# -*- coding: utf-8 -*-
"""
Created on Mon Jul 15 11:39:16 2019

@author: Jagan Mohan
"""

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

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

# Missing Values
X1 = dataset.iloc[:,2:4]
X1.isnull().sum() #Since it is 0 we can proceed forward

# Since there are no categorical features we can proceed forward without Label Encoding & Onehotencoding

#Train Test Split Data
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y, train_size = 0.8, random_state = 1234)

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

#Logistic Regression
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 1234)
classifier.fit(X_train, y_train)

# Predicting the test Dataset
y_pred = classifier.predict(X_test)

#Generating 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('Classifier (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()





