Loan Approval Decision Tree Analysis

This project involved analyzing a loan approval dataset using a decision tree algorithm to predict the approval status of loan applicants based on their financial features such as income, credit score, and loan amount. This project was part of my MIS2502 coursework. My role was to clean and analyze data, build a decision tree model, and interpret the results to gain insights into the approval process. Through this project, I identified the optimal minimum split for the decision tree, achieving high training and validation accuracy rates. I also explored nodes with extreme probabilities to uncover patterns in approved and rejected applicants. Additionally, I applied the model to real-world scenarios by predicting outcomes for individual applicants. This experience enhanced my understanding of decision tree algorithms, feature importance, and the role of predictive analytics in financial decision-making. It also improved my technical skills in data processing and model evaluation, as well as my ability to communicate complex findings clearly and effectively. Overall, this project demonstrated my ability to apply analytical tools to solve real-world scenarios.