Loan Approval Prediction using Machine Learning
You can download the used data by visiting this link.
The dataset contains 13 features :
| 1 | Loan | A unique id |
|---|---|---|
| 2 | Gender | Gender of the applicant Male/female |
| 3 | Married | Marital Status of the applicant, values will be Yes/ No |
| 4 | Dependents | It tells whether the applicant has any dependents or not. |
| 5 | Education | It will tell us whether the applicant is Graduated or not. |
| 6 | Self_Employed | This defines that the applicant is self-employed i.e. Yes/ No |
| 7 | ApplicantIncome | Applicant income |
| 8 | CoapplicantIncome | Co-applicant income |
| 9 | LoanAmount | Loan amount (in thousands) |
| 10 | Loan_Amount_Term | Terms of loan (in months) |
| 11 | Credit_History | Credit history of individualās repayment of their debts |
| 12 | Property_Area | Area of property i.e. Rural/Urban/Semi-urban |
| 13 | Loan_Status | Status of Loan Approved or not i.e. Y- Yes, N-No |
Loan Approval Prediction using Machine Learning
LOANS are the major requirement of the modern world. By this only, Banks get a major part of the total profit. It is beneficial for students to manage their education and living expenses, and for people to buy any kind of luxury like houses, cars, etc.
But when it comes to deciding whether the applicantās profile is relevant to be granted with loan or not. Banks have to look after many aspects.
So, here we will be using Machine Learning with Python to ease their work and predict whether the candidateās profile is relevant or not using key features like Marital Status, Education, Applicant Income, Credit History, etc.
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