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diff --git a/q01_pipeline/build.py b/q01_pipeline/build.py
index 96beca7..78c9832 100644
--- a/q01_pipeline/build.py
+++ b/q01_pipeline/build.py
@@ -1,3 +1,4 @@
+# %load q01_pipeline/build.py
 import pandas as pd
 import numpy as np
 from sklearn.model_selection import train_test_split, GridSearchCV
@@ -5,9 +6,58 @@
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.utils.class_weight import compute_class_weight
 from sklearn.metrics import roc_auc_score
-
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import f1_score, confusion_matrix
+from sklearn.metrics import precision_score, recall_score
+from sklearn.metrics import roc_auc_score
+from sklearn.linear_model import LogisticRegression
+from imblearn.under_sampling import RandomUnderSampler
+from imblearn.over_sampling import RandomOverSampler
+from imblearn.over_sampling import SMOTE
 bank = pd.read_csv('data/Bank_data_to_class.csv', sep=',')
+label_enc = LabelEncoder()
+for column in bank.select_dtypes(include=['object']).columns.values:
+    bank[column] = label_enc.fit_transform(bank[column])
+X_train, X_test, y_train, y_test = train_test_split(bank.iloc[:,:-1], 
+                                                    bank.iloc[:,-1], 
+                                                    random_state=9)
+
+rf = RandomForestClassifier(random_state=9)
+lr = LogisticRegression(random_state=9)
 
+model=[rf,lr]
 # Write your solution here :
+def pipeline(X_train, X_test, y_train, y_test,model):
+    dict1=dict()
+    dataset=[[X_train, X_test, y_train, y_test]]
 
+    # Create the Under samplers
+    rus = RandomUnderSampler(random_state=9)
+    X_sample2, y_sample2 =  rus.fit_sample(X_train, y_train)
+    dataset.append([X_sample2, X_test, y_sample2, y_test])
+    
+    
+    ros = RandomOverSampler(random_state=9)
+    X_sample3, y_sample3 = ros.fit_sample(X_train, y_train)
+    dataset.append([X_sample3, X_test, y_sample3, y_test])
+    
+    
+    smote = SMOTE(random_state=9, kind='borderline2')
+    X_sample4, y_sample4 = smote.fit_sample(X_train, y_train)
+    dataset.append([X_sample4, X_test, y_sample4, y_test])
+    
+    roc_old=0
+    roc_new=0
+    for m in model:
+        for X_train, X_test, y_train, y_test in dataset:
+            m.fit(X_train, y_train)
+            roc_new=roc_auc_score(y_test, m.predict(X_test))
+            if(roc_new>=roc_old):
+                dict1.clear()
+                dict1[m]=roc_new
+                roc_old=roc_new
+    return list(dict1.keys())[0],list(dict1.values())[0]
 
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