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diff --git a/q01_bagging/__pycache__/__init__.cpython-36.pyc b/q01_bagging/__pycache__/__init__.cpython-36.pyc
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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..b096227 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
@@ -5,7 +6,6 @@
 import matplotlib.pyplot as plt
 from sklearn.metrics import accuracy_score
 plt.switch_backend('agg')
-
 # Data Loading
 dataframe = pd.read_csv('data/loan_prediction.csv')
 
@@ -15,5 +15,26 @@
 
 
 # Write your code here
+def bagging(X_train,X_test,y_train,y_test,n_est):
+    dt_classifier = DecisionTreeClassifier(random_state=9)
+
+    error_train = []
+    error_test = []
+    for i in range(1,51):
+        bg_classifier = BaggingClassifier(n_estimators=i,base_estimator=dt_classifier,bootstrap=True,max_features=0.67,max_samples=0.67)
+
+        bg_classifier.fit(X_train,y_train)
+
+        predict_train = bg_classifier.predict(X_train)
+        predict_test = bg_classifier.predict(X_test)
+
+        error_train.append(accuracy_score(y_train,predict_train))
+        error_test.append(accuracy_score(y_test,predict_test))
+
+    plt.plot(range(1,51),error_train,label='Train')
+
+    plt.plot(range(1,51),error_test,label='Test')
+    plt.legend()
+    plt.show()
 
 
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