A major proportion of retail bank profit comes from interests in the form of home loans. These loans are borrowed by regular income/high earning customers. Banks are most fearful of defaulters, as bad loans (NPA) usually eat up a major chunk of their profits. Therefore, it is important for banks to be judicious while approving loans for their customer base.
- Perform EDA and feature analysis to give recommendations to the bank on the key features to consider while approving a loan.
- Build a classification model to predict clients who are likely to default on their loan
- Maximizing
Recall(false negatives) as banks are more fearfull of defaulters given they result in greater loss. - Preferably maintaining a high
F1-Score(overall accuracy) as false positives would result in a loss of interest profit for the bank.
- Maximizing
The challenge of this project was to work with a sub-optimal dataset. Rather than seek out more and higher quality data (the conventional solution), the goal was to approach the problem as a real world example of working with limited data while still strive to meet the buisness objectives.
Performed substantial cleaning to address numerous missing values and outliers.
With the challenge of a limited dataset, was able to successfully build and optimize a Hyper Tuned Random Forest Model maximized for recall with a score of 80%.
Found that DEBTINC (Debt-to-income ratio) is a dominant predictor of defaulters, along with 4 other key features CLAGE, VALUE, NINQ and Loan.
From our model and EDA we identified 4 high impact buisness recommendations moving forward.