data_preprosessing_tools_in_R.R

#importing dataset
dataset=read.csv('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('Yes','No'),
                       labels = c('1','0'))

#spliting data into training set and test set
#library(caTools)
set.seed(123)
split=sample.split(dataset$Purchased,SplitRatio = 0.8)
training_set=subset(dataset,split==TRUE)
test_set=subset(dataset,split==FALSE)

#feature scaling
training_set[,2:3]=scale(training_set[,2:3])
test_set[,2:3]=scale(test_set[,2:3])