# Logistic Regression

# 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.model_selection 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 logistic regression to training test

from sklearn.linear_model import LogisticRegression
classifier=LogisticRegression(random_state=0)
classifier.fit(X_train,y_train)


#Predicitng test set results

y_pred=classifier.predict(X_test)


#Making the confussion matrix

from sklearn.metrics import confusion_matrix
cm=confusion_matrix(y_test,y_pred)

