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q01_bagging/build.py

@@ -1,19 +1,49 @@
+# %load q01_bagging/build.py
 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
 plt.switch_backend('agg')
+import numpy as np
 
 # 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)
-
+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)
+