(ML) Data preprocessing

Importing the dataset

dataset = read.csv("C:/RClass/Data.csv")

Taking care of missing data

dataset$Age = ifelse(is.na(dataset$Age), 
                     ave(dataset$Age, FUN= function(x) mean(x, na.rm= TRUE)),
                     dataset$Age)

dataset$Salary = ifelse(is.na(dataset$Salary), 
                        ave(dataset$Salary, FUN= function(x) mean(x, na.rm= TRUE)),
                        dataset$Salary)

Encoding categorical data

dataset$Country = factor(dataset$Country,
                         levels = c('France','Spain','Germany'),
                         labels = c(1, 2, 3))

dataset$Purchased = factor(dataset$Purchased,
                           levels = c('No','Yes'),
                           labels = c(0,1))

Splitting the dataset into the Training set and Test set

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

## Warning: package 'caTools' was built under R version 4.1.3

set.seed(2023)
split = sample.split(dataset$Purchased, SplitRatio = 0.8)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)

Feature scaling

dataset = read.csv("C:/RClass/Data.csv")
training_set[,2:3] =  scale(training_set[,2:3])
test_set[,2:3] = scale(test_set[,2:3])