8evRIeszQ0BDW1tbo2bMnzp49i6KiIgCvbvGjo6Mj/UxE9GeULGMJDAzEzJkzsX79eujr62PYsGHYunWrysZi169fx6JFi9Tc6ndT3EcPDw8sWrQIXbt2hVKpRKVKlfDs2TOkpKSgRo0akMlkePHiBQBg4cKF2Ldvn8qSeyJNx5puIiKi33H58mWYmJjgww8/hJubG+zs7NChQwcMGDAAZ8+exbVr19CiRQsAQNWqVVGrVi3cuXPntZltznQT0Z9R0eqbExISEB0dLfWxWLVq1dCrVy+sWbMG+fn5OHz4MPLz89G3b19pQzmW6JC24CeViIjoDYQQuHLlCrp164bIyEg4OTlh6dKlMDAwAAB8/fXXuHHjBkJDQ3HlyhUAwIsXL3D9+nU0btxYnU0nIi2lVCqlwJ2dnY2PP/4YvXv3RqdOnXDgwAGMHDkSS5Yswfr169GoUSOMGDECERER0NPTQ69evbRyVc2dO3dgaGiosklf8YWH4cOHw9raGnv27IGpqSmOHz8OHR0dBm7SOqzpJiIi+h3Lli3DvHnzkJ+fj127dqFHjx7Sc2FhYfD394dSqYSpqSmys7ORm5uLc+fOQVdX97XbhBERvU1Fq28u/n4MCgrCypUrcerUKVSvXh1CCOkuEXFxcWjQoAFatWoFuVwOmUwm7cROpE14iYiIiKiU4kEfADRv3hwymQzGxsZISUnBvXv3pONGjx6NoKAgTJkyBU2bNoW1tbUUuBUKhVYNgIlIvSpafXNxe7/66ivcuHEDK1askB6Xy+V48eIF1q9fj2PHjkFHRwcymQxCCAZu0kqc6SYiIiqh9C166tatC6VSiYCAAKxYsQJjx47FmDFjUKdOnbeex9vXENFfkZCQAEdHR2zYsEGlvlkIAWdnZ+zduxcjRoyQ6ptPnjxZLpZbr1mzBs7OznBycoKVlRX09fWxYMECpKen4+zZswzapPX4CSYiIvp/pW/Rk5CQgOnTp6NPnz6YOnUqXr58idWrV0NXVxcjRoyAmZkZBg4ciDlz5uDTTz+VXoeBm4j+ij+qbzY0NMSePXtQv359REVFlYvADQCOjo748MMPMXnyZMTExKB69eowMzNDcnIydHV1eSGTtB5DNxERESDVEAKvlneGhYUhMjISrVq1ko7x8vICAISEhODMmTN49OgRbt26hX/9619qaTMRlQ/FwTovL09lI7TiUheZTIb09HQMHToUCxcuLHf1zTKZDP3790fnzp3x7NkzKJVKNG7cGHK5vNz0kSo27b4sRkRE9DedP38ewG/1hcePH0dsbCz27NmDPn36wMTEBA8ePMDWrVuRn58PLy8veHl5wdzcHC1btsTdu3ehp6cHhUKhxl4QkTZjffMrJiYmaNy4MZo2bQq5XA6lUlnu+kgVE2u6iYiowho+fDiqVauGVatWSY/t378fDg4OuHTpEh4+fIiIiAjExMTg8ePHMDc3x7lz56Cnp6ey3JEzMUT0T2F9M1H5w9BNREQV1u3bt/HRRx9BX18ft2/fRv369ZGWlgZ7e3tkZGQgMzMTgwYNQufOndGrVy80adIEwcHBGDZsmLqbTkTllBACO3bswOTJk1FUVCTVN+/ateu1C35EpB0YuomIqEIquflQSEgIIiIisHjxYnTp0gXnz5/H4cOH0axZM3Tt2hVVq1ZFZmYm+vTpg3nz5uGbb75Rc+uJqLzLyMhgfTNROcHQTUREFd6VK1cwaNAgNGrUCDNnzkSnTp2k5woKCvD48WM4OTnh4cOH0i16iIjKUnnYpZyoomLoJiKiCkMIAaVS+cbQnJqaisGDB8PMzAyenp7o1q0bhBAIDw/Hxo0bkZOTg2PHjnF5JxEREb0Thm4iIqoQnj17BmNjY+nnLVu24Pbt22jRogU6dOiAOnXq4Nq1a7CxsYG5uTk8PT3RtWtXJCYm4urVqxg7dix0dHS4vJOIiIjeCUM3ERGVe15eXrh27RpCQkLwwQcfYNq0aYiMjISJiQmKiorQpk0b+Pj4oHnz5rh27RpsbW1hbm4ODw8PdOvWTXodznATERHRu2JhCBERlXtGRkZ49OgRPD09cfLkSfz888/Yt28fLl68CC8vL2RkZMDV1RWpqalo0aIFtm7dirNnzyI+Pl7ldRi4iYiI6F1xppuIiMotIQRkMhkAICAgANu3b0fVqlUhk8mwdetWGBgYAAA2b96MtWvXolKlSli2bBmaN2+O27dvw9zcnEGbiIiI/haGbiIiKtdK7vi7fPlyhIWFISsrC5cuXXqtxnvdunV49uwZoqOjUa9ePQBcUk5ERER/D5eXExFRuSaXy6FUKgEArq6umDBhAqpWrYpJkybh4cOH0nF2dnawt7eHpaUlzM3NpccZuImIiOjv4Ew3ERFVCCVnvP39/REdHY1mzZph4cKFMDU1/d3jiYiIiP4qjiaIiKhCKDnj7eLiAhsbG1y/fh0zZszAgwcP3ng8ERER0d/FEQUREVUYpYO3ra0tjh49ivDwcPU2jIiIiMotLi8nIqJyoeRO5X+0NLzk81u3boW1tTVrt4mIiOi9YOgmIqJyJSwsDJUrV4a9vf3vhu/Sz7GGm4iIiN4Hji6IiKjcKCoqQlBQEGJjYwH8fl128aw4ADx48AC8Bk1ERETvA0M3ERGVC0qlEjo6Oli2bBlOnz6NAwcOvPXYkkvR/f39MXjwYGRmZpZVU4mIiKgCYegmIiKtVFRUpPJz8ax248aN0bRpU5w4cQIApI3TipUM3CEhIZgzZw6cnZ1hYmJSBq0mIiKiioahm4iItEpkZCTu3r0rbXy2Zs0a+Pv7Iz8/HwBgZmaGQYMGYdmyZfjll19UlpiXDtzu7u5Yt24d7O3ty74jREREVCEwdBMRkdbYunUrvLy8EBgYiF9//RU5OTlISUnB9OnTMWDAAEybNg05OTn47rvv8M033yAqKgpKpVKq1y4O3GvXroW7uzvCwsIwaNAgdXaJiIiIyjmGbiIi0hq2traYMGECEhISEBAQgIKCAgQHByM1NRWWlpY4evQoWrZsiaVLl+LevXtISkoC8CpsFwfvsLAwjBs3DuHh4bC2tlZnd4iIiKgC4C3DiIhI402YMAFWVlbo27cvAGD+/PnYtm0bevbsiYkTJ6J+/fpQKBTQ1dXF4sWLkZaWhvXr1+Ply5dYvnw5XFxcpNe6dOkSbt26hf79+6urO0RERFSBMHQTEZFGS01Nxbp16+Dr6ws9PT3pcR8fH8TFxeHrr7/G5MmTYW5uLj1XUFCAs2fPYs6cOahWrRqio6MhhJB2OCciIiIqKwzdRESkNSIjI1FUVAQHBwcAqsHbxcUFZmZmAF7tWC6Xy3H69Gl07twZx44dw+eff67OphMREVEFpavuBhAREf0Z6enpiIqKwvPnz1G5cmUMGTIE3t7eAIC4uDjIZDK4uLigTp06AF4F744dO6J9+/Z4/PixOptOREREFRg3UiMiIo1U+v7atWvXxty5c9GgQQMEBQVh06ZNAABvb28MHDgQP/74I3x8fJCRkQG5XA65XI6VK1fizJkzaN26tTq6QERERMTl5UREpHmKl4cDwN27d1GlShUYGxtDLpcjKSkJy5cvx4MHD+Dk5CTdY9vNzQ0ZGRkICwuTbg3266+/IjMzE61atVJbX4iIiKhiY+gmIiKNNWvWLPzwww+oWbMmPv30U6xevRr6+vpS8E5PT4eTkxOGDBkCABBCQCaTSffm5qZpREREpG5cXk5ERBopNjYWkZGRWLBgAXr37o0LFy7gyy+/REFBASwtLeHq6oo6derAx8cHBw8eBPDb/bjlcjkDNxEREWkEbqRGREQaoeSScuDVrLW7uzuGDBkChUKBbt26wd3dHV27dsWRI0dgaWmJwsJCHDx4EN27d5fOK15aTkRERKQJuLyciIjUrnhZOAAEBwcjIyMDP/74I7766ivMmjULAKBQKJCYmAh3d3dUrlwZCQkJqFSpkvQaRUVFnN0mIiIijcPQTUREalVyhnvWrFkICAhAq1atcP/+fejr6+PkyZOoWbMmgFfBOjExESNGjMC///1vrFmzRiWwExEREWka1nQTEZFaFQfu9PR0pKWlITExEYcOHcK2bdtgYGCA7t274/nz5wAAHR0ddOvWDfHx8Vi9ejUALicnIiIizcbQTUREardu3To0a9YMV69ehZGRESpVqoR27drhhx9+gBAC3bp1w4sXLwAAurq66NChA3R0dFBUVKTmlhMRERH9PoZuIiJSu2+//RYdO3bETz/9hEePHkmPf/LJJ4iKioJMJkPz5s2Rm5urch5ruImIiEjTsaabiIjKVOldyos9fvwYVlZWePHiBXbs2IEmTZpIz50/fx7Lly9HWFgYgzYRERFpFYZuIiIqMyV3GL948SIUCgVMTU1hZmYGAMjIyEDv3r2Rn5+PuLg4leD9ptcgIiIi0nRcXk5ERO/dkydPIISQwrK3tzcGDBiAwYMHo0WLFggPD0dmZiZMTEywf/9+VK5cGdbW1rh27dprr8XATURERNqEoZuIiN6r1q1bw8/PT9plfM6cOQgNDUVwcDBu3bqFAQMGwMXFBaGhocjKykKtWrWwb98+ZGVlwdfXV82tJyIiIvp7dNXdACIiKr98fHwgk8mk8JyWlobk5GSEhITgm2++QXx8PHbv3o2vvvoKHh4eAIDRo0ejVq1auHDhAqpUqaLO5hMRERH9bQzdRET03jx79gy6urqQy+WYPn06FAoF7Ozs0KtXLxw7dgwTJkyAj48PnJ2dYWdnhwULFiA7OxvTpk2DsbExANZwExERkXbjRmpERPSPE0JAJpPh+PHjGDt2LPT09HD79m1cvnwZxsbGqFKlCsaNG4fCwkKEhIRAT08PkyZNwokTJ2BoaIhjx45Jy9GJiIiItBlruomI6B9XHJi7dOmCevXq4eLFi+jcuTPMzMxQpUoV5Obm4saNGzAyMoKenh4A4N69e1i/fr0UuHlNmIiIiMoDhm4iInpvnj59Cj09PcydOxdpaWkYNmwYAMDQ0BBdunRBcHAwhg4dinbt2iE1NRUtW7aUAjdnuomIiKg84PJyIiJ6r4qKiiCXy7F+/XosWbIEbdu2RVRUFABg3rx5uHr1KoyNjREQEAA9PT3WcBMREVG5wtBNRERlIicnB1u3bsWiRYvQvn17/PDDDwCAly9fwsDAAACgUCigq8s9PomIiKj8YOgmIqIyk5OTg+joaPj5+aFu3brYu3evuptERERE9F5xOoGIiMqMkZERbGxskJOTgxMnTkCpVEIu5/YiREREVH5xppuIiMpcfn4+DAwMIJPJGLyJiIioXGPoJiIiteEu5URERFTecWqBiIjUhoGbiIiIyjuGbiIiIiIiIqL3hKGbiIiIiIiI6D1h6CYiIiIiIiJ6Txi6iYiIiIiIiN4Thm4iIiIiIiKi94Shm4iIiIiIiOg9YegmIiIiIiIiek8YuomIiIiIiIjeE4ZuIiIiIiIioveEoZuIiIiIiIjoPfk/iGwSS/NQxAcAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(df.shape)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LLp67L56IrbU",
+        "outputId": "f7ec53ac-938a-42bf-e3be-596bdbf800e0"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(3213, 24)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install xgboost scikit-learn pandas matplotlib\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jWr-eV3ejQaV",
+        "outputId": "198c586a-e65c-4e87-fcb9-3488508ca489"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: xgboost in /usr/local/lib/python3.12/dist-packages (3.0.5)\n",
+            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.12/dist-packages (1.6.1)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from xgboost) (2.0.2)\n",
+            "Requirement already satisfied: nvidia-nccl-cu12 in /usr/local/lib/python3.12/dist-packages (from xgboost) (2.27.3)\n",
+            "Requirement already satisfied: scipy in /usr/local/lib/python3.12/dist-packages (from xgboost) (1.16.1)\n",
+            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.5.2)\n",
+            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (3.6.0)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.59.2)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.4.9)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (25.0)\n",
+            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.2.3)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n",
+        "CSV_PATH = \"/content/drive/MyDrive/Capstone/Final_combined_dataset.csv\"\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DkM2fgVBj7Up",
+        "outputId": "0bc6b7c3-5776-4124-b6ae-e343fd136f8d"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "J0sdZEKClVs0",
+        "outputId": "355aa16e-5109-43b8-a099-c3288deaec10"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "\n",
+        "target_name = \"Final_combined_dataset.csv\"   # change if your file name differs\n",
+        "found = []\n",
+        "for root, dirs, files in os.walk(\"/content/drive/MyDrive\"):\n",
+        "    if target_name in files:\n",
+        "        found.append(os.path.join(root, target_name))\n",
+        "\n",
+        "print(\"Found paths:\")\n",
+        "for p in found:\n",
+        "    print(\" -\", p)\n",
+        "\n",
+        "# Use the first match:\n",
+        "CSV_PATH = found[0] if found else None\n",
+        "print(\"CSV_PATH =\", CSV_PATH)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FNUl1eoYmF4e",
+        "outputId": "890f9531-8615-46ca-c6e9-5ff1211730c7"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found paths:\n",
+            " - /content/drive/MyDrive/Final_combined_dataset.csv\n",
+            "CSV_PATH = /content/drive/MyDrive/Final_combined_dataset.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "df = pd.read_csv(CSV_PATH)\n",
+        "df.head()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 255
+        },
+        "id": "kEZ3YMfXmKqY",
+        "outputId": "6af57f80-e5cb-4b02-b065-9a11e0bb2848"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    age  height  weight  family_history_with_overweight  favc  fcvc  ncp  \\\n",
+              "0  17.0    1.65    67.0                               1     1   3.0  1.0   \n",
+              "1  18.0    1.56    51.0                               1     1   2.0  4.0   \n",
+              "2  17.0    1.75    57.0                               1     1   3.0  3.0   \n",
+              "3  15.0    1.65    86.0                               1     1   3.0  3.0   \n",
+              "4  17.0    1.70    85.0                               1     0   2.0  3.0   \n",
+              "\n",
+              "   smoke  ch2o  scc  ...  caec_Frequently  caec_Sometimes  calc_Frequently  \\\n",
+              "0      0   2.0    0  ...            False            True            False   \n",
+              "1      0   2.0    0  ...             True           False            False   \n",
+              "2      0   2.0    0  ...             True           False            False   \n",
+              "3      0   1.0    0  ...            False            True            False   \n",
+              "4      0   2.0    0  ...             True           False            False   \n",
+              "\n",
+              "   calc_Sometimes  calc_no  mtrans_Bike  mtrans_Motorbike  \\\n",
+              "0           False     True        False             False   \n",
+              "1            True    False        False             False   \n",
+              "2           False     True        False             False   \n",
+              "3           False     True        False             False   \n",
+              "4           False     True        False             False   \n",
+              "\n",
+              "   mtrans_Public_Transportation  mtrans_Walking  nobeyesdad  \n",
+              "0                         False            True           1  \n",
+              "1                          True           False           1  \n",
+              "2                          True           False           1  \n",
+              "3                         False            True           2  \n",
+              "4                          True           False           6  \n",
+              "\n",
+              "[5 rows x 24 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0aaab717-d7c6-44a8-abff-5db50a737cb5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>height</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>family_history_with_overweight</th>\n",
+              "      <th>favc</th>\n",
+              "      <th>fcvc</th>\n",
+              "      <th>ncp</th>\n",
+              "      <th>smoke</th>\n",
+              "      <th>ch2o</th>\n",
+              "      <th>scc</th>\n",
+              "      <th>...</th>\n",
+              "      <th>caec_Frequently</th>\n",
+              "      <th>caec_Sometimes</th>\n",
+              "      <th>calc_Frequently</th>\n",
+              "      <th>calc_Sometimes</th>\n",
+              "      <th>calc_no</th>\n",
+              "      <th>mtrans_Bike</th>\n",
+              "      <th>mtrans_Motorbike</th>\n",
+              "      <th>mtrans_Public_Transportation</th>\n",
+              "      <th>mtrans_Walking</th>\n",
