Loan prediction using logistic regression. analyticsvidhya.

Loan prediction using logistic regression. We performed an exploratory data analysis of the different factors and how they correlated with loan defaults. Many individuals apply for bank loans. Mar 31, 2021 · Using observations made in the EDA, we proceeded to use logistic regression to predict the odds of loan defaults with several loan characteristics as predictor variables. Jul 15, 2023 · In this article, we delve into the world of predictive analytics and explore how logistic regression, a powerful machine learning algorithm, can empower lenders to make informed decisions and Our goal is to not only understand the nuances of the dataset through thorough visualization and data exploration, but also to build a predictive model to help categorize loan statuses. A python application using Logistic Regression to predict the likelihood of loan defaults. Below is an end-to-end outline of the project, based on the code in the provided Jupyter notebook. analyticsvidhya. The credit gained by the customers can be a growing asset for a bank due to the earnings from interests or a liability if the customer is unable to pay the loan. A huge amount of capital that is disbursed may turn into bad debt just because the bank was not well This project aims to build a machine learning model to predict loan approvals based on applicant information, using Logistic Regression. Learn how to predict if a person will be able to pay the loan with logistic regression algorithm using sklearn library for machine learning. nxqis9 8kiavx xrv2 dyck sqgmh andof yumqj hpt ovbo jxmnz8