Vehicle Default Loan Prediction

Financial institues are suffering from significant loses due to car loan defaults. This has resulted in tighter car loans and higher car loan denial rates. Institutions have addressed the need for better credit risk scoring model. Therefore, in our project we seek to predict vehicle loan defaults on customers to determine whether a subject of given criteria is receptive to defaulting their loan or not.

Research Hypothesis:

In our project, we try to hypothesize if we can accurately predict the possibility that the loanee/borrower will default on the car loan.

Data Description

Our data is fetched from Kaggle website that contains information about loanee (Demographics such as age, income, etc.), loan itself (amount, loan to value ratio, Equated Monthly Installments, etc.) and information from the bureau (Bureau score, number of active accounts, status of other loans, credit history, etc.). There is no disclosure about the origin of the data (i.e country) but from our column investigation, it turns out to be from India. Based on that assumption which is not fully verified, we will treat it as undisclosed.

There are approximately 230k+ rows and 41 columns in the dataset. Data description goes as follows:

Variable Name	Description
UniqueID	Identifier for customers
loan_default	"Payment default in the first EMI on due date, 1 for default"
disbursed_amount	Amount of Loan disbursed
asset_cost	Cost of the Asset
ltv	Loan to Value of the asset
branch_id	Branch where the loan was disbursed
supplier_id	Vehicle Dealer where the loan was disbursed
manufacturer_id	"Vehicle manufacturer(Hero, Honda, TVS etc.)"
Current_pincode	Current pincode of the customer
Date.of.Birth	Date of birth of the customer
Employment.Type	Employment Type of the customer (Salaried/Self Employed)
DisbursalDate	Date of disbursement
State_ID	State of disbursement
Employee_code_ID	Employee of the organization who logged the disbursement
MobileNo_Avl_Flag	if Mobile no. was shared by the customer then flagged as 1
Aadhar_flag	if aadhar was shared by the customer then flagged as 1
PAN_flag	if pan was shared by the customer then flagged as 1
VoterID_flag	if voter was shared by the customer then flagged as 1
Driving_flag	if DL was shared by the customer then flagged as 1
Passport_flag	if passport was shared by the customer then flagged as 1
PERFORM_CNS.SCORE	Bureau Score
PERFORM_CNS.SCORE.DESCRIPTION	Bureau score description
PRI.NO.OF.ACCTS	count of total loans taken by the customer at the time of disbursement
PRI.ACTIVE.ACCTS	count of active loans taken by the customer at the time of disbursement
PRI.OVERDUE.ACCTS	count of default accounts at the time of disbursement
PRI.CURRENT.BALANCE	total Principal outstanding amount of the active loans at the time of disbursement
PRI.SANCTIONED.AMOUNT	total amount that was sanctioned for all the loans at the time of disbursement
PRI.DISBURSED.AMOUNT	total amount that was disbursed for all the loans at the time of disbursement
SEC.NO.OF.ACCTS	count of total loans taken by the customer at the time of disbursement
SEC.ACTIVE.ACCTS	count of active loans taken by the customer at the time of disbursement
SEC.OVERDUE.ACCTS	count of default accounts at the time of disbursement
SEC.CURRENT.BALANCE	total Principal outstanding amount of the active loans at the time of disbursement
SEC.SANCTIONED.AMOUNT	total amount that was sanctioned for all the loans at the time of disbursement
SEC.DISBURSED.AMOUNT	total amount that was disbursed for all the loans at the time of disbursement
PRIMARY.INSTAL.AMT	EMI Amount of the primary loan
SEC.INSTAL.AMT	EMI Amount of the secondary loan
NEW.ACCTS.IN.LAST.SIX.MONTHS	New loans taken by the customer in last 6 months before the disbursment
DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS	Loans defaulted in the last 6 months
AVERAGE.ACCT.AGE	Average loan tenure
CREDIT.HISTORY.LENGTH	Time since first loan
NO.OF_INQUIRIES	Enquries done by the customer for loans

Tools:

The prediction is going to be delivered on an IPython Notebook using Cookiecutter structure. Tools to be used are:

Python 3.7
Pandas
Numpy
Scikit-learn
Matplotlib
Seaborn
Tableau
Flask - Deploying model through Heroku

abduliante / vehicle-default-loan-prediction Goto Github PK