+              "      <th>nobeyesdad</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1.65</td>\n",
+              "      <td>67.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1.56</td>\n",
+              "      <td>51.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1.75</td>\n",
+              "      <td>57.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>15.0</td>\n",
+              "      <td>1.65</td>\n",
+              "      <td>86.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1.70</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 24 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0aaab717-d7c6-44a8-abff-5db50a737cb5')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-0aaab717-d7c6-44a8-abff-5db50a737cb5 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0aaab717-d7c6-44a8-abff-5db50a737cb5');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-d84d03da-4ab8-4a9b-8784-caf0f25bd017\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d84d03da-4ab8-4a9b-8784-caf0f25bd017')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
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+              "     width=\"24px\">\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-d84d03da-4ab8-4a9b-8784-caf0f25bd017 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install -q xgboost scikit-learn pandas matplotlib\n"
+      ],
+      "metadata": {
+        "id": "Bvk5vHG_naXC"
+      },
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "66prkSoAnatB",
+        "outputId": "1e9aa10a-ffc4-4747-fac7-528f6558d7c4"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# OPTION A: set the exact path (recommended)\n",
+        "CSV_PATH = \"/content/drive/MyDrive/Final_combined_dataset.csv\"  # <-- adjust if needed\n",
+        "\n",
+        "# OPTION B: search MyDrive for the file name\n",
+        "if CSV_PATH is None or CSV_PATH.strip() == \"\":\n",
+        "    import os\n",
+        "    target_name = \"Final_combined_dataset.csv\"   # change if your file name differs\n",
+        "    found = []\n",
+        "    for root, dirs, files in os.walk(\"/content/drive/MyDrive\"):\n",
+        "        if target_name in files:\n",
+        "            found.append(os.path.join(root, target_name))\n",
+        "    print(\"Found paths:\")\n",
+        "    for p in found: print(\" -\", p)\n",
+        "    CSV_PATH = found[0] if found else None\n",
+        "\n",
+        "print(\"CSV_PATH =\", CSV_PATH)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RGlKyTouna10",
+        "outputId": "f02e8542-0b08-4c6a-9d1a-f380e251d68c"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "CSV_PATH = /content/drive/MyDrive/Final_combined_dataset.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "assert CSV_PATH is not None, \"CSV_PATH is not set. Edit the path above or use the search.\"\n",
+        "\n",
+        "df = pd.read_csv(CSV_PATH)\n",
+        "\n",
+        "# Normalize column names: strip spaces and make lower-case for consistency\n",
+        "df.columns = [c.strip().lower() for c in df.columns]\n",
+        "\n",
+        "print(df.shape)\n",
+        "df.head()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 272
+        },
+        "id": "5oSBi8euna6g",
+        "outputId": "fa6d5df9-00a7-4d59-da1e-0533390bbb45"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(3213, 24)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    age  height  weight  family_history_with_overweight  favc  fcvc  ncp  \\\n",
+              "0  17.0    1.65    67.0                               1     1   3.0  1.0   \n",
+              "1  18.0    1.56    51.0                               1     1   2.0  4.0   \n",
+              "2  17.0    1.75    57.0                               1     1   3.0  3.0   \n",
+              "3  15.0    1.65    86.0                               1     1   3.0  3.0   \n",
+              "4  17.0    1.70    85.0                               1     0   2.0  3.0   \n",
+              "\n",
+              "   smoke  ch2o  scc  ...  caec_frequently  caec_sometimes  calc_frequently  \\\n",
+              "0      0   2.0    0  ...            False            True            False   \n",
+              "1      0   2.0    0  ...             True           False            False   \n",
+              "2      0   2.0    0  ...             True           False            False   \n",
+              "3      0   1.0    0  ...            False            True            False   \n",
+              "4      0   2.0    0  ...             True           False            False   \n",
+              "\n",
+              "   calc_sometimes  calc_no  mtrans_bike  mtrans_motorbike  \\\n",
+              "0           False     True        False             False   \n",
+              "1            True    False        False             False   \n",
+              "2           False     True        False             False   \n",
+              "3           False     True        False             False   \n",
+              "4           False     True        False             False   \n",
+              "\n",
+              "   mtrans_public_transportation  mtrans_walking  nobeyesdad  \n",
+              "0                         False            True           1  \n",
+              "1                          True           False           1  \n",
+              "2                          True           False           1  \n",
+              "3                         False            True           2  \n",
+              "4                          True           False           6  \n",
+              "\n",
+              "[5 rows x 24 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e941ff15-aef6-469a-b13e-1a92823bbaf8\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>height</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>family_history_with_overweight</th>\n",
+              "      <th>favc</th>\n",
+              "      <th>fcvc</th>\n",
+              "      <th>ncp</th>\n",
+              "      <th>smoke</th>\n",
+              "      <th>ch2o</th>\n",
+              "      <th>scc</th>\n",
+              "      <th>...</th>\n",
+              "      <th>caec_frequently</th>\n",
+              "      <th>caec_sometimes</th>\n",
+              "      <th>calc_frequently</th>\n",
+              "      <th>calc_sometimes</th>\n",
+              "      <th>calc_no</th>\n",
+              "      <th>mtrans_bike</th>\n",
+              "      <th>mtrans_motorbike</th>\n",
+              "      <th>mtrans_public_transportation</th>\n",
+              "      <th>mtrans_walking</th>\n",
+              "      <th>nobeyesdad</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1.65</td>\n",
+              "      <td>67.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1.56</td>\n",
+              "      <td>51.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1.75</td>\n",
+              "      <td>57.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>15.0</td>\n",
+              "      <td>1.65</td>\n",
+              "      <td>86.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1.70</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 24 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e941ff15-aef6-469a-b13e-1a92823bbaf8')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e941ff15-aef6-469a-b13e-1a92823bbaf8 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e941ff15-aef6-469a-b13e-1a92823bbaf8');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-0de6b3f1-e4b7-4213-a9f6-32139b5efe02\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0de6b3f1-e4b7-4213-a9f6-32139b5efe02')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-0de6b3f1-e4b7-4213-a9f6-32139b5efe02 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from pathlib import Path\n",
+        "\n",
+        "# ---- CONFIG ----\n",
+        "TARGET_COL      = \"nobeyesdad\"   # label with 7 classes in this dataset\n",
+        "HEIGHT_CM_COL   = \"height\"\n",
+        "WEIGHT_KG_COL   = \"weight\"\n",
+        "AGE_COL         = \"age\"\n",
+        "RANDOM_SEED     = 42\n",
+        "OUT_DIR = Path(\"/content/week9_xgb_outputs\"); OUT_DIR.mkdir(exist_ok=True)\n",
+        "# ----------------\n",
+        "\n",
+        "# Safety checks (will warn instead of crash)\n",
+        "for c in [TARGET_COL, HEIGHT_CM_COL, WEIGHT_KG_COL, AGE_COL]:\n",
+        "    if c not in df.columns:\n",
+        "        print(f\"⚠️  Column '{c}' not found in df.columns\")\n",
+        "\n",
+        "# --- Privacy: age bands (if age exists)\n",
+        "if AGE_COL in df.columns:\n",
+        "    df[\"ageband\"] = pd.cut(\n",
+        "        df[AGE_COL],\n",
+        "        bins=[0, 18, 25, 35, 45, 60, 200],\n",
+        "        labels=[\"0-18\",\"19-25\",\"26-35\",\"36-45\",\"46-60\",\"60+\"]\n",
+        "    )\n",
+        "\n",
+        "# --- BMI feature (if height & weight exist and 'bmi' not already present)\n",
+        "if HEIGHT_CM_COL in df.columns and WEIGHT_KG_COL in df.columns and \"bmi\" not in df.columns:\n",
+        "    height_m = df[HEIGHT_CM_COL] / 100.0\n",
+        "    with np.errstate(divide='ignore', invalid='ignore'):\n",
+        "        df[\"bmi\"] = df[WEIGHT_KG_COL] / (height_m ** 2)\n",
+        "\n",
+        "print(\"Added features:\", [c for c in [\"ageband\",\"bmi\"] if c in df.columns])\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qBZeCK2Vnl10",
+        "outputId": "913f6902-3d61-44cf-a166-c40e0f4d0d8c"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Added features: ['ageband', 'bmi']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "\n",
+        "# Ensure target exists\n",
+        "assert TARGET_COL in df.columns, f\"Target column '{TARGET_COL}' not found. Actual: {df.columns.tolist()}\"\n",
+        "\n",
+        "# Convert boolean columns to integers so SimpleImputer accepts them\n",
+        "bool_cols_in_df = df.select_dtypes(include=[\"bool\"]).columns.tolist()\n",
+        "for col in bool_cols_in_df:\n",
+        "    df[col] = df[col].astype(int)\n",
+        "\n",
+        "# Split X/y\n",
+        "y_raw = df[TARGET_COL].astype(str)\n",
+        "X = df.drop(columns=[TARGET_COL])\n",
+        "\n",
+        "# Separate numeric/categorical\n",
+        "numeric_cols = X.select_dtypes(include=[np.number]).columns.tolist()\n",
+        "categorical_cols = [c for c in X.columns if c not in numeric_cols]\n",
+        "\n",
+        "# Impute numeric & categorical separately\n",
+        "num_imp = SimpleImputer(strategy=\"median\")\n",
+        "cat_imp = SimpleImputer(strategy=\"most_frequent\")\n",
+        "\n",
+        "X_num = (pd.DataFrame(num_imp.fit_transform(X[numeric_cols]),\n",
+        "                      columns=numeric_cols, index=X.index)\n",
+        "         if numeric_cols else pd.DataFrame(index=X.index))\n",
+        "\n",
+        "X_cat = (pd.DataFrame(cat_imp.fit_transform(X[categorical_cols]),\n",
+        "                      columns=categorical_cols, index=X.index)\n",
+        "         if categorical_cols else pd.DataFrame(index=X.index))\n",
+        "\n",
+        "# One-hot encode categoricals\n",
+        "X_pre = pd.get_dummies(pd.concat([X_num, X_cat], axis=1), drop_first=False)\n",
+        "\n",
+        "# Train/test split\n",
+        "X_train, X_test, y_train, y_test = train_test_split(\n",
+        "    X_pre, y_raw, test_size=0.2, stratify=y_raw, random_state=RANDOM_SEED\n",
+        ")\n",
+        "\n",
+        "X_pre.shape, X_train.shape, X_test.shape\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XT8LWCzena-c",
+        "outputId": "8a3bb78e-dabc-4511-e87c-7aa5bbad979e"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "((3213, 25), (2570, 25), (643, 25))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
