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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..1393c67 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
@@ -5,15 +6,34 @@
 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')
 
 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)
+nest=[]
+for i in range(1,51):
+    nest.append(i)
+ac1 = []
+ac2 = []
+n_est=None
+def bagging(X_train,X_test,y_train,y_test,n_est):
+    for i in nest:
+        dtc = DecisionTreeClassifier()
+        bc = BaggingClassifier(base_estimator=dtc,n_estimators=i,max_samples=0.67,max_features=0.67,random_state=9)
+        model = bc.fit(X_train,y_train)
+        y_pred = model.predict(X_train)
+        a = accuracy_score(y_train,y_pred)
+        ac1.append(a)
 
+        y_pred2 = model.predict(X_test)
+        b = accuracy_score(y_test,y_pred2)
+        ac2.append(b)
+    plt.plot(nest,ac1)
+    plt.plot(nest,ac2)
+        
+c = bagging(X_train,X_test,y_train,y_test,n_est)
 
-# Write your code here
 
 
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