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diff --git a/q01_bagging/build.py b/q01_bagging/build.py
index c34fb02..4276dce 100644
--- a/q01_bagging/build.py
+++ b/q01_bagging/build.py
@@ -1,3 +1,4 @@
+# %load q01_bagging/build.py
 import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.tree import DecisionTreeClassifier
@@ -14,5 +15,28 @@
 
 
 # Write your code here
+def bagging(X_train, X_test, y_train, y_test,n_est = 10):
+    decision_clf = DecisionTreeClassifier()
+    
+    # Fitting single decision tree
+    decision_clf.fit(X_train, y_train)
+    y_pred_decision = decision_clf.predict(X_test)
+    score_dt1 = accuracy_score(y_test, y_pred_decision)
+    
+    
+    # Fitting bagging classifier with DecisionTreeClassifier
+    bagging_clf1 = BaggingClassifier(decision_clf, n_est, max_samples=0.67,max_features=0.67, 
+                                bootstrap=True, random_state=9)
 
+    bagging_clf1.fit(X_train, y_train)
+    y_pred_bagging = bagging_clf1.predict(X_test)
+    score_bc_dt = accuracy_score(y_test, y_pred_bagging)
+    
+    return plt.plot(n_est,score_bc_dt)
+    
+
+
+
+print(bagging(X_train, X_test, y_train, y_test,n_est = 10))
+plt.show()
 
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diff --git a/q02_stacking_clf/__pycache__/__init__.cpython-36.pyc b/q02_stacking_clf/__pycache__/__init__.cpython-36.pyc
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diff --git a/q02_stacking_clf/build.py b/q02_stacking_clf/build.py
index 7b1c5f8..5f2bbbd 100644
--- a/q02_stacking_clf/build.py
+++ b/q02_stacking_clf/build.py
@@ -15,4 +15,49 @@
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=9)
 
 # Write your code here
+clf1 = LogisticRegression(random_state=9)
+clf2 = DecisionTreeClassifier(random_state=9)
+clf3 = DecisionTreeClassifier(max_depth=9, random_state=9)
 
+bagging_clf1 = BaggingClassifier(clf2, n_estimators=100, max_samples=100,
+                                 bootstrap=True, random_state=9, oob_score=True)
+bagging_clf2 = BaggingClassifier(clf1, n_estimators=100, max_samples=100,
+                                 bootstrap=True, random_state=9, oob_score=True)
+bagging_clf3 = BaggingClassifier(clf3, n_estimators=100, max_samples=100,
+                                 bootstrap=True, random_state=9, oob_score=True)
+
+model = [bagging_clf1, bagging_clf2, bagging_clf3]
+
+#Actual function call
+dataframe = pd.read_csv('data/loan_prediction.csv')
+X = dataframe.iloc[:, :-1]
+y = dataframe.iloc[:, -1]
+
+X_train,X_test,y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=9)
+print (X_train.shape)
+print (X_test.shape)
+print (y_train.shape)
+print (y_test.shape)
+
+def stacking_clf(model, X_train,y_train, X_test, y_test):
+
+    x_train_mdl = pd.DataFrame()
+    for mdl in model:
+
+        mdl.fit(X_train, y_train)
+        x_train_mdl = pd.concat( [x_train_mdl, pd.DataFrame( mdl.predict_proba(X_train))]
+                                ,axis=1)
+
+    mdl_clf = LogisticRegression(random_state=9)
+    mdl_clf.fit(x_train_mdl,y_train)
+
+    x_test_mdl= pd.DataFrame()
+    for mdl in model:
+        x_test_mdl = pd.concat( [x_test_mdl, pd.DataFrame( mdl.predict_proba(X_test))]
+                                ,axis=1)
+
+    y_pred = mdl_clf.predict(x_test_mdl)
+
+    score = accuracy_score(y_test, y_pred)
+    return float(score)
+print(stacking_clf(model,X_train,y_train,X_test,y_test))
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