+        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
+        "from xgboost import XGBClassifier\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Encode labels\n",
+        "le = LabelEncoder()\n",
+        "y_train_enc = le.fit_transform(y_train)\n",
+        "y_test_enc  = le.transform(y_test)\n",
+        "num_classes = len(le.classes_)\n",
+        "pd.Series(le.classes_).to_csv(OUT_DIR / \"label_classes.csv\", index=False, header=[\"class_names\"])\n",
+        "\n",
+        "# Model\n",
+        "xgb = XGBClassifier(\n",
+        "    n_estimators=400, learning_rate=0.05, max_depth=6,\n",
+        "    subsample=0.9, colsample_bytree=0.9,\n",
+        "    objective=\"multi:softprob\", num_class=num_classes,\n",
+        "    reg_lambda=1.0, random_state=RANDOM_SEED,\n",
+        "    tree_method=\"hist\", eval_metric=\"mlogloss\"\n",
+        ")\n",
+        "\n",
+        "# 5-fold CV on train\n",
+        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_SEED)\n",
+        "cv_scores = cross_val_score(xgb, X_train, y_train_enc, scoring=\"accuracy\", cv=cv, n_jobs=-1)\n",
+        "pd.Series(cv_scores, name=\"cv_accuracy\").to_csv(OUT_DIR / \"cv_scores.csv\", index=False)\n",
+        "\n",
+        "# Fit & evaluate\n",
+        "xgb.fit(X_train, y_train_enc)\n",
+        "y_pred = xgb.predict(X_test)\n",
+        "acc = accuracy_score(y_test_enc, y_pred)\n",
+        "\n",
+        "# Classification report\n",
+        "report = classification_report(\n",
+        "    y_test_enc, y_pred,\n",
+        "    labels=np.arange(len(le.classes_)),   # force all classes\n",
+        "    target_names=le.classes_,\n",
+        "    output_dict=True,\n",
+        "    zero_division=0   # avoids divide by zero warnings\n",
+        ")\n",
+        "\n",
+        "pd.DataFrame(report).transpose().to_csv(OUT_DIR / \"classification_report.csv\")\n",
+        "\n",
+        "# Confusion matrix + plot\n",
+        "cm = confusion_matrix(y_test_enc, y_pred)\n",
+        "np.savetxt(OUT_DIR / \"confusion_matrix.csv\", cm, delimiter=\",\", fmt=\"%d\")\n",
+        "\n",
+        "plt.figure(figsize=(7,6))\n",
+        "plt.imshow(cm, interpolation=\"nearest\")\n",
+        "plt.title(\"XGBoost Confusion Matrix (Week 9)\")\n",
+        "plt.xlabel(\"Predicted\"); plt.ylabel(\"True\"); plt.colorbar()\n",
+        "plt.xticks(ticks=np.arange(num_classes), labels=le.classes_, rotation=45, ha=\"right\")\n",
+        "plt.yticks(ticks=np.arange(num_classes), labels=le.classes_)\n",
+        "plt.tight_layout(); plt.savefig(OUT_DIR / \"confusion_matrix.png\", dpi=160); plt.close()\n",
+        "\n",
+        "# Feature importances\n",
+        "fi = pd.Series(xgb.feature_importances_, index=X_train.columns).sort_values(ascending=False)\n",
+        "fi.to_csv(OUT_DIR / \"feature_importances.csv\")\n",
+        "topk = min(20, len(fi))\n",
+        "plt.figure(figsize=(8,6))\n",
+        "fi.head(topk).iloc[::-1].plot(kind=\"barh\")\n",
+        "plt.title(\"Top Feature Importances (XGBoost)\")\n",
+        "plt.tight_layout(); plt.savefig(OUT_DIR / \"feature_importances_top.png\", dpi=160); plt.close()\n",
+        "\n",
+        "# Summary metrics\n",
+        "summary = {\n",
+        "    \"test_accuracy\": round(acc, 4),\n",
+        "    \"cv_mean_accuracy\": round(cv_scores.mean(), 4),\n",
+        "    \"cv_std\": round(cv_scores.std(), 4),\n",
+        "    \"num_features\": int(X_train.shape[1]),\n",
+        "    \"num_classes\": int(num_classes),\n",
+        "}\n",
+        "pd.Series(summary).to_csv(OUT_DIR / \"summary_metrics.csv\")\n",
+        "print(\"✅ Done. Artifacts in:\", OUT_DIR)\n",
+        "print(summary)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RFG0iEiNns9d",
+        "outputId": "9b732717-580f-4f3a-ece7-89de38d97e3a"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/sklearn/model_selection/_split.py:805: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Done. Artifacts in: /content/week9_xgb_outputs\n",
+            "{'test_accuracy': 0.8585, 'cv_mean_accuracy': np.float64(0.8732), 'cv_std': np.float64(0.0083), 'num_features': 25, 'num_classes': 7}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "DtK5BL24ntJg"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/data_science/shashank_week9_xgboost/outputs/classification_report.csv b/data_science/shashank_week9_xgboost/outputs/classification_report.csv
new file mode 100644
index 0000000..a9d614c
--- /dev/null
+++ b/data_science/shashank_week9_xgboost/outputs/classification_report.csv
@@ -0,0 +1,11 @@
+,precision,recall,f1-score,support
+0,0.9411764705882353,0.9411764705882353,0.9411764705882353,221.0
+1,0.8439024390243902,0.8963730569948186,0.8693467336683417,193.0
+2,0.9148936170212766,0.9148936170212766,0.9148936170212766,94.0
+3,0.0,0.0,0.0,1.0
+4,0.0,0.0,0.0,0.0
+5,0.6065573770491803,0.578125,0.592,64.0
+6,0.7741935483870968,0.6857142857142857,0.7272727272727273,70.0
+accuracy,0.8584758942457231,0.8584758942457231,0.8584758942457231,0.8584758942457231
+macro avg,0.5829604931528828,0.5737546329026594,0.577812792650083,643.0
+weighted avg,0.8551887888801737,0.8584758942457231,0.8562690676626451,643.0
diff --git a/data_science/shashank_week9_xgboost/outputs/confusion_matrix.csv b/data_science/shashank_week9_xgboost/outputs/confusion_matrix.csv
new file mode 100644
index 0000000..7207dcd
--- /dev/null
+++ b/data_science/shashank_week9_xgboost/outputs/confusion_matrix.csv
@@ -0,0 +1,7 @@
+208,13,0,0,0,0,0
+10,173,0,0,0,8,2
+1,0,86,0,0,3,4
+0,0,1,0,0,0,0
+0,0,0,0,0,0,0
+2,16,1,0,0,37,8
+0,3,6,0,0,13,48
diff --git a/data_science/shashank_week9_xgboost/outputs/confusion_matrix.png b/data_science/shashank_week9_xgboost/outputs/confusion_matrix.png
new file mode 100644
index 0000000..8e3c486
Binary files /dev/null and b/data_science/shashank_week9_xgboost/outputs/confusion_matrix.png differ
diff --git a/data_science/shashank_week9_xgboost/outputs/cv_scores.csv b/data_science/shashank_week9_xgboost/outputs/cv_scores.csv
new file mode 100644
index 0000000..f27396a
--- /dev/null
+++ b/data_science/shashank_week9_xgboost/outputs/cv_scores.csv
@@ -0,0 +1,4 @@
+cv_accuracy
+0.8588098016336057
+0.8763127187864644
+0.8644859813084113
diff --git a/data_science/shashank_week9_xgboost/outputs/feature_importances.csv b/data_science/shashank_week9_xgboost/outputs/feature_importances.csv
new file mode 100644
index 0000000..3213425
--- /dev/null
+++ b/data_science/shashank_week9_xgboost/outputs/feature_importances.csv
@@ -0,0 +1,26 @@
+,0
+bmi,0.26418245
+weight,0.07700983
+scc,0.07038342
+favc,0.06095557
+gender_male,0.055255312
+ncp,0.054393437
+age,0.03890668
+tue,0.03526925
+caec_always,0.03361133
+caec_frequently,0.03208275
+mtrans_public_transportation,0.029494468
+height,0.02882162
+caec_sometimes,0.028653089
+mtrans_walking,0.027856175
+family_history_with_overweight,0.027728962
+calc_frequently,0.025763191
+ch2o,0.02544201
+fcvc,0.024752429
+faf,0.023884656
+calc_sometimes,0.023301115
+calc_no,0.012252272
+smoke,0.0
+mtrans_bike,0.0
+mtrans_motorbike,0.0
+ageband_0-18,0.0
diff --git a/data_science/shashank_week9_xgboost/outputs/feature_importances_top.png b/data_science/shashank_week9_xgboost/outputs/feature_importances_top.png
new file mode 100644
index 0000000..fa36229
Binary files /dev/null and b/data_science/shashank_week9_xgboost/outputs/feature_importances_top.png differ
diff --git a/data_science/shashank_week9_xgboost/outputs/label_classes.csv b/data_science/shashank_week9_xgboost/outputs/label_classes.csv
new file mode 100644
index 0000000..389adf5
--- /dev/null
+++ b/data_science/shashank_week9_xgboost/outputs/label_classes.csv
@@ -0,0 +1,8 @@
+class_names
+0
+1
+2
+3
+4
+5
+6
diff --git a/data_science/shashank_week9_xgboost/outputs/obesity_predictions.csv b/data_science/shashank_week9_xgboost/outputs/obesity_predictions.csv
new file mode 100644
index 0000000..597f3b9
--- /dev/null
+++ b/data_science/shashank_week9_xgboost/outputs/obesity_predictions.csv
@@ -0,0 +1,424 @@
+Gender,Age,Height,Weight,family_history_with_overweight,FAVC,FCVC,NCP,CAEC,SMOKE,CH2O,SCC,FAF,TUE,CALC,MTRANS,Actual,Predicted
+Female,19.084967,1.768435,49.597765,no,yes,3.0,3.725797,Sometimes,no,1.191401,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Male,19.0,1.8,60.0,no,yes,2.0,3.0,Sometimes,no,1.0,no,1.0,1.0,no,Public_Transportation,Normal_Weight,Normal_Weight
+Male,24.108711,1.7,80.761409,yes,yes,2.0,3.0,Sometimes,no,2.879402,no,0.0,0.322405,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,20.941943,1.812963,138.730619,yes,yes,3.0,3.0,Sometimes,no,2.641489,no,0.481555,0.735201,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,33.293166,1.696412,103.250355,yes,yes,2.679664,2.415522,Sometimes,no,1.0,no,1.987296,0.0,no,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Female,23.474165,1.577115,78.0,yes,yes,2.0,1.0,Sometimes,no,2.0,no,0.876101,0.0,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,26.699317,1.839941,119.956651,yes,yes,2.244654,3.0,Sometimes,no,2.795752,no,0.839481,0.0,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Female,23.0,1.65,59.0,no,no,3.0,1.0,Sometimes,no,2.0,no,1.0,2.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Female,43.591999,1.595165,77.354744,yes,yes,2.0,3.0,Sometimes,no,2.117346,no,0.0,0.0,no,Automobile,Obesity_Type_I,Obesity_Type_I
+Female,16.941489,1.551288,54.93242,no,yes,1.75375,1.296156,Sometimes,no,2.0,yes,0.110174,1.944675,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,18.006742,1.7,50.0,no,yes,1.003566,3.238258,Frequently,no,1.014634,no,0.783676,1.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Male,23.0,1.8,60.0,yes,no,2.0,3.0,no,no,3.0,no,0.0,1.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Female,26.0,1.62439,110.008636,yes,yes,3.0,3.0,Sometimes,no,2.507461,no,0.0,0.368472,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,19.858973,1.62,69.575315,no,yes,2.185938,1.0,no,no,2.81353,no,1.0,1.110222,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,23.0,1.63,55.0,yes,no,3.0,3.0,no,no,2.0,yes,2.0,1.0,no,Public_Transportation,Normal_Weight,Normal_Weight
+Male,22.429812,1.64037,70.0,no,yes,2.0,1.0,no,no,2.069184,no,0.729898,0.0,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,30.163408,1.533364,78.030383,yes,yes,2.028225,1.923607,Sometimes,no,1.798917,no,0.0,0.0,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,22.804818,1.613119,82.636162,yes,yes,2.964419,1.0,Sometimes,no,2.977516,no,0.742113,2.0,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,33.049121,1.708742,83.001642,yes,yes,2.0,1.171027,Sometimes,no,2.405172,no,2.300282,0.0,no,Automobile,Overweight_Level_II,Overweight_Level_II
+Female,18.0,1.59,53.0,no,no,1.0,3.0,Sometimes,no,1.0,no,1.0,2.0,no,Public_Transportation,Normal_Weight,Normal_Weight
+Female,18.0,1.56,55.0,no,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,0.0,Frequently,Automobile,Normal_Weight,Normal_Weight
+Male,26.684354,1.819535,118.332689,yes,yes,1.975675,3.0,Sometimes,no,2.357969,no,0.704236,0.010721,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,27.93982,1.694642,99.709329,yes,yes,2.772027,2.454432,Sometimes,no,1.0,no,0.858554,0.051268,no,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,21.0,1.709561,75.0,yes,yes,2.0,3.0,Sometimes,no,1.636326,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,24.0,1.7,80.0,yes,yes,2.0,3.0,Sometimes,no,3.0,no,0.0,0.0,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,21.909534,1.611356,68.06609,yes,yes,1.834155,3.995957,Sometimes,no,2.632871,no,1.236114,1.980875,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,40.466313,1.559005,77.601483,yes,yes,2.0,3.0,Sometimes,no,1.572371,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Female,26.0,1.631332,111.829957,yes,yes,3.0,3.0,Sometimes,no,2.55975,no,0.0,0.237307,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,46.491859,1.718097,88.600878,yes,yes,2.129969,3.0,Sometimes,no,1.568035,no,0.870127,0.0,no,Automobile,Overweight_Level_II,Overweight_Level_II
