From 6327c96a74070ffff18477f1ed26bfe14d52d0c2 Mon Sep 17 00:00:00 2001
From: Tusharshah2006 <tusharshah2006@gmail.com>
Date: Sun, 11 Nov 2018 14:01:07 +0000
Subject: [PATCH] Done

---
 __pycache__/__init__.cpython-36.pyc           | Bin 0 -> 156 bytes
 .../__pycache__/__init__.cpython-36.pyc       | Bin 0 -> 168 bytes
 q01_bagging/__pycache__/build.cpython-36.pyc  | Bin 0 -> 1308 bytes
 q01_bagging/build.py                          |  25 +++++++++++++++++-
 .../tests/__pycache__/__init__.cpython-36.pyc | Bin 0 -> 174 bytes
 .../test_q01_bagging.cpython-36.pyc           | Bin 0 -> 965 bytes
 6 files changed, 24 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 19f8726..8f0dcba 100644
--- a/q01_bagging/build.py
+++ b/q01_bagging/build.py
@@ -1,4 +1,6 @@
+# %load q01_bagging/build.py
 import pandas as pd
+import numpy as np
 from sklearn.model_selection import train_test_split
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.ensemble import BaggingClassifier
@@ -12,8 +14,29 @@
 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)
-
+estimator = np.arange(1, 51)
 
 # Write your code here
+def bagging(X_train, X_test, y_train, y_test, estimator):
+    train_accuracy = []
+    test_accuracy = []
+    
+    for est in estimator:
+        bc = BaggingClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=est, 
+                               max_samples=0.67, bootstrap=True, random_state=9,
+                               max_features=0.67)
+        model = bc.fit(X_train, y_train)
+
+        y_train_pred = model.predict(X_train)
+        train_accuracy.append(accuracy_score(y_train, y_train_pred))
+
+        y_test_pred = model.predict(X_test)
+        test_accuracy.append(accuracy_score(y_test, y_test_pred))
+    
+    plt.plot(estimator, train_accuracy)
+    plt.plot(estimator, test_accuracy)
+    plt.show()
+
+bagging(X_train, X_test, y_train, y_test, estimator)
 
 
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