# -*- coding: utf-8 -*-
"""
Created on Thu Mar 26 10:37:37 2020

@author: HP
"""
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd 


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

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




#splitting the dataset
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.fit_transform(X_test)"""

#ftiing model to slr
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train,y_train)

# Predicting the test result
y_pred = regressor.predict(X_test)

#visualising the training set result
plt.scatter(X_train,y_train, color = 'red')
plt.plot(X_train,regressor.predict(X_train), color='blue')
plt.title('Salary vs Exp train set')
plt.xlabel('Years of Exp')
plt.ylabel('Salary')
plt.show()

#visualising the test set result
plt.scatter(X_test,y_test, color = 'red')
plt.plot(X_train,regressor.predict(X_train), color='blue')
plt.title('Salary vs Exp test set')
plt.xlabel('Years of Exp')
plt.ylabel('Salary')
plt.show()