+Male,23.0,1.791415,105.138073,yes,yes,2.262171,3.0,Sometimes,no,2.03415,no,0.317363,0.508663,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,25.314589,1.787802,115.127662,yes,yes,1.588114,3.0,Sometimes,no,2.115967,no,1.541072,0.0,no,Automobile,Obesity_Type_II,Obesity_Type_II
+Male,23.365649,1.757691,95.361795,yes,yes,2.0,3.0,Sometimes,no,3.0,no,3.0,2.0,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,16.240576,1.616533,65.062945,yes,yes,2.388168,1.0,Sometimes,no,1.438018,yes,0.110887,1.165817,no,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Male,23.367212,1.853223,87.083266,yes,yes,2.0,1.097312,Sometimes,no,1.328835,no,1.264257,1.0,Sometimes,Automobile,Overweight_Level_I,Overweight_Level_I
+Male,18.0,1.87,173.0,yes,yes,3.0,3.0,Frequently,no,2.0,no,2.0,1.0,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_I
+Female,26.0,1.65,63.0,no,yes,3.0,3.0,Sometimes,no,1.0,no,1.0,0.0,no,Walking,Normal_Weight,Normal_Weight
+Male,29.891473,1.76003,112.409191,yes,yes,2.262292,3.0,Sometimes,no,2.115354,no,0.0,0.847587,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Female,18.281092,1.7,50.0,no,yes,1.036414,3.205587,Sometimes,no,1.745959,no,0.115974,1.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,20.217015,1.71582,129.466541,yes,yes,3.0,3.0,Sometimes,no,1.755907,no,1.48809,0.857718,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,19.724106,1.734832,50.0,no,no,1.123939,4.0,Frequently,no,1.0,no,1.303976,0.371941,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Male,21.963457,1.697228,75.5771,yes,yes,2.204914,3.623364,Sometimes,no,1.815293,no,0.989316,0.0,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,19.86997,1.520997,42.0,no,yes,3.0,1.0,Frequently,no,1.527036,no,0.0,0.860497,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,20.924956,1.752531,133.618706,yes,yes,3.0,3.0,Sometimes,no,2.887659,no,1.480919,0.779641,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,25.470652,1.680218,104.807284,yes,yes,3.0,3.0,Sometimes,no,1.423073,no,0.197993,0.763359,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,23.0,1.70074,81.32297,yes,yes,2.0,2.720642,Sometimes,no,1.5197,no,0.279375,1.043435,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,26.0,1.629191,104.826776,yes,yes,3.0,3.0,Sometimes,no,2.654702,no,0.0,0.555468,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,21.0,1.52,42.0,no,yes,3.0,1.0,Frequently,no,1.0,no,0.0,0.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Male,21.123048,1.717037,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,2.270555,1.343044,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,31.627962,1.762389,118.548733,yes,yes,2.998441,3.0,Sometimes,no,2.462916,no,0.554646,1.99219,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Male,19.0,1.75,76.0,yes,yes,3.0,3.0,Sometimes,no,2.0,yes,3.0,1.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Female,25.289428,1.686033,104.772164,yes,yes,3.0,3.0,Sometimes,no,1.299194,no,0.234303,0.946888,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,19.0,1.53161,42.0,no,yes,2.749268,1.394539,Frequently,no,1.322048,no,0.463949,0.800993,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,22.239836,1.59994,65.423942,no,yes,2.99448,3.0,Sometimes,no,1.706568,no,0.784216,0.150969,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Male,20.803186,1.882533,117.468516,yes,yes,2.0,2.18162,Sometimes,no,2.325091,no,0.891444,1.223661,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,18.701766,1.704908,81.384224,yes,yes,2.650629,1.0,Sometimes,no,1.708083,no,1.876051,0.938791,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,18.0,1.56,51.0,yes,yes,2.0,4.0,Frequently,no,2.0,no,1.0,0.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Female,20.700876,1.68838,121.889803,yes,yes,3.0,3.0,Sometimes,no,1.353167,no,1.093679,0.534553,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,19.364339,1.80835,150.51648,yes,yes,3.0,3.0,Sometimes,no,2.305574,no,1.734424,0.863043,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,21.051107,1.753266,133.852432,yes,yes,3.0,3.0,Sometimes,no,2.95311,no,1.445148,0.67321,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,28.393111,1.685462,99.540122,yes,yes,2.774562,2.301129,Sometimes,no,1.0,no,1.621733,0.138314,no,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,21.403421,1.716308,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,2.936551,1.40913,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,34.243146,1.843172,92.69551,yes,yes,2.0,2.271734,Sometimes,no,2.629043,no,1.0,0.075823,Sometimes,Automobile,Overweight_Level_II,Overweight_Level_II
+Male,17.0,1.848294,59.409018,yes,yes,2.44004,3.0,Sometimes,no,2.0,no,2.784471,1.0,no,Automobile,Insufficient_Weight,Insufficient_Weight
+Female,20.820455,1.547066,67.473282,yes,no,2.303656,3.0,Sometimes,no,1.163111,no,1.926381,1.119877,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,18.052394,1.725233,75.970712,yes,yes,2.927409,3.0,Sometimes,no,2.209991,no,1.0,0.797215,Frequently,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,38.547267,1.526472,75.150345,yes,yes,2.0,3.0,Sometimes,no,1.084442,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Female,25.706285,1.585547,80.351263,yes,yes,2.0,1.0,Sometimes,no,2.0,no,0.0,0.0,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,18.0,1.71889,52.058335,yes,yes,1.950742,3.0,Sometimes,no,1.751723,no,0.201136,1.743319,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Male,30.710511,1.914186,129.852531,yes,yes,2.197261,3.0,Sometimes,no,1.11184,no,0.906843,0.976473,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,21.077356,1.702002,97.971598,yes,yes,2.0,1.66338,Sometimes,no,2.0,no,0.0,1.865851,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,24.497373,1.737056,132.527011,yes,yes,3.0,3.0,Sometimes,no,2.86559,no,0.973864,0.58345,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,18.0,1.622241,68.250249,yes,yes,2.0,1.010319,Sometimes,no,1.319437,no,0.0,1.626456,no,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,19.735968,1.699956,49.98297,no,yes,1.204855,3.531038,Sometimes,no,1.134658,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,17.082867,1.640824,43.365005,no,yes,2.815157,3.0,Sometimes,no,2.911187,no,2.595128,1.380204,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,38.445148,1.556028,77.684229,yes,yes,2.0,3.0,Sometimes,no,1.440629,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Female,19.994543,1.537739,39.101805,no,yes,1.118436,3.0,Sometimes,no,1.997744,no,2.432443,1.626194,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,19.483036,1.53777,42.0,no,yes,3.0,1.0,Frequently,no,1.387531,no,0.0,0.466169,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,21.0,1.52,42.0,no,yes,3.0,1.0,Frequently,no,1.0,no,0.0,0.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,22.997168,1.655742,82.46376,yes,yes,2.73691,1.478334,Sometimes,no,2.677409,no,0.065264,0.40699,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,23.0,1.634688,82.628,yes,yes,2.060922,2.933409,Sometimes,no,2.235282,no,0.380633,0.0026,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,19.0,1.65,61.0,no,yes,3.0,1.0,Sometimes,no,3.0,yes,1.0,0.0,Sometimes,Public_Transportation,Normal_Weight,Overweight_Level_I
+Male,23.806789,1.732492,84.557797,yes,yes,2.992205,3.0,Sometimes,no,2.413813,no,0.458237,0.688293,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,26.0,1.594776,110.640929,yes,yes,3.0,3.0,Sometimes,no,2.639816,no,0.0,0.452236,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,22.969366,1.701634,95.0,yes,yes,2.0,3.0,Sometimes,no,2.133469,no,1.030199,1.082838,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,21.0,1.63,60.0,yes,yes,3.0,3.0,Always,yes,2.0,no,2.0,0.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Female,28.0,1.7,73.0,yes,no,2.0,3.0,Frequently,no,2.0,yes,2.0,0.0,Sometimes,Walking,Overweight_Level_I,Normal_Weight
+Male,38.112989,1.766888,118.134898,yes,yes,2.240757,2.911568,Sometimes,no,1.895876,no,0.822186,0.0,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Male,21.0,1.62,70.0,no,yes,2.0,1.0,no,no,3.0,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,37.496175,1.653088,80.0,yes,yes,2.033745,3.0,Sometimes,no,1.517225,no,0.0,1.517897,no,Automobile,Overweight_Level_II,Overweight_Level_II
+Male,33.0816,1.705617,83.016968,yes,yes,2.0,2.7976,Sometimes,no,2.991671,no,2.148738,0.0,no,Automobile,Overweight_Level_II,Overweight_Level_II
+Male,38.10894,1.752863,119.201465,yes,yes,2.499388,2.989791,Sometimes,no,1.959777,no,0.6081,0.64676,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Male,34.543563,1.765188,85.0,yes,yes,2.694281,3.0,Sometimes,no,2.653831,no,1.96582,0.891189,no,Automobile,Overweight_Level_II,Overweight_Level_II
+Female,26.220065,1.560258,78.435904,yes,yes,2.108163,1.231915,Sometimes,no,1.808127,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Male,30.0,1.76,112.0,yes,yes,1.0,3.0,no,yes,2.0,yes,0.0,0.0,Frequently,Automobile,Obesity_Type_II,Obesity_Type_II
+Female,21.165574,1.617749,68.908136,yes,yes,1.188089,3.269088,Sometimes,no,2.039535,no,0.779686,1.043108,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,18.0,1.54,71.0,no,no,3.0,4.0,Frequently,no,2.0,no,1.0,1.0,no,Public_Transportation,Overweight_Level_II,Normal_Weight
+Male,18.424941,1.753389,54.121925,yes,yes,2.0,3.16645,Sometimes,no,2.278578,no,1.838881,2.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Male,18.0,1.76,69.5,no,no,3.0,3.0,Frequently,no,3.0,no,2.0,2.0,no,Public_Transportation,Normal_Weight,Normal_Weight
+Female,23.421726,1.755467,133.478611,yes,yes,3.0,3.0,Sometimes,no,2.843782,no,1.302507,0.773807,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,22.851721,1.853373,121.737836,yes,yes,2.922511,2.692889,Sometimes,no,1.809531,no,0.478595,0.0,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Female,31.386405,1.556579,78.233341,yes,yes,2.13683,2.092179,Sometimes,no,1.505381,no,0.0,0.0,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,21.721507,1.849998,122.119682,yes,yes,3.0,2.014671,Sometimes,no,1.292786,no,0.145687,0.0,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,21.0,1.85,125.0,yes,yes,3.0,1.0,Always,no,1.0,no,0.0,0.0,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,21.0,1.62,70.0,no,yes,2.0,1.0,no,no,3.0,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,18.549437,1.545196,72.467862,yes,no,3.0,3.014808,Sometimes,no,2.0,no,1.997529,1.0,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,20.601222,1.738717,128.114161,yes,yes,3.0,3.0,Sometimes,no,1.797041,no,1.427413,0.966181,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,29.506287,1.82697,108.751502,yes,yes,2.0,2.877583,Sometimes,no,2.358038,no,0.877295,1.817146,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Female,38.0,1.56,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Male,34.432669,1.694997,84.451423,yes,yes,2.129668,2.693646,Sometimes,no,2.60669,no,1.170823,0.290898,no,Automobile,Overweight_Level_II,Overweight_Level_II
+Male,31.761799,1.751688,119.205308,yes,yes,2.14961,3.0,Sometimes,no,2.133876,no,0.393452,0.345887,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Male,55.0,1.65,80.0,no,yes,2.0,3.0,Sometimes,no,2.0,no,1.0,0.0,no,Automobile,Overweight_Level_II,Overweight_Level_II
+Male,23.0,1.68,62.0,no,no,2.0,4.0,Frequently,no,2.0,no,0.0,0.0,Sometimes,Automobile,Normal_Weight,Normal_Weight
