Loan Eligibility Prediction Using Machine Learning

Loans are a main source of income for banks. Loan has become an essential for everyone. Loan is given for the one who could re- pay it. To predict this scenario machine learning is used. This makes a way easier to know whether the loan should be granted or not. Predictions here are purely based on a person’s income and his eligibility to pay-back the amount. These models are trained relying on the past datasets. The datasets are available on various platforms such as kaggle. These algorithms have a high accuracy and the outcomes are trustable. In today’s world everybody looks up to how fast the work is going to be done. These are those kinds of emerging technologies which are really helpful to get the results sooner. It also reduces the risk of frauds and financial loses and improves decision- making. It focuses on purifying the data and selection of the attributes. This algorithm includes an additional advantage as it increases the accuracy the time complexity gets reduced. Here, data is partitioned into training and testing parts as model gets trained using train dataset and its performance is evaluated on test dataset. These procedures are complicated and time consuming to do manually. This approach of ML is much easier to convey.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-01
DOI
https://doi.org/10.5281/zenodo.15314734
Primary Topic
HIV/AIDS Research and Interventions
Type
article
Field-Weighted Citation Impact
0.00
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article

Loan Eligibility Prediction Using Machine Learning

Zenodo (CERN European Organization for Nuclear Research)
HIV/AIDS Research and Interventions
article

Loan Eligibility Prediction Using Machine Learning

article en

Abstract

Loans are a main source of income for banks. Loan has become an essential for everyone. Loan is given for the one who could re- pay it. To predict this scenario machine learning is used. This makes a way easier to know whether the loan should be granted or not. Predictions here are purely based on a person’s income and his eligibility to pay-back the amount. These models are trained relying on the past datasets. The datasets are available on various platforms such as kaggle. These algorithms have a high accuracy and the outcomes are trustable. In today’s world everybody looks up to how fast the work is going to be done. These are those kinds of emerging technologies which are really helpful to get the results sooner. It also reduces the risk of frauds and financial loses and improves decision- making. It focuses on purifying the data and selection of the attributes. This algorithm includes an additional advantage as it increases the accuracy the time complexity gets reduced. Here, data is partitioned into training and testing parts as model gets trained using train dataset and its performance is evaluated on test dataset. These procedures are complicated and time consuming to do manually. This approach of ML is much easier to convey.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 47%
HIV/AIDS Research and Interventions
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