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diff --git a/q01_bagging/build.py b/q01_bagging/build.py
index 19f8726..33efd71 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
@@ -15,5 +16,62 @@
 
 
 # Write your code here
+# def bagging(X_train,X_test,y_train,y_test,n_est):
+    
+#     dt = DecisionTreeClassifier()
+#     score_test = []
+#     score_train = []
+#     for x in range(1,50):
+    
+#         bag = BaggingClassifier(base_estimator = dt, n_estimators = x,random_state=9, bootstrap= True, max_features=0.67,max_samples=0.67)
+
+#         bag.fit(X_train,y_train)
+#         score_test.append(bag.score(X_test,y_test))
+#         score_train.append(bag.score(X_train,y_train))
+
+    
+#         plt.plot(score_test)
+#         plt.plot(score_train)
+import pandas as pd
+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
+
+# 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)
+
+
+# Write your code here
+
+def bagging(X_train, X_test, y_train, y_test, n_est):
+    acc_train, acc_test = [], []
+    ranges = range(1, n_est, 2)
+    for n_est in ranges:
+        clf_bagging_20 = BaggingClassifier(DecisionTreeClassifier(), n_estimators=n_est, max_samples=0.67,
+                                           max_features=0.67, bootstrap=True, random_state=9)
+        clf_bagging_20.fit(X_train, y_train)
+        X_pred_bagging = clf_bagging_20.predict(X_train)
+        y_pred_bagging = clf_bagging_20.predict(X_test)
+        acc_score_train = accuracy_score(y_train, X_pred_bagging)
+        acc_score_test = accuracy_score(y_test, y_pred_bagging)
+        acc_train.append(acc_score_train)
+        acc_test.append(acc_score_test)
+    plt.figure(figsize=(10, 6))
+    plt.plot(ranges, acc_train, c='b', label='Train set')
+    plt.plot(ranges, acc_test, c='g', label='Test set')
+    plt.legend(loc='upper right')
+    plt.xlabel('n_estimators')
+    plt.ylabel('Accuracy')
+    plt.show()
+
+
+
+
 
 
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