Predicting Loan Eligibility for Dream Housing Finance

Automating Financial Decisions

Housing finance companies receive thousands of applications daily. Automating the loan eligibility process in real-time based on customer demographics, income, and credit history is a classic data science problem.

Classification Under Risk

Using historical loan datasets, I implemented a CatBoost Classifier, which naturally handles the categorical variables prevalent in financial applications without extensive preprocessing.

Efficiency Gains

The model accurately predicted eligibility, ranking highly among 75,000+ participants. Such models reduce manual underwriting time from days to seconds.