salary-years-simple-linear-regression

Simple linear regression model implementation in Python and R for to showing the relationship between salary and years of experience.

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Simple linear Regression in Python

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Importing libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

Importing 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.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)

Applying simple linear regression

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)

Visualizing 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()

Visualizing Test set results

plt.scatter(X_test, y_test, color = 'red')
plt.plot(X_train, regressor.predict(X_train), color = 'blue')
plt.title('Salary vs Experience {Test set}')
plt.xlabel('Years of experience')
plt.ylabel('Salary')
plt.show()

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Simple linear Regression in R

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Importing dataset

dataset = read.csv('Salary_Data.csv')

Splitting the dataset into the training set and test set

#install.packages('caTools')
library(caTools)
set.seed(123)
split = sample.split(dataset$Salary, SplitRatio = 2/3)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)

Applying simple linear regression

regressor = lm(formula = Salary ~ YearsExperience,
data = training_set)

Predicting the Test set results

y_pred = predict(regressor, newdata = test_set)

Visualising the Training set results

#install.packages('ggplot2')
library(ggplot2)

ggplot() +
geom_point(aes(x = training_set$YearsExperience, y = training_set$Salary),
colour = 'red') +
geom_line(aes(x = training_set$YearsExperience, y = predict(regressor, newdata = training_set)),
colour = 'blue') +
ggtitle('Salary vs Experience (Training set)') +
xlab('Years of experience') +
ylab('Salary')

Visualizing Test set results

ggplot() + geom_point(aes(x = test_set$YearsExperience, y = test_set$Salary),
colour = 'red') +
geom_line(aes(x = training_set$YearsExperience, y = predict(regressor, newdata = training_set)),
colour = 'blue') +
geom_smooth(method = 'lm') +
ggtitle('Salary vs Experience (Test set)') +
xlab('Years of experience') +
ylab('Salary')