Data Cleaning

In the previous task we have removed the outlier and now we will do rest of the cleaning.

Write a function data_cleaning that :

• Split the Dependent Variables and Independent Variable into train and test part. (Hint: X_train, y_train, remember!)
• Impute the missing value of Numerical and categorical variables in train and test part of the data.

Parameters:

Parameter	dtype	argument type	default value	description
data	pandas DataFrame	compulsory		Data at hand for cleaning

Returns:

Parameter	dtype	description
X	DataFrame	Dataframe containing feature variables
y	Series/DataFrame	Target Variable
X_train	Numpy arrays for training any format acceptable by sklearn	scaled X_train
X_test	Numpy arrays for testing any format acceptable by sklearn	scaled X_test
y_train	Numpy arrays for training any format acceptable by sklearn	y_train
y_test	Numpy arrays for testing any format acceptable by sklearn	y_test

Hint :

• Set random seed as 9 before and while splitting the data set. Use test_size=0.25 while splitting
• Numerical variable (LoanAmount) imputation can be performed with mean imputation.
• Categorical variables(Gender, Married, Dependents, Self_Employed, Loan_Amount_Term, Credit_History) null values should be imputed with mode imputation.

Let's get started !