The main goal of the project is to develop a predictive model that determines the likelihood of a loan applicant being eligible for approval based on financial and personal data. In the real world, banks often face challenges when evaluating loan applications due to varying risk profiles and incomplete information. This project addresses that by leveraging ensemble learning models, which combine the predictive power of multiple algorithms to improve reliability and reduce bias. Key features such as income, credit score, debt-to-income ratio, and inflation-adjusted parameters will be considered to make accurate predictions. By the end of the project, students will produce a functioning system that can assess loan eligibility efficiently, supporting both financial knowledge and technical expertise in machine learning.
This project is organized over a twelve-week development cycle. In the first few weeks, students will establish their development environment using tools such as Python, Anaconda, or Google Colab and gain hands-on experience with ML libraries like Scikit-learn and XGBoost. The project then proceeds to dataset preparation, including data cleaning, feature selection, and exploratory data analysis.
In the middle stages, students will construct, train, and evaluate the ensemble learning model. Techniques like cross-validation and hyperparameter tuning will be used to optimize model performance. They will also test the model using new applicant data and focus on reducing misclassifications. The later weeks will involve system integration, refining the results, creating documentation, and presenting the completed project. While real-world banking rules vary across institutions, this model will serve as a generalized framework for loan prediction based on commonly accepted criteria.
