#Random Forest classifcation model

#Dataset provided by SuperDataScience.com

#import libraries

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

#import dataset and declare x & y variables

dataset = pd.read_csv('Social_Network_Ads.csv')

x = dataset.iloc[:, [2,3]].values

y = dataset.iloc[:, 4].values

#split the dataset into the training and test sets

from sklearn.cross_validation import train_test_split

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.15, random_state = 0)

#feature scaling (not necessary for decision trees, but helps when visualizing the data)

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()

x_train = sc.fit_transform(x_train)

x_test = sc.transform(x_test)

#fitting classifier to training set

from sklearn.ensemble import RandomForestClassifier

classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)

classifier.fit(x_train, y_train)

#create a prediction of the test set

y_pred = classifier.predict(x_test)

#Create a confusion matrix to evaluate the model

from sklearn.metrics import confusion_matrix

cm = confusion_matrix(y_test, y_pred)

#graphing the sets to visualize the training data and model

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(('blue', 'yellow')))

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(('blue', 'yellow'))(i), label = j, edgecolors = "black")

plt.title('Random Forest (training set)')

plt.xlabel('Age')

plt.ylabel('Estimated Salary')

plt.show()

#graphing the sets to visualize the test data and model

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(('blue', 'yellow')))

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(('blue', 'yellow'))(i), label = j, edgecolors = "black")

plt.title('Random Forest (test set)')

plt.xlabel('Age')

plt.ylabel('Estimated Salary')

plt.show()

#after evaluating this method, Random Forest and Decision Trees are overfitting the dataset and it may be better to go with the Logisitic Regression or SVM classification