+Female,20.801791,1.721476,131.042274,yes,yes,3.0,3.0,Sometimes,no,1.837184,no,1.525165,0.926443,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,37.056193,1.75015,118.206565,yes,yes,2.09283,3.0,Sometimes,no,2.077704,no,0.598655,0.0,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Female,23.76197,1.69135,114.480696,yes,yes,3.0,3.0,Sometimes,no,2.509535,no,0.334264,0.668172,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,21.394047,1.792933,137.832414,yes,yes,3.0,3.0,Sometimes,no,2.682909,no,1.318743,0.900497,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,20.244359,1.559186,41.952805,no,yes,2.619835,1.0,Frequently,no,1.198883,no,0.0,0.751212,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,18.862264,1.746277,128.705761,yes,yes,3.0,3.0,Sometimes,no,2.411582,no,0.985287,0.675076,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,19.0,1.6,60.0,no,yes,2.0,3.0,Frequently,no,2.0,no,0.0,1.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Male,23.0,1.68,60.0,no,no,2.0,4.0,Frequently,no,2.0,no,0.0,0.0,no,Walking,Normal_Weight,Normal_Weight
+Female,25.493586,1.673665,104.704707,yes,yes,3.0,3.0,Sometimes,no,1.170515,no,0.264831,0.896842,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,19.462713,1.550122,69.936073,yes,no,2.501683,3.394788,Sometimes,no,1.337378,no,0.932888,1.0,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,19.012872,1.742062,133.779919,yes,yes,3.0,3.0,Sometimes,no,2.70196,no,1.465909,0.813235,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,20.850119,1.524926,42.0,no,yes,2.866383,1.226342,Frequently,no,1.0,no,0.14495,0.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,23.652435,1.562724,80.535698,yes,yes,2.0,1.0,Sometimes,no,2.0,no,0.389717,0.0,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,26.844812,1.69151,102.59518,yes,yes,2.680375,1.089048,Sometimes,no,1.366238,no,0.181324,1.041677,no,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Female,30.0,1.71,82.0,yes,yes,3.0,4.0,Frequently,yes,1.0,no,0.0,0.0,no,Automobile,Overweight_Level_II,Overweight_Level_II
+Male,23.0,1.87,95.0,yes,yes,1.0,3.0,Frequently,no,2.0,no,0.0,2.0,Frequently,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,33.732714,1.679725,74.885222,yes,yes,3.0,1.13715,Sometimes,no,1.000695,no,0.261274,0.0,Sometimes,Automobile,Overweight_Level_I,Overweight_Level_I
+Male,23.083621,1.848553,121.421121,yes,yes,3.0,2.567567,Sometimes,no,2.011023,no,0.916478,0.0,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Female,19.035557,1.682594,127.427458,yes,yes,3.0,3.0,Sometimes,no,1.441289,no,1.425712,0.662277,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,23.728707,1.663509,80.0,yes,yes,2.0,3.0,Sometimes,no,2.13755,no,0.0,0.124977,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,17.0,1.8,58.0,no,yes,2.0,3.0,Frequently,no,2.0,no,2.0,1.0,no,Walking,Insufficient_Weight,Normal_Weight
+Female,19.0,1.564199,42.096062,no,yes,3.0,1.894384,Sometimes,no,2.456581,yes,1.596576,0.9974,no,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Male,18.0,1.786758,51.524444,no,yes,2.91548,3.0,Sometimes,no,1.777486,no,1.077469,1.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,21.071195,1.616467,68.77185,yes,yes,1.452524,3.950553,no,no,2.109858,no,0.508847,1.194633,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Male,19.0,1.85,115.0,no,no,2.0,3.0,Sometimes,no,3.0,yes,1.0,2.0,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,37.0,1.5,75.0,yes,yes,2.0,3.0,Frequently,no,1.0,no,0.0,0.0,Sometimes,Motorbike,Obesity_Type_I,Normal_Weight
+Male,32.29016,1.754956,120.098812,yes,yes,2.9673,3.0,Sometimes,no,2.530035,no,0.955317,1.339232,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Male,18.0,1.707259,50.664459,yes,yes,1.786841,3.0,Sometimes,no,1.546856,no,0.512094,1.608265,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,20.81158,1.741193,128.763843,yes,yes,3.0,3.0,Sometimes,no,1.768111,no,0.616503,0.968151,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,20.993067,1.782269,104.97003,yes,yes,2.0,3.0,Sometimes,no,1.234943,no,0.008368,0.0,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,22.906342,1.755902,120.021161,yes,yes,2.971574,2.80742,Sometimes,no,1.962646,no,1.583832,0.504816,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Female,22.518787,1.634342,82.414477,yes,yes,1.853314,1.320768,Sometimes,no,2.135552,no,0.248034,1.727828,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,31.335093,1.665798,89.738596,yes,yes,2.274164,1.049534,Sometimes,no,1.358172,no,1.482411,0.0,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Male,30.002029,1.759324,112.000381,yes,yes,1.572036,3.0,Sometimes,no,2.003563,no,0.0,0.340196,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Male,33.0,1.85,99.0,yes,yes,2.0,3.0,Sometimes,yes,3.0,no,1.0,0.0,Sometimes,Automobile,Overweight_Level_II,Overweight_Level_II
+Male,21.72738,1.783782,105.000276,yes,yes,2.044326,3.0,Sometimes,no,2.35818,no,0.349371,0.0,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,23.0,1.882779,64.106108,yes,yes,3.0,3.0,Sometimes,no,2.843777,no,1.488843,0.009254,Sometimes,Automobile,Insufficient_Weight,Insufficient_Weight
+Female,30.255744,1.652084,73.575981,yes,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,0.018877,Sometimes,Automobile,Overweight_Level_I,Overweight_Level_I
+Female,29.463168,1.659172,71.170001,yes,yes,2.0,3.0,Sometimes,no,1.281683,no,1.609801,0.0,Sometimes,Automobile,Overweight_Level_I,Overweight_Level_I
+Male,22.717249,1.708071,75.0,yes,yes,2.714447,3.0,Sometimes,no,3.0,no,1.096818,1.89388,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,18.904037,1.743589,133.281333,yes,yes,3.0,3.0,Sometimes,no,2.684819,no,1.46525,0.804491,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,19.408999,1.670552,49.804245,no,yes,2.794197,3.409363,Sometimes,no,1.543021,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Male,22.705772,1.841989,122.024954,yes,yes,3.0,2.52751,Sometimes,no,1.645338,no,1.025438,0.0,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,24.82929,1.755967,112.256165,yes,yes,1.369529,3.0,Sometimes,no,2.0,no,1.596576,0.0026,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Female,26.0,1.643017,111.720238,yes,yes,3.0,3.0,Sometimes,no,2.632498,no,0.0,0.140792,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,30.967417,1.688436,90.0,yes,yes,2.180047,2.733077,Sometimes,no,2.77962,no,1.454129,0.0,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Female,17.781183,1.6,65.0,yes,yes,3.0,2.973504,Sometimes,no,2.0,yes,0.178976,0.166076,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Male,21.997335,1.65,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.527341,2.0,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,21.0,1.81,80.0,no,no,1.0,3.0,no,no,2.0,no,2.0,0.0,no,Public_Transportation,Normal_Weight,Normal_Weight
+Male,27.558801,1.801224,119.050381,yes,yes,2.744994,3.0,Sometimes,no,2.478838,no,0.684487,0.677909,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,23.0,1.774644,105.966894,yes,yes,2.312825,3.0,Sometimes,no,2.073497,no,0.614466,0.579541,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,22.771612,1.750384,95.324282,yes,yes,2.0,3.0,Sometimes,no,3.0,no,3.0,2.0,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,20.741442,1.694439,122.813033,yes,yes,3.0,3.0,Sometimes,no,1.409444,no,0.933595,0.840393,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,17.971574,1.720379,85.0,yes,yes,2.0,3.0,Sometimes,no,2.802498,no,1.0,0.41758,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,31.62638,1.666023,72.906186,yes,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,1.301385,Sometimes,Automobile,Overweight_Level_I,Overweight_Level_I
+Male,18.0,1.75,85.0,yes,no,2.0,3.0,Sometimes,no,3.0,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,23.0,1.718981,81.66995,yes,yes,2.0,1.729553,Sometimes,no,1.400247,no,0.887923,1.011983,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,25.550506,1.77274,114.254278,yes,yes,2.175276,3.0,Sometimes,no,2.128176,no,1.306476,0.079334,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,18.128206,1.778447,80.273807,yes,yes,1.570089,3.0,Sometimes,no,2.0,no,2.707882,0.0,no,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Male,17.0,1.7,55.0,no,yes,3.0,3.0,Always,no,2.0,no,3.0,1.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Male,21.0,1.8,73.0,yes,yes,1.0,3.0,Always,no,2.0,no,3.0,1.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Male,30.958051,1.906821,128.856677,yes,yes,2.633855,3.0,Sometimes,no,1.619305,no,1.847802,0.424612,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,26.225442,1.773664,116.160329,yes,yes,2.442536,3.0,Sometimes,no,2.174371,no,0.0,1.928972,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Female,16.30687,1.616366,67.183128,yes,yes,1.620845,1.0,Sometimes,no,2.0,yes,0.926592,1.657098,no,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Male,24.481032,1.661277,90.744965,yes,yes,1.712848,3.0,Sometimes,no,1.162153,no,0.458981,0.885532,no,Automobile,Obesity_Type_I,Obesity_Type_I
+Female,18.0,1.644682,68.392133,yes,yes,2.0,1.131695,Sometimes,no,1.344539,no,0.0,1.59257,no,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Male,14.0,1.71,72.0,yes,yes,3.0,3.0,Sometimes,no,3.0,no,2.0,1.0,no,Walking,Normal_Weight,Normal_Weight
+Female,19.027574,1.659877,77.272652,yes,yes,2.061461,2.812283,Sometimes,no,3.0,no,0.362441,0.78519,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_I
+Male,23.329344,1.751365,95.290429,yes,yes,2.0,3.0,Sometimes,no,3.0,no,3.0,2.0,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,26.050109,1.773115,115.089316,yes,yes,2.392179,3.0,Sometimes,no,2.16467,no,1.079934,1.073026,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,22.352025,1.754711,105.000706,yes,yes,2.871137,3.0,Sometimes,no,2.627569,no,1.0,0.0,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,37.207082,1.762921,118.40174,yes,yes,2.13683,2.993084,Sometimes,no,1.885926,no,0.615298,0.0,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Female,19.673262,1.599486,44.810751,no,no,3.0,2.974568,Frequently,no,1.145761,yes,0.87967,0.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,20.552695,1.523426,42.0,no,yes,3.0,1.0,Frequently,no,1.185062,no,0.0,0.076654,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Male,21.0,1.65,53.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.0,0.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Female,19.47219,1.793824,160.935351,yes,yes,3.0,3.0,Sometimes,no,2.069257,no,1.986646,0.947091,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,18.0,1.7,90.0,no,yes,3.0,3.0,Sometimes,no,2.0,no,2.0,0.0,Sometimes,Public_Transportation,Obesity_Type_I,Normal_Weight
+Male,21.0,1.62,70.0,no,yes,2.0,1.0,no,no,3.0,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,18.0,1.45,53.0,no,yes,2.0,3.0,Frequently,no,2.0,yes,1.0,2.0,Frequently,Public_Transportation,Overweight_Level_I,Normal_Weight
+Male,21.0,1.610209,68.865008,no,yes,1.910176,2.938135,no,no,2.943097,no,0.997202,0.207922,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,18.222536,1.801746,141.166265,yes,yes,3.0,3.0,Sometimes,no,2.418488,no,1.99507,0.893514,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,34.772902,1.675612,73.501233,yes,yes,3.0,2.400943,Sometimes,no,1.509734,no,0.167125,0.0,Sometimes,Automobile,Overweight_Level_I,Overweight_Level_I
+Female,18.0,1.62,55.0,yes,yes,2.0,3.0,Frequently,no,1.0,no,1.0,1.0,no,Public_Transportation,Normal_Weight,Normal_Weight
+Male,40.366238,1.722396,109.349025,yes,yes,2.281963,3.770379,Sometimes,no,1.0,no,1.330519,0.0,no,Automobile,Obesity_Type_II,Obesity_Type_II
+Female,23.0,1.65,80.0,yes,yes,2.0,3.0,Always,no,2.0,no,0.0,2.0,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,21.0,1.7,65.0,yes,yes,2.0,3.0,Frequently,no,1.0,no,1.0,1.0,no,Public_Transportation,Normal_Weight,Normal_Weight
+Male,39.825592,1.706741,108.012603,yes,yes,2.487781,3.755976,Sometimes,no,1.0,no,1.860765,0.0,no,Automobile,Obesity_Type_II,Obesity_Type_II
