# -*- coding: utf-8 -*-
"""
Created on Wed Mar  6 21:51:31 2019

@author: Medeea
"""

#Simple Linear Regression

#Importing the libraries

#3 libraries

#mathematic tools

import numpy as np

#plot nice charts

import matplotlib.pyplot as plt

#import datasets

import pandas as pd


#importing the dataset

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

X = dataset.iloc[:, :-1].values

y = dataset.iloc[:, 1].values


#Splitting the dataset into the Training set and Test set


#from sklearn.cross_validation import train_test_split
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)

 
#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)"""

  
#Fitting Simple Linear Regression to the Training set

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)


#Predicting the Test set results

y_pred = regressor.predict(X_test)


#Visualising the Training set results

plt.scatter(X_train, y_train, color = 'red')
plt.plot(X_train, regressor.predict(X_train), color = 'blue')
plt.title('Salary vs Experience (Training set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()


#Visualising the Test set results

 
