simple linear regression
dataset=read.csv('Salary_Data.csv')
dataset
## YearsExperience Salary
## 1 1.1 39343
## 2 1.3 46205
## 3 1.5 37731
## 4 2.0 43525
## 5 2.2 39891
## 6 2.9 56642
## 7 3.0 60150
## 8 3.2 54445
## 9 3.2 64445
## 10 3.7 57189
## 11 3.9 63218
## 12 4.0 55794
## 13 4.0 56957
## 14 4.1 57081
## 15 4.5 61111
## 16 4.9 67938
## 17 5.1 66029
## 18 5.3 83088
## 19 5.9 81363
## 20 6.0 93940
## 21 6.8 91738
## 22 7.1 98273
## 23 7.9 101302
## 24 8.2 113812
## 25 8.7 109431
## 26 9.0 105582
## 27 9.5 116969
## 28 9.6 112635
## 29 10.3 122391
## 30 10.5 121872
Splitting the dataset
library(caTools)
## Warning: package 'caTools' was built under R version 3.4.2
set.seed(123)
split=sample.split(dataset$Salary, SplitRatio= 2/3)
training_set=subset(dataset, split==TRUE)
test_set=subset(dataset, split==FALSE)
linear regression
regressor= lm(formula=Salary ~YearsExperience, data=training_set)
summary(regressor)
##
## Call:
## lm(formula = Salary ~ YearsExperience, data = training_set)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7325.1 -3814.4 427.7 3559.7 8884.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25592 2646 9.672 1.49e-08 ***
## YearsExperience 9365 421 22.245 1.52e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5391 on 18 degrees of freedom
## Multiple R-squared: 0.9649, Adjusted R-squared: 0.963
## F-statistic: 494.8 on 1 and 18 DF, p-value: 1.524e-14
Predicting the test set resuslts
y_pred=predict(regressor, newdata=test_set)
y_pred
## 2 4 5 8 11 16 20
## 37766.77 44322.33 46195.35 55560.43 62115.99 71481.07 81782.66
## 21 24 26
## 89274.72 102385.84 109877.90
visiualization of the training set results
library(ggplot2)
ggplot()+
geom_point(aes(x=training_set$YearsExperience, y=training_set$Salary), colour='pink')+
geom_line(aes(x=training_set$YearsExperience, y=predict(regressor, newdata=training_set)), colour='green') +
ggtitle('Salary vs Experience(Training set)')+
xlab('Years of Experience')+
ylab('Salary')
visiualization of 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='blue') +
ggtitle('Salary vs Experience(Test set)')+
xlab('Years of Experience')+
ylab('Salary')