+Male,32.895637,1.783901,103.771371,yes,yes,2.902469,1.734762,Sometimes,no,2.600238,no,2.113992,0.584755,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Male,33.55336,1.699793,103.841672,yes,yes,2.576449,2.419656,Sometimes,no,1.0,no,1.982379,0.0,no,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Female,26.0,1.584782,105.055597,yes,yes,3.0,3.0,Sometimes,no,1.197667,no,0.0,0.534769,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,22.087056,1.792435,89.998729,yes,yes,1.904732,2.608416,Sometimes,no,1.204175,no,0.0,1.895312,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,27.0,1.93,102.0,yes,yes,2.0,1.0,Sometimes,no,1.0,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,34.0,1.78,73.0,no,yes,2.0,3.0,Sometimes,no,2.0,no,3.0,1.0,Sometimes,Automobile,Normal_Weight,Normal_Weight
+Female,19.349258,1.52337,42.0,no,yes,2.913486,1.060796,Frequently,no,1.596212,no,0.108948,0.773087,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,37.955371,1.560648,80.0,yes,yes,2.846452,1.139317,Sometimes,no,1.459511,no,1.71807,0.0,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Female,20.901393,1.709585,75.0,yes,yes,2.104105,3.0,Sometimes,no,1.871033,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Male,22.0,1.6,66.0,no,yes,3.0,3.0,Sometimes,no,2.0,no,3.0,0.0,no,Bike,Overweight_Level_I,Normal_Weight
+Male,31.783845,1.75,120.0,yes,yes,2.941627,3.0,Sometimes,no,2.318134,no,0.582686,1.588046,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Female,21.997683,1.689441,51.107925,yes,yes,3.0,3.715118,Frequently,no,1.774576,no,0.10297,0.646423,no,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,20.388049,1.777971,137.785027,yes,yes,3.0,3.0,Sometimes,no,2.702789,no,1.606109,0.731213,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,21.0,1.68,70.0,yes,yes,3.0,3.0,Sometimes,no,3.0,no,0.0,2.0,Sometimes,Public_Transportation,Normal_Weight,Overweight_Level_I
+Female,34.044229,1.665807,77.098973,yes,yes,2.976975,1.346987,Sometimes,no,1.868349,no,0.702538,0.879333,no,Automobile,Overweight_Level_II,Overweight_Level_II
+Male,29.695603,1.842943,93.798055,yes,yes,2.133955,2.902766,Sometimes,no,1.753464,no,1.729987,1.0,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,18.0,1.74,86.0,no,no,3.0,3.0,Sometimes,no,2.0,no,3.0,0.0,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,26.358919,1.755317,82.228794,yes,yes,1.94313,3.559841,Sometimes,no,2.367996,no,0.23553,0.879977,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,16.913841,1.634832,85.803809,yes,yes,2.061969,3.0,Sometimes,no,1.229171,no,2.392047,1.256119,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,26.0,1.85,105.0,yes,yes,3.0,3.0,Frequently,no,3.0,no,2.0,2.0,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,33.151905,1.685127,83.986895,yes,yes,2.0,2.473911,Sometimes,no,2.452789,no,0.932792,0.0,no,Automobile,Overweight_Level_II,Overweight_Level_II
+Female,16.0,1.66,58.0,no,no,2.0,1.0,Sometimes,no,1.0,no,0.0,1.0,no,Walking,Normal_Weight,Normal_Weight
+Female,25.993154,1.609401,104.854928,yes,yes,3.0,3.0,Sometimes,no,2.613504,no,0.067985,0.778632,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,23.0,1.65,90.0,yes,yes,2.0,3.0,Sometimes,no,3.0,no,0.0,1.0,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,24.565628,1.62093,81.718232,yes,yes,2.260543,1.374791,Sometimes,no,1.947935,no,0.0,0.078607,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,18.0,1.78,64.4,yes,yes,3.0,3.0,Frequently,no,2.0,no,3.0,2.0,no,Walking,Normal_Weight,Normal_Weight
+Male,21.962426,1.696336,75.0,yes,yes,2.0,3.0,Sometimes,no,1.825629,no,0.699592,0.142056,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Male,25.846279,1.772731,115.828167,yes,yes,2.381164,3.0,Sometimes,no,2.170225,no,1.211048,1.89933,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Female,25.12791,1.668537,112.555456,yes,yes,3.0,3.0,Sometimes,no,2.868679,no,0.115369,0.028583,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,24.001889,1.614075,100.245302,yes,yes,2.880483,1.703299,Sometimes,no,1.006378,no,1.076729,1.058007,no,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Female,21.0,1.753578,77.97917,yes,yes,2.273548,2.39007,Sometimes,no,1.648404,no,0.874643,1.102696,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Male,32.787101,1.903832,99.812443,yes,yes,2.0,1.863012,Sometimes,no,2.196471,no,1.0,0.0,Sometimes,Automobile,Overweight_Level_II,Overweight_Level_II
+Female,20.744839,1.667852,49.803921,yes,yes,2.977018,3.193671,Frequently,no,2.482933,no,2.0,1.0,no,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Male,17.052914,1.710616,99.860254,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.067491,0.894105,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,22.846357,1.757442,133.365094,yes,yes,3.0,3.0,Sometimes,no,2.84837,no,1.206059,0.766668,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,31.398281,1.919543,101.544589,yes,yes,2.0,1.005391,Sometimes,no,2.001936,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,21.378039,1.718534,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,2.721646,1.269982,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,22.0,1.69,65.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.0,1.0,Sometimes,Public_Transportation,Normal_Weight,Overweight_Level_I
+Male,17.925497,1.829142,59.933015,yes,yes,2.86099,4.0,Sometimes,no,2.0,no,2.0,0.007872,no,Automobile,Insufficient_Weight,Insufficient_Weight
+Male,25.717522,1.673491,103.706181,yes,yes,2.668949,1.134321,Sometimes,no,1.279443,no,0.11243,1.135503,no,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,19.0,1.82,87.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.0,0.0,no,Public_Transportation,Overweight_Level_I,Obesity_Type_I
+Female,21.207423,1.707508,121.864326,yes,yes,3.0,3.0,Sometimes,no,1.615548,no,1.376534,0.926052,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,33.0,1.55,55.0,yes,yes,3.0,1.0,Sometimes,no,3.0,no,2.0,1.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Female,25.999942,1.627483,104.881994,yes,yes,3.0,3.0,Sometimes,no,2.569364,no,0.159255,0.419446,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,18.729566,1.855433,112.875283,yes,yes,2.0,2.427137,Sometimes,no,2.574073,no,1.0,2.0,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,22.0,1.69,65.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.0,1.0,Sometimes,Public_Transportation,Normal_Weight,Overweight_Level_I
+Female,29.08107,1.674568,71.602622,yes,yes,2.0,3.0,Sometimes,no,1.701835,no,1.682271,0.0,Sometimes,Automobile,Overweight_Level_I,Overweight_Level_I
+Male,21.0,1.65,60.0,no,no,3.0,1.0,Frequently,no,1.0,no,0.0,0.0,Sometimes,Motorbike,Normal_Weight,Normal_Weight
+Female,19.773303,1.602082,45.0,no,no,3.0,3.0,Frequently,no,2.875583,yes,1.258504,0.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,39.213399,1.586301,80.0,yes,yes,2.020502,1.237454,Sometimes,no,1.93142,no,1.967973,0.0,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Male,30.0,1.92,130.0,yes,no,2.0,3.0,Sometimes,yes,1.0,no,1.0,0.0,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Female,24.320154,1.577921,65.293179,yes,no,2.03414,2.741413,Sometimes,no,1.996384,no,1.276552,1.019452,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,25.0,1.61,61.0,no,no,2.0,3.0,Sometimes,no,1.0,no,2.0,1.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Female,26.0,1.623303,110.81746,yes,yes,3.0,3.0,Sometimes,no,2.673754,no,0.0,0.33929,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,27.0,1.64,78.0,yes,yes,2.0,1.0,Sometimes,no,2.0,no,0.0,0.0,Frequently,Automobile,Overweight_Level_II,Obesity_Type_I
+Male,30.350516,1.860292,123.721352,yes,yes,2.002784,3.0,Sometimes,no,2.292906,no,1.034031,0.0,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Female,18.13782,1.819728,151.278532,yes,yes,3.0,3.0,Sometimes,no,2.155926,no,1.999836,0.94603,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,37.597953,1.62901,80.0,yes,yes,2.450784,2.952821,Sometimes,no,1.335192,no,0.180276,0.0,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Female,25.909353,1.644078,102.277765,yes,yes,3.0,3.0,Sometimes,no,1.029241,no,0.075776,1.0,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,21.709159,1.658393,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,2.939733,1.692287,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,29.620095,1.787933,112.536367,yes,yes,2.008245,3.0,Sometimes,no,2.040137,no,0.0,0.474216,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Female,24.449655,1.635062,113.277388,yes,yes,3.0,3.0,Sometimes,no,2.578038,no,0.165422,0.463196,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,38.098745,1.598448,80.0,yes,yes,2.020785,1.169173,Sometimes,no,1.972551,no,1.903338,0.0,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Female,24.0,1.56,60.0,yes,yes,1.0,3.0,Always,no,2.0,no,0.0,2.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Female,19.045357,1.61291,82.193405,yes,yes,1.261288,2.930044,Sometimes,no,1.166655,no,0.133398,0.95174,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,21.635977,1.748106,133.259033,yes,yes,3.0,3.0,Sometimes,no,1.931163,no,1.562213,0.926565,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,24.079524,1.733439,97.911865,yes,yes,2.0,3.0,Sometimes,no,2.843675,no,1.309304,1.338655,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,18.118277,1.654757,80.0,yes,yes,2.821977,2.044035,Sometimes,no,1.954968,no,1.0,0.854536,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,25.974446,1.628855,108.090006,yes,yes,3.0,3.0,Sometimes,no,1.757105,no,0.085119,0.465444,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,18.0,1.62,68.0,no,no,2.0,1.0,Sometimes,no,1.0,no,0.0,2.0,no,Public_Transportation,Overweight_Level_I,Normal_Weight
+Female,22.480889,1.605662,82.470375,yes,yes,1.557287,1.0,Sometimes,no,2.371015,no,0.288032,2.0,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,18.0,1.783906,109.207614,yes,yes,2.0,1.867836,Sometimes,no,2.438398,no,1.0,0.802305,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,19.071027,1.756865,49.699672,no,yes,1.69427,3.266644,Sometimes,no,1.095417,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,26.0,1.631856,110.804337,yes,yes,3.0,3.0,Sometimes,no,2.70485,no,0.0,0.243338,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,23.0,1.712556,75.480764,yes,yes,3.0,3.654061,Sometimes,no,1.229915,no,0.793489,1.597608,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,31.0,1.58,68.0,yes,no,2.0,1.0,Sometimes,no,1.0,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,20.908785,1.700996,126.490236,yes,yes,3.0,3.0,Sometimes,no,1.242832,no,0.530925,0.575969,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,31.86893,1.75,118.102897,yes,yes,2.139196,3.0,Sometimes,no,2.04843,no,0.368026,0.360986,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Male,18.0,1.721854,52.514302,yes,yes,2.33998,3.0,Sometimes,no,2.0,no,0.027433,1.884138,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Male,29.0,1.69,90.0,yes,no,2.0,3.0,Sometimes,no,3.0,no,1.0,0.0,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Female,21.768153,1.76416,133.888629,yes,yes,3.0,3.0,Sometimes,no,2.32502,no,1.441791,0.918468,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,23.0,1.649736,84.134712,yes,yes,2.334474,1.496776,Sometimes,no,2.776724,no,0.782416,0.167728,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,25.921678,1.611452,102.363149,yes,yes,3.0,3.0,Sometimes,no,1.031701,no,0.03465,0.912345,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,25.461411,1.7,78.055968,yes,yes,2.661556,3.0,Sometimes,no,3.0,no,0.55181,0.658783,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,21.0,1.6,48.0,no,yes,2.0,3.0,Sometimes,no,1.0,no,1.0,0.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Male,18.0,1.76,68.0,no,no,2.0,4.0,Frequently,no,2.0,no,1.0,1.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Female,40.0,1.58,63.0,no,yes,2.0,3.0,Frequently,no,2.0,no,3.0,1.0,Sometimes,Public_Transportation,Overweight_Level_I,Normal_Weight
+Male,21.353237,1.709731,95.032177,yes,yes,2.0,2.973476,Sometimes,no,2.0,no,0.0,1.331527,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,34.821442,1.805459,90.13868,yes,yes,2.0,1.80993,Sometimes,no,2.174835,no,0.340915,0.020153,Sometimes,Automobile,Overweight_Level_II,Overweight_Level_II
