Project Information

What Affects Personal Loan Acceptance?

Understanding the dynamics of personal loan acceptance is crucial in today’s financial landscape, with over 20 million personal loans taken out every year in America. This project delves into factors influencing personal loan acceptance using Kaggle’s Bank Personal Loan Modelling dataset, which includes data from 5,000 customers. By examining variables such as age, income, education level, and mortgage status, we aimed to model the likelihood of loan acceptance through logistic regression. The analysis revealed that customers with higher income and education levels are more likely to accept personal loans. Visualizing these relationships using both 2D and 3D scatter plots provided a clearer understanding of the interactions between different variables. Notably, the analysis showed that customers with a higher income are 30% more likely to accept a personal loan compared to those with lower income.