From f0a558618880fe95845474a474a09344423c4819 Mon Sep 17 00:00:00 2001
From: TheSarang <narkhedesarang@gmail.com>
Date: Mon, 9 Jul 2018 05:23:58 +0000
Subject: [PATCH] Done

---
 __pycache__/__init__.cpython-36.pyc           | Bin 0 -> 151 bytes
 .../__pycache__/__init__.cpython-36.pyc       | Bin 0 -> 163 bytes
 q01_bagging/__pycache__/build.cpython-36.pyc  | Bin 0 -> 1346 bytes
 q01_bagging/build.py                          |  21 +++++++++++++++++-
 .../tests/__pycache__/__init__.cpython-36.pyc | Bin 0 -> 169 bytes
 .../test_q01_bagging.cpython-36.pyc           | Bin 0 -> 960 bytes
 6 files changed, 20 insertions(+), 1 deletion(-)
 create mode 100644 __pycache__/__init__.cpython-36.pyc
 create mode 100644 q01_bagging/__pycache__/__init__.cpython-36.pyc
 create mode 100644 q01_bagging/__pycache__/build.cpython-36.pyc
 create mode 100644 q01_bagging/tests/__pycache__/__init__.cpython-36.pyc
 create mode 100644 q01_bagging/tests/__pycache__/test_q01_bagging.cpython-36.pyc

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diff --git a/q01_bagging/build.py b/q01_bagging/build.py
index c34fb02..8967715 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
@@ -12,7 +13,25 @@
 y = dataframe.iloc[:, -1]
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=9)
 
+def bagging(X_train, X_test, y_train, y_test, n_est):
+    acc_score_train = []
+    acc_score_test = []
+    for i in range(1,n_est+1):
+        bagging_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=i, max_samples=0.67,max_features = 0.67,
+                                bootstrap=True, random_state=9)
+        bagging_clf.fit(X_train, y_train)
 
-# Write your code here
+        y_test_pred = bagging_clf.predict(X_test)
+        y_train_pred = bagging_clf.predict(X_train)
+
+        acc_score_train.append(accuracy_score(y_train, y_train_pred))
+        acc_score_test.append(accuracy_score(y_test, y_test_pred))
+        
+    plt.plot(range(1,n_est+1),acc_score_train, label = 'Train Set')
+    plt.plot(range(1,n_est+1),acc_score_test, label = 'Test Set')
+    plt.legend()
+    plt.xlabel('No. of estimators')
+    plt.ylabel('Accuracy')
+    plt.show()
 
 
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