### Pre-processing using Caret
We need to pre-process our data before we can use it for modeling. Let’s check if the data has any missing values:
sum(is.na(train))
#[1] 86
Next, let us use Caret to impute these missing values using KNN algorithm. We will predict these missing values based on other attributes for that row. Also, we’ll scale and center the numerical data by using the convenient ``preprocess()`` in Caret.
#Imputing missing values using KNN.Also centering and scaling numerical columns
preProcValues <- preProcess(train, method = c("knnImpute","center","scale"))
library('RANN')
train_processed <- predict(preProcValues, train)
sum(is.na(train_processed))
#[1] 0It is also very easy to use one hot encoding in Caret to create dummy variables for each level of a categorical variable. But first, we’ll convert the dependent variable to numerical.
#Converting outcome variable to numeric
train_processed$Loan_Status<-ifelse(train_processed$Loan_Status=='N',0,1)
id<-train_processed$Loan_ID
train_processed$Loan_ID<-NULL
#Checking the structure of processed train file
str(train_processed)
#'data.frame': 614 obs. of 12 variables:
#$ Gender : Factor w/ 3 levels "","Female","Male": 3 3 3 3 3 3 3 3 3 3 ...
#$ Married : Factor w/ 3 levels "","No","Yes": 2 3 3 3 2 3 3 3 3 3 ...
#$ Dependents : Factor w/ 5 levels "","0","1","2",..: 2 3 2 2 2 4 2 5 4 3 ...
#$ Education : Factor w/ 2 levels "Graduate","Not Graduate": 1 1 1 2 1 1 2 1 1 #1 ...
#$ Self_Employed : Factor w/ 3 levels "","No","Yes": 2 2 3 2 2 3 2 2 2 2 ...
#$ ApplicantIncome : num 0.0729 -0.1343 -0.3934 -0.4617 0.0976 ...
#$ CoapplicantIncome: num -0.554 -0.0387 -0.554 0.2518 -0.554 ...
#$ LoanAmount : num 0.0162 -0.2151 -0.9395 -0.3086 -0.0632 ...
#$ Loan_Amount_Term : num 0.276 0.276 0.276 0.276 0.276 ...
#$ Credit_History : num 0.432 0.432 0.432 0.432 0.432 ...
#$ Property_Area : Factor w/ 3 levels "Rural","Semiurban",..: 3 1 3 3 3 3 3 2 3 2 #...
#$ Loan_Status : num 1 0 1 1 1 1 1 0 1 0 ...
Now, creating dummy variables using one hot encoding:
#Converting every categorical variable to numerical using dummy variables
dmy <- dummyVars(" ~ .", data = train_processed,fullRank = T)
train_transformed <- data.frame(predict(dmy, newdata = train_processed))
#Checking the structure of transformed train file
str(train_transformed)
#'data.frame': 614 obs. of 19 variables:
#$ Gender.Female : num 0 0 0 0 0 0 0 0 0 0 ...
#$ Gender.Male : num 1 1 1 1 1 1 1 1 1 1 ...
#$ Married.No : num 1 0 0 0 1 0 0 0 0 0 ...
#$ Married.Yes : num 0 1 1 1 0 1 1 1 1 1 ...
#$ Dependents.0 : num 1 0 1 1 1 0 1 0 0 0 ...
#$ Dependents.1 : num 0 1 0 0 0 0 0 0 0 1 ...
#$ Dependents.2 : num 0 0 0 0 0 1 0 0 1 0 ...
#$ Dependents.3. : num 0 0 0 0 0 0 0 1 0 0 ...
#$ Education.Not.Graduate : num 0 0 0 1 0 0 1 0 0 0 ...
#$ Self_Employed.No : num 1 1 0 1 1 0 1 1 1 1 ...
#$ Self_Employed.Yes : num 0 0 1 0 0 1 0 0 0 0 ...
#$ ApplicantIncome : num 0.0729 -0.1343 -0.3934 -0.4617 0.0976 ...
#$ CoapplicantIncome : num -0.554 -0.0387 -0.554 0.2518 -0.554 ...
#$ LoanAmount : num 0.0162 -0.2151 -0.9395 -0.3086 -0.0632 ...
#$ Loan_Amount_Term : num 0.276 0.276 0.276 0.276 0.276 ...
#$ Credit_History : num 0.432 0.432 0.432 0.432 0.432 ...
#$ Property_Area.Semiurban: num 0 0 0 0 0 0 0 1 0 1 ...
#$ Property_Area.Urban : num 1 0 1 1 1 1 1 0 1 0 ...
#$ Loan_Status : num 1 0 1 1 1 1 1 0 1 0 ...
#Converting the dependent variable back to categorical
train_transformed$Loan_Status<-as.factor(train_transformed$Loan_Status)
Here, “fullrank=T” will create only (n-1) columns for a categorical column with n different levels. This works well particularly for the representing categorical predictors like gender, married, etc. where we only have two levels: Male/Female, Yes/No, etc. because 0 can be used to represent one class while 1 represents the other class in same column.