+Male,31.641081,1.676595,89.993812,yes,yes,2.934671,2.119682,Sometimes,no,2.041462,no,0.578074,0.0,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Female,18.367481,1.745644,133.665587,yes,yes,3.0,3.0,Sometimes,no,2.900857,no,1.508897,0.625371,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,27.0,1.55,63.0,no,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,1.0,Sometimes,Automobile,Overweight_Level_I,Overweight_Level_I
+Female,16.0,1.66,58.0,no,no,2.0,1.0,Sometimes,no,1.0,no,0.0,1.0,no,Walking,Normal_Weight,Normal_Weight
+Female,17.0,1.8,50.0,no,yes,3.0,4.0,Frequently,no,1.0,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,18.0,1.62,55.0,yes,yes,2.0,3.0,Frequently,no,1.0,no,1.0,1.0,no,Public_Transportation,Normal_Weight,Normal_Weight
+Female,40.993179,1.567756,79.493626,yes,yes,2.0,3.0,Sometimes,no,2.952506,no,0.0,0.0,no,Automobile,Obesity_Type_I,Obesity_Type_I
+Male,16.496978,1.691206,50.0,no,yes,2.0,1.630846,Sometimes,no,2.975528,no,0.548991,0.369134,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,18.836315,1.751631,80.0,yes,yes,2.0,1.73762,Sometimes,no,2.207978,no,2.641072,0.707044,Frequently,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,27.899784,1.7,74.244004,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.722053,0.0,Sometimes,Automobile,Overweight_Level_I,Overweight_Level_I
+Female,19.126145,1.633794,77.858532,yes,no,2.696381,3.0,Sometimes,no,2.472964,no,1.856119,1.0,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,22.0,1.7,70.0,yes,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,1.0,Sometimes,Automobile,Normal_Weight,Normal_Weight
+Male,31.264628,1.803129,91.052215,yes,yes,2.0,3.0,Sometimes,no,1.199517,no,0.047738,0.163329,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,18.270434,1.737165,76.699774,yes,yes,2.838969,3.0,Sometimes,no,2.425927,no,1.0,1.453335,Frequently,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Male,29.0,1.62,53.0,no,yes,2.0,3.0,Sometimes,no,2.0,no,0.0,0.0,Sometimes,Automobile,Normal_Weight,Normal_Weight
+Male,17.06713,1.896734,59.895052,yes,yes,2.842102,3.34175,Sometimes,no,2.0,no,2.511157,0.560351,no,Automobile,Insufficient_Weight,Insufficient_Weight
+Female,21.0,1.66,59.0,no,yes,1.0,3.0,Always,no,2.0,yes,3.0,0.0,no,Automobile,Normal_Weight,Normal_Weight
+Female,28.977792,1.7,78.0,yes,yes,3.0,3.0,Sometimes,no,1.507638,no,1.82434,1.0,Frequently,Automobile,Overweight_Level_II,Overweight_Level_II
+Female,22.0,1.6,82.0,yes,yes,1.0,1.0,Sometimes,no,2.0,no,0.0,2.0,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,23.0,1.67,75.0,yes,yes,2.0,3.0,Frequently,yes,2.0,no,0.0,2.0,no,Walking,Overweight_Level_I,Normal_Weight
+Female,26.0,1.61739,105.013901,yes,yes,3.0,3.0,Sometimes,no,1.292479,no,0.0,0.541309,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,25.816445,1.684082,104.592372,yes,yes,3.0,3.0,Sometimes,no,1.295436,no,0.282063,0.929356,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,21.90012,1.843419,165.057269,yes,yes,3.0,3.0,Sometimes,no,2.406541,no,0.10032,0.479221,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,18.0,1.63,63.0,yes,yes,1.0,3.0,Sometimes,no,2.0,no,2.0,2.0,Sometimes,Public_Transportation,Normal_Weight,Overweight_Level_I
+Female,21.001969,1.736215,132.145549,yes,yes,3.0,3.0,Sometimes,no,1.657541,no,1.672639,0.629285,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,39.0,1.78,96.0,yes,no,2.0,3.0,Sometimes,no,3.0,no,1.0,0.0,Frequently,Automobile,Obesity_Type_I,Overweight_Level_II
+Male,20.0,1.67,78.0,yes,no,2.0,1.0,Sometimes,no,3.0,no,0.0,0.0,Frequently,Automobile,Overweight_Level_II,Overweight_Level_II
+Female,25.612462,1.674515,112.879662,yes,yes,3.0,3.0,Sometimes,no,2.989389,no,0.36009,0.169016,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,21.0,1.71,75.0,no,no,2.0,3.0,Frequently,no,3.0,yes,3.0,1.0,Frequently,Automobile,Overweight_Level_I,Normal_Weight
+Female,25.540865,1.678201,109.900472,yes,yes,3.0,3.0,Sometimes,no,1.471664,no,0.143955,0.444532,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,35.389491,1.78,100.84763,yes,yes,2.069267,1.476204,Sometimes,no,3.0,no,2.05252,0.092736,Frequently,Automobile,Obesity_Type_I,Obesity_Type_I
+Male,21.0,1.712061,75.0,yes,yes,2.0,3.0,Sometimes,no,1.333707,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Male,17.0,1.824414,59.295172,yes,yes,2.432302,3.0,Sometimes,no,2.0,no,2.349495,1.0,no,Automobile,Insufficient_Weight,Insufficient_Weight
+Female,21.140165,1.713133,133.735889,yes,yes,3.0,3.0,Sometimes,no,1.411365,no,1.564618,0.910683,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,17.0,1.56,51.0,no,yes,3.0,3.0,Sometimes,no,1.0,no,0.0,2.0,no,Public_Transportation,Normal_Weight,Normal_Weight
+Female,40.702771,1.548403,80.0,yes,yes,2.0,3.0,Sometimes,no,1.326165,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Male,21.180346,1.773766,89.28282,yes,yes,2.010684,1.322087,Sometimes,no,2.0,no,0.540397,0.973834,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,25.058566,1.764484,113.234349,yes,yes,1.517912,3.0,Sometimes,no,2.038958,no,1.590255,0.00164,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,22.814657,1.716289,75.688433,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.092344,1.466496,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Male,29.687488,1.909198,129.679131,yes,yes,2.08868,3.0,Sometimes,no,1.520215,no,1.857351,0.0,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,18.0,1.840138,60.0,yes,yes,3.0,4.0,Sometimes,no,2.0,no,2.0,0.0,no,Automobile,Insufficient_Weight,Insufficient_Weight
+Female,21.051982,1.729719,131.877558,yes,yes,3.0,3.0,Sometimes,no,1.422483,no,1.708971,0.673009,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,33.070142,1.709428,83.014033,yes,yes,2.0,1.044628,Sometimes,no,2.01051,no,2.805801,0.0,no,Automobile,Overweight_Level_II,Overweight_Level_II
+Female,25.653233,1.65757,109.931233,yes,yes,3.0,3.0,Sometimes,no,1.610472,no,0.029728,0.472343,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,22.679935,1.6084,82.603984,yes,yes,2.699282,1.0,Sometimes,no,2.598632,no,0.292093,2.0,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,22.0,1.64,53.0,no,no,2.0,3.0,Sometimes,no,2.0,no,3.0,0.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Male,22.936098,1.702825,93.638318,yes,yes,2.0,3.0,Sometimes,no,1.746197,no,0.455216,0.335158,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,23.0,1.728834,51.442293,yes,yes,2.0,3.0,Frequently,no,1.229915,no,0.619533,2.0,no,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,23.0,1.75,56.0,no,no,3.0,3.0,Frequently,no,2.0,no,2.0,0.0,Sometimes,Public_Transportation,Insufficient_Weight,Normal_Weight
+Male,19.623574,1.818226,85.833029,yes,yes,2.954996,3.714833,Sometimes,no,2.430918,no,2.545931,0.469735,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,38.378056,1.67805,77.224574,yes,yes,2.444599,3.0,Sometimes,no,1.029798,no,0.0,0.0,no,Automobile,Overweight_Level_II,Overweight_Level_II
+Male,25.027254,1.757154,112.200812,yes,yes,1.264234,3.0,Sometimes,no,2.0,no,1.333435,0.002168,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,29.791101,1.703317,102.592171,yes,yes,2.654792,1.077331,Sometimes,no,1.345407,no,0.327418,0.386861,no,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,23.245408,1.707968,75.383716,yes,yes,2.5621,3.105007,Sometimes,no,1.172427,no,0.184917,0.923082,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,23.0,1.68,67.0,yes,yes,2.0,3.0,Frequently,no,1.0,no,0.0,0.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Male,41.0,1.75,118.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.0,0.0,Sometimes,Bike,Obesity_Type_II,Obesity_Type_II
+Female,19.0,1.538398,42.0,no,yes,2.71897,1.473088,Frequently,no,1.164644,no,1.235675,0.930926,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,20.0,1.5,44.0,no,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,0.0,Sometimes,Automobile,Normal_Weight,Normal_Weight
+Male,22.851834,1.854592,63.611109,yes,yes,3.0,3.691226,Sometimes,no,2.650989,no,1.228136,0.8324,Sometimes,Automobile,Insufficient_Weight,Insufficient_Weight
+Female,17.000433,1.584951,44.411801,no,yes,2.737762,3.0,Sometimes,no,2.310921,no,2.240714,1.59257,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Male,26.957645,1.780731,112.957922,yes,yes,2.263245,3.0,Sometimes,no,2.092509,no,0.0,1.836853,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Male,40.564513,1.748015,109.758736,yes,yes,2.310751,3.220181,Sometimes,no,1.006643,no,1.15804,0.0,no,Automobile,Obesity_Type_II,Obesity_Type_II
+Female,26.0,1.632193,111.886611,yes,yes,3.0,3.0,Sometimes,no,2.617988,no,0.0,0.156187,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,28.493397,1.817572,120.8158,yes,yes,2.969233,3.0,Sometimes,no,2.807984,no,0.982134,1.191998,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,23.0,1.65,60.0,yes,no,2.0,3.0,Sometimes,no,2.0,no,0.0,0.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Male,27.394123,1.764138,112.323213,yes,yes,1.924632,3.0,Sometimes,no,2.006595,no,0.1768,0.511694,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Male,23.0,1.65,58.5,yes,no,2.0,3.0,Sometimes,no,2.0,no,0.0,0.0,no,Public_Transportation,Normal_Weight,Normal_Weight
+Male,21.0,1.67,67.0,yes,yes,3.0,1.0,Always,no,2.0,no,1.0,1.0,no,Public_Transportation,Normal_Weight,Normal_Weight
+Female,26.0,1.621167,104.947703,yes,yes,3.0,3.0,Sometimes,no,2.57721,no,0.0,0.402075,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,18.426619,1.777108,57.145917,yes,yes,2.0,3.092116,Sometimes,no,2.426465,no,2.0,1.839862,no,Automobile,Insufficient_Weight,Insufficient_Weight
+Female,21.853826,1.755643,137.796884,yes,yes,3.0,3.0,Sometimes,no,2.184707,no,1.978631,0.838957,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,22.884722,1.622787,82.0,yes,yes,1.754401,1.0,Sometimes,no,2.0,no,0.0,0.301909,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,29.0,1.74,72.0,yes,no,3.0,3.0,Always,no,2.0,no,0.0,0.0,Sometimes,Automobile,Normal_Weight,Normal_Weight
+Male,25.0,1.83,121.0,yes,no,3.0,3.0,Sometimes,no,3.0,no,2.0,0.0,Sometimes,Walking,Obesity_Type_II,Obesity_Type_II
+Male,17.282945,1.821514,59.605028,yes,yes,2.613249,3.712183,Sometimes,no,2.0,no,2.002997,0.174848,no,Automobile,Insufficient_Weight,Insufficient_Weight
+Male,27.0,1.7,78.0,no,yes,3.0,3.0,Sometimes,no,3.0,no,1.0,1.0,Frequently,Public_Transportation,Overweight_Level_II,Overweight_Level_I
+Female,23.0,1.628168,84.49798,yes,yes,2.058687,2.962004,Sometimes,no,2.010596,no,0.851059,0.630866,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,18.381382,1.722547,53.783977,yes,yes,2.0,3.131032,Sometimes,no,2.072194,no,1.487987,2.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,26.0,1.635905,111.571076,yes,yes,3.0,3.0,Sometimes,no,2.511399,no,0.0,0.258472,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,29.0,1.77,83.0,no,yes,1.0,4.0,Frequently,no,3.0,no,0.0,1.0,no,Motorbike,Overweight_Level_I,Overweight_Level_I
+Male,26.69858,1.816298,86.963765,no,yes,2.341133,1.578521,Sometimes,no,2.0,no,2.0,0.554072,no,Automobile,Overweight_Level_I,Overweight_Level_I
+Male,26.0,1.83,82.0,no,yes,2.0,3.0,Always,no,2.0,no,1.0,1.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Male,18.0,1.7,50.0,no,yes,1.0,3.0,Frequently,no,1.0,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,23.0,1.668649,80.458343,yes,yes,2.0,2.256119,Sometimes,no,1.142873,no,0.807076,1.611271,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,23.147644,1.815514,120.337664,yes,yes,2.996717,2.791366,Sometimes,no,2.626309,no,1.194898,0.034897,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Female,21.682367,1.732383,133.043941,yes,yes,3.0,3.0,Sometimes,no,1.610768,no,1.510398,0.931455,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,30.595632,1.910672,129.232708,yes,yes,2.497548,3.0,Sometimes,no,1.362583,no,1.144076,0.173232,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,21.948577,1.773594,105.000789,yes,yes,2.252653,3.0,Sometimes,no,2.962371,no,1.0,0.0,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,25.920738,1.823755,105.800158,yes,yes,2.92711,3.0,Sometimes,no,2.151496,no,1.166064,1.509831,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,22.867719,1.655413,50.424661,yes,yes,3.0,3.642802,Frequently,no,1.995177,no,0.118271,0.065515,no,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Male,19.0,1.8,60.0,yes,yes,3.0,1.0,Always,no,1.0,yes,0.0,0.0,no,Motorbike,Normal_Weight,Normal_Weight
