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diff --git a/q01_bagging/__pycache__/build.cpython-36.pyc b/q01_bagging/__pycache__/build.cpython-36.pyc
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diff --git a/q01_bagging/build.py b/q01_bagging/build.py
index 19f8726..ca231a2 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
@@ -5,6 +6,7 @@
 import matplotlib.pyplot as plt
 from sklearn.metrics import accuracy_score
 plt.switch_backend('agg')
+import numpy as np
 
 # Data Loading
 dataframe = pd.read_csv('data/loan_prediction.csv')
@@ -12,8 +14,36 @@
 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)
-
+n_est = 1
 
 # Write your code here
 
+def bagging(X_train, X_test, y_train, y_test,n_est):
+    
+    i=1
+    train = dict()
+    test = dict()
+    
+    while (i<=50):        
+
+        bagging_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=i, max_samples=0.67, 
+                                        bootstrap=True, random_state=9,max_features=0.67)
+        model = bagging_clf.fit(X_train, y_train)
+        
+        y_pred_train = model.predict(X_train)
+        score_train = accuracy_score(y_train, y_pred_train)
+        
+        y_pred_test = model.predict(X_test)
+        score_test = accuracy_score(y_test, y_pred_test)
+        
+        train[i]=score_train
+        test[i]=score_test
+        i= i + 1
+    
+    plt.plot(np.arange(1,51),train.values())
+    plt.plot(np.arange(1,51),test.values())
+    plt.show()
+
+# bagging(X_train, X_test, y_train, y_test,n_est)
+
 
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