Bank Loan Status Prediction Project Overview

The Bank Loan Status Prediction project focuses on modernizing the loan approval process in the banking sector using advanced ensemble learning techniques. By analyzing borrower profiles and loan attributes such as credit history, income, employment status, and loan terms, the project aims to improve loan allocation accuracy. Utilizing a comprehensive dataset from Kaggle, various machine learning models including Random Forest, LightGBM, CatBoost, and XGBoost were employed. These models were combined through ensemble techniques to enhance prediction accuracy and reduce bias. The system also incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalances and improve model reliability. Additionally, an application was built using Gradio to provide users with an intuitive interface for predicting loan eligibility, thus optimizing the loan disbursement process and setting new benchmarks in risk management and portfolio performance.