diff --git a/__pycache__/__init__.cpython-36.pyc b/__pycache__/__init__.cpython-36.pyc
new file mode 100644
index 0000000..ff400dc
Binary files /dev/null and b/__pycache__/__init__.cpython-36.pyc differ
diff --git a/q01_bagging/__pycache__/__init__.cpython-36.pyc b/q01_bagging/__pycache__/__init__.cpython-36.pyc
new file mode 100644
index 0000000..a417501
Binary files /dev/null and b/q01_bagging/__pycache__/__init__.cpython-36.pyc differ
diff --git a/q01_bagging/__pycache__/build.cpython-36.pyc b/q01_bagging/__pycache__/build.cpython-36.pyc
new file mode 100644
index 0000000..07110e7
Binary files /dev/null and b/q01_bagging/__pycache__/build.cpython-36.pyc differ
diff --git a/q01_bagging/build.py b/q01_bagging/build.py
index 19f8726..d701a3d 100644
--- a/q01_bagging/build.py
+++ b/q01_bagging/build.py
@@ -1,11 +1,13 @@
+# %load q01_bagging/build.py
 import pandas as pd
+import numpy as np
 from sklearn.model_selection import train_test_split
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.ensemble import BaggingClassifier
 import matplotlib.pyplot as plt
 from sklearn.metrics import accuracy_score
-plt.switch_backend('agg')
 
+plt.switch_backend('agg')
 # Data Loading
 dataframe = pd.read_csv('data/loan_prediction.csv')
 
@@ -13,7 +15,30 @@
 y = dataframe.iloc[:, -1]
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=9)
 
-
-# Write your code here
+def bagging(X_train,X_test,y_train,y_test,n_est):
+    np.random.seed(9)
+    # Fitting bagging classifier with Logisitc Regression
+    bagging_clf2 = BaggingClassifier(DecisionTreeClassifier(), n_estimators=n_est, max_samples=67, 
+                                bootstrap=True, random_state=9)
+    bagging_clf2.fit(X_train, y_train)
+    y_pred = bagging_clf2.predict(X_train)
+    
+    y_pred_bagging = bagging_clf2.predict(X_test)
+    score_bc_dt = accuracy_score(y_test, y_pred_bagging)
+    score_bc = accuracy_score(y_train, y_pred)
+    
+    return  score_bc,score_bc_dt
+    
+xaxis = range (1,51)
+yaxis = []
+zaxis = []
+for i in range (1,51):
+    a,b = bagging(X_train,X_test,y_train,y_test,i)
+    yaxis.append(a)
+    zaxis.append(b)
+    
+plt.plot(xaxis,yaxis,c='blue')
+plt.plot(xaxis,zaxis , c ='red')
+plt.show()
 
 
diff --git a/q01_bagging/tests/__pycache__/__init__.cpython-36.pyc b/q01_bagging/tests/__pycache__/__init__.cpython-36.pyc
new file mode 100644
index 0000000..1f5e413
Binary files /dev/null and b/q01_bagging/tests/__pycache__/__init__.cpython-36.pyc differ
diff --git a/q01_bagging/tests/__pycache__/test_q01_bagging.cpython-36.pyc b/q01_bagging/tests/__pycache__/test_q01_bagging.cpython-36.pyc
new file mode 100644
index 0000000..486a2e3
Binary files /dev/null and b/q01_bagging/tests/__pycache__/test_q01_bagging.cpython-36.pyc differ
diff --git a/q02_stacking_clf/__pycache__/__init__.cpython-36.pyc b/q02_stacking_clf/__pycache__/__init__.cpython-36.pyc
new file mode 100644
index 0000000..0bcd88f
Binary files /dev/null and b/q02_stacking_clf/__pycache__/__init__.cpython-36.pyc differ
diff --git a/q02_stacking_clf/__pycache__/build.cpython-36.pyc b/q02_stacking_clf/__pycache__/build.cpython-36.pyc
new file mode 100644
index 0000000..851a1d9
Binary files /dev/null and b/q02_stacking_clf/__pycache__/build.cpython-36.pyc differ
diff --git a/q02_stacking_clf/build.py b/q02_stacking_clf/build.py
index 7b1c5f8..c3d6267 100644
--- a/q02_stacking_clf/build.py
+++ b/q02_stacking_clf/build.py
@@ -1,9 +1,11 @@
+# %load q02_stacking_clf/build.py
 # Default imports
 from sklearn.model_selection import train_test_split
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.linear_model import LogisticRegression
 from sklearn.ensemble import BaggingClassifier
 from sklearn.metrics import accuracy_score
+from mlxtend.classifier import StackingClassifier
 import pandas as pd
 import numpy as np
 
@@ -14,5 +16,13 @@
 
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=9)
 
-# Write your code here
+def stacking_clf(model,X_train,y_train, X_test, y_test):
+    for k in model:
+        k.fit(X_train, y_train)  
+        y_pred = k.predict(X_test)
+        accuracy = accuracy_score(y_test, y_pred)
+    return  accuracy
+
+
+
 
diff --git a/q02_stacking_clf/tests/__pycache__/__init__.cpython-36.pyc b/q02_stacking_clf/tests/__pycache__/__init__.cpython-36.pyc
new file mode 100644
index 0000000..b0bc437
Binary files /dev/null and b/q02_stacking_clf/tests/__pycache__/__init__.cpython-36.pyc differ
diff --git a/q02_stacking_clf/tests/__pycache__/test_q02_stacking_clf.cpython-36.pyc b/q02_stacking_clf/tests/__pycache__/test_q02_stacking_clf.cpython-36.pyc
new file mode 100644
index 0000000..188c1c6
Binary files /dev/null and b/q02_stacking_clf/tests/__pycache__/test_q02_stacking_clf.cpython-36.pyc differ