Credit Risk Prediction
Machine learning models for predicting loan default risk using structured financial data.
Credit Risk Prediction
Machine learning models for data-driven loan default prediction.
Project Snapshot
| Role | Machine Learning Engineer |
| Domain | Financial Risk Analysis |
| Project Type | Supervised Machine Learning |
| Status | Completed |
| Technologies | Python · Scikit-learn · XGBoost · Random Forest · Logistic Regression |
Executive Summary
This project explored the use of supervised machine learning techniques to predict loan default risk using structured financial data.
The primary objective was to compare multiple classification algorithms, improve predictive performance through feature engineering, and support data-driven credit risk assessment.
Business Problem
Credit risk assessment is a binary classification problem where financial and behavioral information is used to estimate the likelihood of loan default.
Building reliable predictive models can help support consistent and data-informed lending decisions.
Technical Solution
A complete machine learning workflow was implemented, including data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation.
Multiple classification algorithms were developed and compared to better understand the strengths and trade-offs of different approaches.
My Contributions
- Built an end-to-end machine learning pipeline.
- Performed data preprocessing and feature engineering.
- Developed Logistic Regression, Random Forest, and XGBoost models.
- Compared model performance using standard classification metrics.
- Improved predictive performance through feature engineering and model optimization.
Results
- Achieved approximately 12% improvement in predictive performance through feature engineering and model optimization.
- Compared multiple machine learning models for structured financial prediction.
- Produced interpretable outputs suitable for supporting credit risk analysis.
Technology Stack
- Python
- Scikit-learn
- Pandas
- NumPy
- XGBoost
- Random Forest
- Logistic Regression
Engineering Decisions
Instead of relying on a single machine learning algorithm, I compared multiple supervised learning models to better understand their predictive behavior on structured financial data.
Feature engineering played an important role in improving overall model performance while maintaining interpretability.
Lessons Learned
This project strengthened my understanding of supervised learning workflows, feature engineering, and model evaluation for structured datasets.
If I continue developing this project, I would package the model as an API, implement automated model monitoring, and explore explainability techniques such as SHAP to better support real-world decision making.