ajiboyefemifaith/loan-app

An end to end machine learning project to predict if an applicant would be given a house loan

Loan-app

An end to end machine learning project to predict the eligibility status of a home loan applicant.

Overview

About Company

Ajiboye and Sons Finance company deals in all home loans and have presence across all urban, semi urban and rural areas. Before a customer can apply for a house loan, the company needs to validate the customer's eligibility status for loan.

Problem

Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form.

These details include 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 that are eligible for loan amount so that they can specifically target these customers. Here they have provided a partial data set.

Dataset

The dataset was gotten from kaggle: https://www.kaggle.com/ajaymanwani/loan-approval-prediction/

Dataset Description:

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

Results

The logistic regression model was built using 10 features with the highest feature importance score and an acuracy of 83% was gotten.

Credit_History credit history meets guidelines

Property_Area Urban/ Semi Urban/ Rural

Loan_Status Loan approved (Y/N)