#Naive Bayes Classification

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

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

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

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

from sklearn.model_selection import train_test_split as tts

xTrain,xTest,yTrain,yTest=tts(x,y,test_size=0.25,random_state=0)

from sklearn.preprocessing import StandardScaler as ss

scale=ss()

xTrain=scale.fit_transform(xTrain)

xTest=scale.transform(xTest)

from sklearn.naive_bayes import GaussianNB as nb

classifier=nb()

classifier.fit(xTrain,yTrain)

yPred=classifier.predict(xTest)

from sklearn.metrics import confusion_matrix as cm

cm=cm(yTest,yPred)

from matplotlib.colors import ListedColormap

X_set, y_set = xTrain, yTrain

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('Naive Bayes Classification (Training set)')

plt.xlabel('Age')

plt.ylabel('Estimated Salary')

plt.legend()

plt.show()

from matplotlib.colors import ListedColormap

X_set, y_set = xTest, yTest

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('Naive Bayes Classification (Test set)')

plt.xlabel('Age')

plt.ylabel('Estimated Salary')

plt.legend()

plt.show()