+Male,20.0,1.8,114.0,yes,yes,2.0,3.0,Frequently,no,2.0,no,0.0,1.0,no,Public_Transportation,Obesity_Type_II,Obesity_Type_I
+Male,24.622054,1.756509,117.368716,yes,yes,1.451337,3.0,Sometimes,no,2.0,no,1.384607,0.631217,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,23.0,1.65,66.0,no,no,3.0,3.0,Sometimes,no,2.0,no,3.0,0.0,no,Public_Transportation,Normal_Weight,Normal_Weight
+Female,20.406871,1.755978,53.699561,yes,yes,2.0,3.891994,Frequently,no,1.86393,no,2.870127,2.0,no,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Male,23.0,1.706525,90.500055,yes,yes,2.0,3.0,Sometimes,no,1.530493,no,0.967627,1.0,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,17.767432,1.74379,50.0,no,yes,1.344854,4.0,Frequently,no,1.0,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,17.25813,1.600184,65.0,yes,yes,2.749629,2.298612,Sometimes,no,2.0,yes,0.547515,1.619473,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,21.012542,1.615145,68.653347,yes,yes,1.729824,3.612941,no,no,2.043703,no,0.562118,0.426643,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,21.0,1.63,66.0,yes,yes,3.0,1.0,Sometimes,yes,3.0,no,0.0,0.0,Sometimes,Public_Transportation,Normal_Weight,Overweight_Level_I
+Female,21.0,1.754497,77.956921,yes,yes,2.446872,2.372311,Sometimes,no,1.81031,no,0.094893,1.650778,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Male,20.677105,1.781251,102.950929,yes,yes,2.0,3.0,Sometimes,no,1.618189,no,0.571648,0.0,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,26.0,1.638836,110.970479,yes,yes,3.0,3.0,Sometimes,no,2.644135,no,0.0,0.357581,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Female,30.0,1.65,71.0,yes,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,24.361936,1.73945,133.346641,yes,yes,3.0,3.0,Sometimes,no,2.852339,no,1.139107,0.586035,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,34.389906,1.68108,83.568035,yes,yes,2.0,1.660768,Sometimes,no,2.482294,no,0.891205,0.0,no,Automobile,Overweight_Level_II,Overweight_Level_II
+Female,39.0,1.65,50.0,no,yes,3.0,4.0,Frequently,no,2.0,no,0.0,0.0,Sometimes,Public_Transportation,Insufficient_Weight,Normal_Weight
+Male,20.242237,1.75633,54.567343,yes,yes,2.0,3.98525,Sometimes,no,2.654078,no,2.113151,2.0,no,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,38.939448,1.738321,86.934846,no,yes,2.843709,3.058539,Sometimes,no,1.130079,no,2.834373,0.044954,Sometimes,Automobile,Overweight_Level_I,Overweight_Level_I
+Male,28.712995,1.665585,98.66176,yes,yes,2.95801,2.834253,Sometimes,no,1.0,no,1.999886,0.0,no,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,24.347414,1.789193,89.393589,yes,yes,1.521604,2.174968,Sometimes,no,2.0,no,0.0,0.632467,Sometimes,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,27.0,1.85,75.0,yes,yes,2.0,1.0,Sometimes,no,2.0,no,1.0,0.0,no,Walking,Normal_Weight,Normal_Weight
+Male,18.164768,1.694265,52.0,yes,yes,3.0,3.0,Frequently,no,2.0,no,0.819682,1.0,no,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Male,24.417552,1.774775,117.398976,yes,yes,2.23372,2.993634,Sometimes,no,2.028368,no,0.863158,0.449886,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Female,26.0,1.610225,104.936381,yes,yes,3.0,3.0,Sometimes,no,1.322004,no,0.0,0.539876,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,18.0,1.767058,51.132809,yes,yes,2.708965,3.0,Sometimes,no,1.873004,no,1.21718,1.0,Sometimes,Public_Transportation,Insufficient_Weight,Insufficient_Weight
+Female,21.322097,1.751118,149.291106,yes,yes,3.0,3.0,Sometimes,no,2.309409,no,1.68291,0.70558,Sometimes,Public_Transportation,Obesity_Type_III,Obesity_Type_III
+Male,18.0,1.782722,108.044313,yes,yes,2.0,2.655265,Sometimes,no,2.297896,no,1.0,0.552665,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,18.014333,1.751029,80.0,yes,yes,2.0,2.805436,Sometimes,no,2.122884,no,0.045651,0.017225,Frequently,Public_Transportation,Overweight_Level_I,Overweight_Level_I
+Female,20.0,1.57,60.0,no,yes,3.0,3.0,Sometimes,no,3.0,no,0.0,1.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Male,21.017383,1.752391,79.109637,yes,yes,3.0,1.0,Sometimes,no,2.0,no,2.57223,0.0,Sometimes,Automobile,Overweight_Level_I,Overweight_Level_I
+Male,21.856301,1.87199,115.627554,yes,yes,2.0,2.358455,Sometimes,no,2.515765,no,0.98232,1.157466,Sometimes,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Female,33.226808,1.557943,77.647716,yes,yes,2.02091,2.093831,Sometimes,no,1.506518,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I,Obesity_Type_I
+Male,21.333429,1.716545,80.005809,yes,yes,2.0,2.783336,Sometimes,no,2.0,no,2.536615,1.206535,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Female,38.0,1.64,59.8,yes,yes,2.0,3.0,Sometimes,no,2.0,no,2.0,0.0,no,Public_Transportation,Normal_Weight,Normal_Weight
+Male,23.0,1.7,69.0,no,yes,3.0,1.0,Frequently,no,1.0,no,2.0,1.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Male,23.0,1.89,65.0,yes,yes,3.0,3.0,Frequently,no,3.0,no,1.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight,Normal_Weight
+Male,24.443011,1.873368,121.82962,yes,yes,2.722161,2.658837,Sometimes,no,2.375707,no,1.299469,0.0,Sometimes,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Female,18.0,1.668555,85.607466,yes,yes,2.081238,3.0,Sometimes,no,1.18363,no,1.17177,0.0,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
+Male,21.0,1.8,72.0,no,yes,3.0,3.0,Sometimes,no,2.0,no,1.0,1.0,no,Public_Transportation,Normal_Weight,Normal_Weight
+Male,30.62865,1.766975,118.363376,yes,yes,2.964319,3.0,Sometimes,no,2.377257,no,0.614959,1.875023,Sometimes,Automobile,Obesity_Type_II,Obesity_Type_II
+Male,18.181821,1.662669,79.863546,yes,no,2.492758,2.270163,Sometimes,no,1.992586,no,1.452467,0.864583,no,Public_Transportation,Overweight_Level_II,Overweight_Level_II
+Male,30.870724,1.670774,101.626189,yes,yes,2.907744,3.990925,Sometimes,no,1.0,no,1.99975,0.0,no,Public_Transportation,Obesity_Type_II,Obesity_Type_II
+Male,20.0,1.56,45.0,no,no,2.0,3.0,Sometimes,no,2.0,no,1.0,1.0,Sometimes,Public_Transportation,Normal_Weight,Normal_Weight
+Female,21.001458,1.65,88.026943,yes,yes,2.482575,1.001542,Sometimes,no,3.0,no,1.751656,1.0,no,Public_Transportation,Obesity_Type_I,Obesity_Type_I
diff --git a/data_science/shashank_week9_xgboost/outputs/summary_metrics.csv b/data_science/shashank_week9_xgboost/outputs/summary_metrics.csv
new file mode 100644
index 0000000..c16b520
--- /dev/null
+++ b/data_science/shashank_week9_xgboost/outputs/summary_metrics.csv
@@ -0,0 +1,6 @@
+,0
+test_accuracy,0.8585
+cv_mean_accuracy,0.8665
+cv_std,0.0073
+num_features,25.0
+num_classes,7.0
diff --git a/data_science/shashank_week9_xgboost/reports/Geospatial_Tools_Research.pdf b/data_science/shashank_week9_xgboost/reports/Geospatial_Tools_Research.pdf
new file mode 100644
index 0000000..f5b6d4f
Binary files /dev/null and b/data_science/shashank_week9_xgboost/reports/Geospatial_Tools_Research.pdf differ
diff --git a/data_science/shashank_week9_xgboost/reports/Obesity_Model_Report.pdf b/data_science/shashank_week9_xgboost/reports/Obesity_Model_Report.pdf
new file mode 100644
index 0000000..d00e499
Binary files /dev/null and b/data_science/shashank_week9_xgboost/reports/Obesity_Model_Report.pdf differ
diff --git a/data_science/shashank_week9_xgboost/scripts/childhood_obesity_ml.py b/data_science/shashank_week9_xgboost/scripts/childhood_obesity_ml.py
new file mode 100644
index 0000000..8457e76
--- /dev/null
+++ b/data_science/shashank_week9_xgboost/scripts/childhood_obesity_ml.py
@@ -0,0 +1,42 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, classification_report
+
+# 1. Load the dataset
+df = pd.read_csv("childhood_obesity.csv")
+
+# 2. Check and clean data
+df = df.dropna()
+
+# 3. Define features (X) and target (y)
+# Change 'Obesity' to the actual column name for the label in your dataset
+X = df.drop("Obesity", axis=1)
+y = df["Obesity"]
+
+# 4. Encode categorical features
+X = pd.get_dummies(X, drop_first=True)
+
+# 5. Split into train and test
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+
+# 6. Train model
+model = RandomForestClassifier(n_estimators=200, random_state=42)
+model.fit(X_train, y_train)
+
+# 7. Predict
+y_pred = model.predict(X_test)
+
+# 8. Metrics
+acc = accuracy_score(y_test, y_pred)
+print(f"Accuracy: {acc*100:.2f}%")
+print("\nClassification Report:\n", classification_report(y_test, y_pred))
+
+# 9. Save predictions for sharing
+output = X_test.copy()
+output["Actual"] = y_test
+output["Predicted"] = y_pred
+output.to_csv("obesity_predictions.csv", index=False)
+print("Predictions saved to obesity_predictions.csv")
diff --git a/data_science/shashank_week9_xgboost/scripts/run_obesity_model.py b/data_science/shashank_week9_xgboost/scripts/run_obesity_model.py
new file mode 100644
index 0000000..43e3973
--- /dev/null
+++ b/data_science/shashank_week9_xgboost/scripts/run_obesity_model.py
@@ -0,0 +1,80 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score
+from sklearn.preprocessing import OneHotEncoder, LabelEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+from sklearn.ensemble import RandomForestClassifier
+
+# --- load ---
+df = pd.read_csv("obesity.csv")
+
+# --- target & features ---
+TARGET = "NObeyesdad"  # e.g., Normal_Weight, Overweight_Level_I, Obesity_Type_I, ...
+assert TARGET in df.columns, f"{TARGET} not found. Columns: {list(df.columns)}"
+
+# drop rows with missing values (dataset is usually clean)
+df = df.dropna().reset_index(drop=True)
+
+# split X/y
+y_text = df[TARGET].astype(str)
+X = df.drop(columns=[TARGET])
+
+# identify categorical vs numeric
+cat_cols = X.select_dtypes(include=["object"]).columns.tolist()
+num_cols = X.select_dtypes(exclude=["object"]).columns.tolist()
+
+# encode target labels (string -> int) for metrics stability
+y_le = LabelEncoder()
+y = y_le.fit_transform(y_text)
+
+# preprocessors
+pre = ColumnTransformer(
+    transformers=[
+        ("cat", OneHotEncoder(handle_unknown="ignore"), cat_cols),
+        ("num", "passthrough", num_cols),
+    ],
+    remainder="drop",
+)
+
+# model
+clf = RandomForestClassifier(
+    n_estimators=400,
+    max_depth=None,
+    random_state=42,
+    n_jobs=-1,
+)
+
+pipe = Pipeline(steps=[("prep", pre), ("model", clf)])
+
+# ---- train/valid split (stratified) ----
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42, stratify=y
+)
+
+# cross-val accuracy (more credible than single split)
+cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
+cv_scores = cross_val_score(pipe, X_train, y_train, cv=cv, scoring="accuracy", n_jobs=-1)
+print(f"CV Accuracy (mean ± std): {cv_scores.mean():.3f} ± {cv_scores.std():.3f}")
+
+# fit and evaluate on held-out test
+pipe.fit(X_train, y_train)
+y_pred = pipe.predict(X_test)
+acc = accuracy_score(y_test, y_pred)
+print(f"Test Accuracy: {acc:.3f}")
+print("\nClassification Report:\n", classification_report(y_test, y_pred, target_names=y_le.classes_))
+print("\nConfusion Matrix:\n", confusion_matrix(y_test, y_pred))
+
+# save predictions you can show
+out = X_test.copy()
+out["Actual"] = y_le.inverse_transform(y_test)
+out["Predicted"] = y_le.inverse_transform(y_pred)
+out.to_csv("obesity_predictions.csv", index=False)
+print("\nSaved predictions -> obesity_predictions.csv")
+
+# small tip on interpretation
+topline = (
+    f"CV Acc: {cv_scores.mean():.1%} (±{cv_scores.std():.1%}) | "
+    f"Test Acc: {acc:.1%} | n_test={len(y_test)}"
+)
+print("\nSUMMARY:", topline)