Bank-Loan-Defaulter

This project aimed to develop a predictive model for identifying potential bank loan defaulters using customer data, incorporating features such as income, age, experience, marital status, house ownership, car ownership, current job years, and current house years. The data preprocessing involved handling missing values, encoding categorical variables, and scaling numerical features. After evaluating multiple models, a Random Forest classifier was identified as the optimal model due to its superior performance. The final solution was deployed using Streamlit, creating an interactive web application that allows users to input customer data and receive a prediction on whether the customer is likely to default on a loan.