# -*- coding: utf-8 -*-
"""
Created on Fri Jul 26 14:54:12 2019

@author: Rohit
"""

# -*- coding: utf-8 -*-
"""
Created on Fri Jul 26 13:07:05 2019

@author: Rohit
"""

# -*- coding: utf-8 -*-
"""
Spyder Editor

This is a temporary script file.
"""
import numpy as np
import pandas as pd
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.cross_validation import train_test_split
#random state is used so that same random items is repeated eveery time we 
#split the data into train and test set
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 SVM to the Training set
from sklearn.tree import DecisionTreeClassifier
classifier=DecisionTreeClassifier(criterion='entropy',random_state=0)
#classifier = SVC(kernel = 'rbf', 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)

# Plotting onto the graph
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())

from matplotlib.colors import ListedColormap
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 (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
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())

from matplotlib.colors import ListedColormap
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 (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()









