Credit Risk Classification for Banking Systems

Project Overview

Developed a credit risk classification model using a dataset with mixed categorical and numerical features, incorporating a cost matrix for the business-specific outcomes.

Key Steps

1. Data Exploration and Visualization

   • Explored data to understand distributions, correlations, and patterns.
   • Visualized data using plots and charts to identify trends and anomalies.

2. Data Preprocessing

   • Detected outliers and handled corrupted data.
   • Performed statistical analysis.
   • Applied minority class upsampling to address imbalance.

3. Dimensionality Reduction

   • Utilized Factor Analysis of Mixed Data (FAMD) to reduce feature complexity from 58 to 10, while maintaining model performance (F1-Score = 8.5).

4. Model Training and Optimization

   • Trained and optimized various algorithms:
     • XGBoost
     • RandomForest
     • KNN
     • Gaussian Naive Bayes
     • LightGBM
   • Used grid search for hyperparameter tuning.
   • Evaluated model generalization using k-fold cross-validation.
   • Monitored memory and time usage across classifiers.

Summary

This project successfully developed a robust and efficient credit risk classification model tailored for banking applications, ensuring high performance and alignment with business-specific outcomes.