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+Sleep Quality Prediction
+
+AIM
+To predict sleep quality based on lifestyle and health factors.
+
+---
+
+DATASET LINK
+
+
+ Sleep Health and Lifestyle Dataset
+
+
+
+---
+
+DESCRIPTION
+
+What is the requirement of the project?
+
+ - This project aims to predict the quality of sleep using various health and lifestyle metrics. Predicting sleep quality helps individuals and healthcare professionals address potential sleep-related health issues early.
+
+
+Why is it necessary?
+
+ - Sleep quality significantly impacts physical and mental health. Early predictions can prevent chronic conditions linked to poor sleep, such as obesity, heart disease, and cognitive impairment.
+
+
+How is it beneficial and used?
+
+ - Individuals: Assess their sleep health and make lifestyle changes to improve sleep quality.
+ - Healthcare Professionals: Use the model as an auxiliary diagnostic tool to recommend personalized interventions.
+
+
+How did you start approaching this project? (Initial thoughts and planning)
+
+ - Researching sleep health factors and existing literature.
+ - Exploring and analyzing the dataset to understand feature distributions.
+ - Preprocessing data for effective feature representation.
+ - Iterating over machine learning models to find the optimal balance between accuracy and interpretability.
+
+
+Mention any additional resources used
+
+
+---
+
+LIBRARIES USED
+
+ - pandas
+ - numpy
+ - scikit-learn
+ - matplotlib
+ - seaborn
+ - joblib
+ - flask
+
+
+---
+
+EXPLANATION
+
+DETAILS OF THE DIFFERENT FEATURES
+
+
+
+
+ | Feature Name |
+ Description |
+ Type |
+ Values/Range |
+
+
+
+
+ | Gender |
+ Respondent's gender |
+ Categorical |
+ [Male, Female] |
+
+
+ | Age |
+ Respondent's age |
+ Numerical |
+ Measured in years |
+
+
+ | Sleep Duration (hours) |
+ Hours of sleep per day |
+ Numerical |
+ Measured in hours |
+
+
+ | Physical Activity Level |
+ Daily physical activity in minutes |
+ Numerical |
+ Measured in minutes |
+
+
+ | Stress Level |
+ Stress level on a scale |
+ Numerical |
+ 1 to 5 (low to high) |
+
+
+ | BMI Category |
+ Body Mass Index category |
+ Categorical |
+ [Underweight, Normal, Overweight, Obese] |
+
+
+ | Systolic Blood Pressure |
+ Systolic blood pressure |
+ Numerical |
+ Measured in mmHg |
+
+
+ | Diastolic Blood Pressure |
+ Diastolic blood pressure |
+ Numerical |
+ Measured in mmHg |
+
+
+ | Heart Rate (bpm) |
+ Resting heart rate |
+ Numerical |
+ Beats per minute |
+
+
+ | Daily Steps |
+ Average number of steps per day |
+ Numerical |
+ Measured in steps |
+
+
+ | Sleep Disorder |
+ Reported sleep disorder |
+ Categorical |
+ [Yes, No] |
+
+
+
+
+---
+
+WHAT I HAVE DONE
+
+Step 1: Exploratory Data Analysis
+
+ - Summary statistics
+ - Data visualization for numerical feature distributions
+ - Target splits for categorical features
+
+
+Step 2: Data Cleaning and Preprocessing
+
+ - Handling missing values
+ - Label encoding categorical features
+ - Standardizing numerical features
+
+
+Step 3: Feature Engineering and Selection
+
+ - Merging features based on domain knowledge
+ - Creating derived features such as "Activity-to-Sleep Ratio"
+
+
+Step 4: Modeling
+
+ - Model trained: Decision Tree
+ - Class imbalance handled using SMOTE
+ - Metric for optimization: F1-score
+
+
+Step 5: Result Analysis
+
+ - Visualized results using confusion matrices and classification reports
+ - Interpreted feature importance for tree-based models
+
+
+---
+
+MODELS USED AND THEIR ACCURACIES
+
+
+
+
+ | Model |
+ Accuracy (%) |
+ F1-Score (%) |
+ Precision (%) |
+ Recall (%) |
+
+
+
+
+ | Decision Tree |
+ 74.50 |
+ 75.20 |
+ 73.00 |
+ 77.50 |
+
+
+
+
+---
+
+
+CONCLUSION
+
+WHAT YOU HAVE LEARNED
+
+
+
Insights gained from the data
+
+ - Sleep Duration, Stress Level, and Physical Activity are the most indicative features for predicting sleep quality.
+
+
+
Improvements in understanding machine learning concepts
+
+ - Learned and implemented preprocessing techniques like encoding categorical variables and handling imbalanced datasets.
+ - Gained insights into deploying a machine learning model using Flask for real-world use cases.
+
+
+
Challenges faced and how they were overcome
+
+ - Managing imbalanced classes: Overcame this by using SMOTE for oversampling the minority class.
+ - Choosing a simple yet effective model: Selected Decision Tree for its interpretability and ease of deployment.
+
+
USE CASES OF THIS MODEL
+
+
+
Application 1
+
+ A health tracker app can integrate this model to assess and suggest improvements in sleep quality based on user inputs.
+
+
+
Application 2
+
+ Healthcare providers can use this tool to make preliminary assessments of patients' sleep health, enabling timely interventions.
+
+
FEATURES PLANNED BUT NOT IMPLEMENTED
+
+
+
Feature 1
+
+ Advanced models such as Random Forest, AdaBoost, and Gradient Boosting were not implemented due to the project's focus on simplicity and interpretability.
+
+
+
Feature 2
+
+ Integration with wearable device data for real-time predictions was not explored but remains a potential enhancement for future work.
+
+