[R] Simple Linear Regression.ipynb

Kernel: R

library("IRdisplay")

display_png(file="img/01.png")  

---

• b0 is constant representing the base salary of anyone who come to profession and have no experience i.e. Experience = 0

• b1 is coefficient representing the slope. The more experience the more raise will be their in salary.

Here in the graph, the black line is Best Fitting Line

---

display_png(file="img/02.png")  

Actual value vs Model value and Ordinary Least Square

display_png(file="img/03.png")  

Data Preprocessing

# Importing the 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 = 0.75)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)

# Feature Scaling
# training_set = scale(training_set)
# test_set = scale(test_set)

training_set

test_set

Fitting Simple Linear Regression to the Training Set

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

summary(regressor)

Call:
lm(formula = Salary ~ YearsExperience, data = training_set)

Residuals:
    Min      1Q  Median      3Q     Max 
-7853.2 -3691.2   904.8  3191.0  8080.8 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)      27232.5     2474.3   11.01 6.17e-10 ***
YearsExperience   9103.7      392.9   23.17 6.38e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5471 on 20 degrees of freedom
Multiple R-squared:  0.9641,	Adjusted R-squared:  0.9623 
F-statistic:   537 on 1 and 20 DF,  p-value: 6.382e-16

---

The smaller the p-value the more significant is the Independent variable on the formula of dependent variable.

Watch this video for more information on p-value

---

Predicting the Test set result

y_pred = predict(regressor, newdata = test_set)

y_pred

Visualising the Training set results

• X = Years of Experience

• Y = Salary

# install.packages('ggplot2')
# package to plot graph
library(ggplot2)

## geom for geometrical
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 = 'green') +
    ggtitle('Salary vs Experience (Training Set)') +
    xlab('Years of Experience') +
    ylab('Salary')

MIME type unknown not supported

Visualising the 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 = 'green') +
    ggtitle('Salary vs Experience (Test Set)') +
    xlab('Years of Experience') +
    ylab('Salary')

MIME type unknown not supported