Abstract:

A loan is when one or more people, organizations, or other entities lend money to other people, organizations, or entities. The recipient (i.e., the borrower) incurs a debt for which he or she is generally responsible for paying interest until the loan is repaid. The project’s objective is to ensure that a person, institution, or organization applying for a loan is verified thoroughly before sanctioning them a loan.

Several criteria like gender, education, number of dependents, to name a few, have to be taken into consideration before approving the loan. The project aims at automating the procedure, thus, helping in reducing the time and energy and making the process more efficient.

Two sets of data – train data set and test data set – are given as the input. The train data set is used to train the Machine Learning Model and determine its accuracy. The test data set is used to output the loan eligibility predictions.

PROPOSED SYSTEM:

1) The proposed system automates the process of determining the applicant’s creditworthiness.

2) A data set containing the details of the loan applicants is collected. It is structured and analysed using suitable analysis techniques. The data set is classified into two categories:

• Train data is used for training the model, i.e., our model will learn from this file. It contains all the independent variables and the target variable.

• Test data contains all the independent variables but not the target variable. We apply the model to predict the target variable for the test data.
The Logistic Regression model is used to predict the binary outcome.

Block Diagram