/Credit-Risk-Modelling

The Repo contains data driven risk models which calculates the chances of a borrower defaults on loan or credit card calculated on basis of n factors

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0

Credit-Risk-Modelling

Context:

Credit risk is nothing but the default in payment of any loan by the borrower. In Banking sector this is an important factor to be considered before approving the loan of an applicant.Dream Housing Finance company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first apply for home loan after that company validates the customer eligibility for loan.

Objective:

Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers. Here they have provided a partial data set.

Dataset:

Variable Description

Loan_ID Unique Loan ID

Gender Male/ Female

Married Applicant married (Y/N)

Dependents Number of dependents

Education Applicant Education (Graduate/ Under Graduate)

Self_Employed Self employed (Y/N)

ApplicantIncome Applicant income

CoapplicantIncome Coapplicant income

LoanAmount Loan amount in thousands

Loan_Amount_Term Term of loan in months

Credit_History credit history meets guidelines

Property_Area Urban/ Semi Urban/ Rural

Loan_Status Loan approved (Y/N)