CreditScorerX

To run the files

$ python3 main.ipynb

Credit Scoring models—Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Logistic Classification—have been implemented from scratch to assist in data-driven lending decisions.

The implementations of the models can be found in the models folder. The classifiers are imported to the main.ipynb file for modularity and easier maintenance.

Results

Accuracy

1. Support Vector Machine - 83.33%
2. Decision Tree - 82.50%
3. Linear Classification - 82.33%
4. K-Nearest Neighbors - 81.50%