## Simple Linear Regression

Problem statement: Find a relation between the independent variable and dependent variable

Variables:

Independent Variables : years_of_service

Dependent Variable : salary

#Importing the libraries

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

#Change the working directory and set it as current console's working directory

#Importing the Dataset

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

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3)

#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 = 'green')

plt.plot(X_train, regressor.predict(X_train), color = "red")

plt.title('Experience vs Salary (train set)')

plt.xlabel('experience')

plt.ylabel('salary')

plt.show()

#Visualising the Test set results

plt.scatter(X_test, y_test, color = 'red')

plt.plot(X_train, regressor.predict(X_train), color = 'yellow')

plt.title('Experience vs Salary (test set)')

plt.xlabel('experience')

plt.ylabel('salary')

plt.show()