ssebowadisan / loan_prediction

Problem Statement

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.

How risky is the borrower?

Given the borrower’s risk, should we lend him/her? In the modern era, the data science teams in the banks build predictive models using machine learning to predict how likely a client is going to default the loan when they only have a handful of information. Loan Prediction is a very common real-life problem that each retail bank faces at least once in its lifetime. If done correctly, it can save a lot of man hours at the end of a retail bank.

Data

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)