# -*- coding: utf-8 -*- """ Created on Thu Sep 27 10:40:47 2018

@author: Administrator """ # de template

# Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd

# Importing the dataset dataset = pd.read_csv('Social_Network_Ads.csv') X = dataset.iloc[:, [2, 3]].values y = dataset.iloc[:, 4].values

# Splitting the dataset into the Training set and Test set from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

# Feature Scaling from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test)

# Fitting Decision Tree to the Training set from sklearn.tree import DecisionTreeClassifier classifier = DecisionTreeClassifier(criterion = 'entropy',random_state = 0) classifier.fit(X_train,y_train)

# Predicting the Test set results y_pred = classifier.predict(X_test)

# Making the Confusion Matrix from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_test, y_pred)

# Visualising the Training set results from matplotlib.colors import ListedColormap X_set, y_set = X_train, y_train X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),                      np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01)) plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),              alpha = 0.75, cmap = ListedColormap(('red', 'green'))) plt.xlim(X1.min(), X1.max()) plt.ylim(X2.min(), X2.max()) for i, j in enumerate(np.unique(y_set)):     plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],                 c = ListedColormap(('red', 'green'))(i), label = j) plt.title('Decision Tree (Training set)') plt.xlabel('Age') plt.ylabel('Estimated Salary') plt.legend() plt.show()

# Visualising the Test set results from matplotlib.colors import ListedColormap X_set, y_set = X_test, y_test X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),                      np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01)) plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),              alpha = 0.75, cmap = ListedColormap(('red', 'green'))) plt.xlim(X1.min(), X1.max()) plt.ylim(X2.min(), X2.max()) for i, j in enumerate(np.unique(y_set)):     plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],                 c = ListedColormap(('red', 'green'))(i), label = j) plt.title('Decision Tree (Test set)') plt.xlabel('Age') plt.ylabel('Estimated Salary') plt.legend() plt.show()

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