The objective of this project is to predict the probability of borrower defaulting on a vehicle loan in the first EMI (Equated Monthly Installments) on the due date.
End-user: Financial institution
Objective: The objective of this project is to predict the probability of loanee or borrower defaulting on a vehicle loan in the first EMI (Equated Monthly Instalments) on the due date.
Dataset: The Vehicle Loan Default Prediction dataset includes following features:
Dependent feature: loan_default
Independent features: disbursed_amount, asset_cost, ltv, PERFORM_CNS.SCORE etc. The dataset contains 345550 rows and 41 features.
- Combining training and testing data.
- Checking missing values in data.
- Imputing Employment.Type missing values with 'Self employed' values.
- Checking the outliers using boxplot.
- Removing the outliers of 'disbursed_amount' & 'asset_cost' features.
- Calculating Age column using 'Date.of.Birth'.
- Calculating 'AVERAGE.ACCT.AGE' and 'CREDIT.HISTORY.LENGTH' in months.
- Creating bins of PERFORM_CNS.SCORE and LTV.
- Replacing Values of PERFORM_CNS.SCORE.DESCRIPTION.
- Generating New Features like 'ACTIVE.ACCTS','CURRENT.BALANCE' etc.
- Dropping irrelevant columns like 'DisbursalDate', 'Current_pincode_ID' ,'NO.OF_INQUIRIES' etc.
CatBoost Classifier: ROC AUC Score: 0.6442
Thank You!