L&T Loan Prediction

Financial institutions incur significant losses due to the default of vehicle loans. This has led to the tightening up of vehicle loan underwriting and increased vehicle loan rejection rates. The need for a better credit risk scoring model is also raised by these institutions. This warrants a study to estimate the determinants of vehicle loan default. A financial institution has hired you to accurately predict the probability of loanee/borrower defaulting on a vehicle loan in the first EMI (Equated Monthly Instalments) on the due date. Following Information regarding the loan and loanee are provided in the datasets:
Loanee Information (Demographic data like age, Identity proof etc.)
Loan Information (Disbursal details, loan to value ratio etc.)
Bureau data & history (Bureau score, number of active accounts, the status of other loans, credit history etc.)
Doing so will ensure that clients capable of repayment are not rejected and important determinants can be identified which can be further used for minimising the default rates.

Downloading the Dataset

Instructions for downloading the dataset

• Find dataset on this page: https://www.kaggle.com/mamtadhaker/lt-vehicle-loan-default-prediction
• The data is in CSV format, and have 41 columns and 233154 rows
• Download the dataset using the opendatasets Python library

Let's begin by downloading the data, and listing the files within the dataset.