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

#dataset import

dataset=pd.read_csv('Salary_Data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values

#Splitting
from sklearn.cross_validation 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)
sc_y = StandardScaler()
y_train = sc_y.fit_transform(y_train)"""


# Simple Linear Regression
from sklearn.linear_model import LinearRegression
regressor=LinearRegression()
regressor.fit(X_train,y_train)

y_pred=regressor.predict(X_test)

# Visualize X train
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()

# Visualize X test
plt.scatter(X_test,y_test, color='red')
plt.plot(X_train,regressor.predict(X_train),color='blue')
plt.title("salary vs Experience(test ")
plt.xlabel('years of experience')
plt.ylabel('Salary')
plt.show()



































