Loan Prediction using Regression is a classic binary classification problem in Machine Learning. The goal is to predict whether a Loan Applicant is likely to be approved or rejected based on certain features or attributes.
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 applicant’s 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 candidate’s profile is relevant or not using key features like Marital Status, Education, Applicant Income, Credit History, etc.
- A Loan Prediction Model based on Logistic Regression algorithm with an accuracy of 84.86486486487%
- test_data.csv and train_data.csv contains the raw dataset.
- The Loan Prediction Notebook contains the analysis part.