ML-Credit-Scoring-ThinFile-Consumers
🎓 Live Research Repository – PhD Thesis Implementation | Credit Scoring with ML & Fairness Evaluation By Dr. Deepa Shukla, 2025 This repository presents the datasets, models, and fairness-aware algorithms developed as part of my PhD thesis.
📢 Explore the live project site:
👉 ML-Credit-Scoring-ThinFile-Consumers GitHub Page
📘 Machine Learning for Credit Scoring of Thin-File Consumers
Final PhD Thesis Implementation – Chapter 5
🎓 Project Overview
This repository contains the complete implementation of Chapter 5 of my PhD thesis, titled:
“Machine Learning Algorithms for Credit Scoring of Thin-File Consumers: A Fairness-Aware Evaluation”
It addresses the challenge of evaluating creditworthiness for thin-file consumers—individuals with limited or no traditional credit history—using machine learning models, alternative data, and fairness-aware methodologies.
ML-Credit-Scoring-ThinFile-Consumers/ │ ├── notebooks/ # Jupyter notebooks: training, SHAP/LIME, fairness ├── results/ # Visualizations, model outputs, fairness plots ├── tables/ # Tables 5.1–5.6 in CSV format ├── thesis_demo_colab.ipynb# Google Colab-compatible demo ├── LICENSE # MIT License ├── README.md # You’re here ├── CITATION.cff # Citation metadata
📊 Dataset
A synthetic dataset was created and published as part of this thesis to support reproducibility and fairness research:
📘 Dataset Title: Synthetic Credit Score of Thin-File Consumers
📅 Published: May 2024
📁 Hosted on: Harvard Dataverse
🔗 Permanent DOI: https://doi.org/10.7910/DVN/6MLVVI
📂 Format: CSV with metadata following FAIR principles
🔍 Contents: Credit scoring features (traditional + alternative), synthetic labels, demographic proxies
📚 Citation
If you use this dataset in your research, please cite as:
```bibtex @dataset{shukla_2024_synthetic, author = {Deepa Shukla}, title = {Synthetic Credit Score of Thin-File Consumers}, year = 2024, doi = {10.7910/DVN/6MLVVI}, url = {https://doi.org/10.7910/DVN/6MLVVI} }
🧠 Models Implemented
| Model | Description |
|---|---|
| Logistic Regression | Linear baseline |
| Decision Tree | Interpretable tree-based learner |
| Random Forest | Ensemble method for robust performance |
| Gradient Boosting | Fine-grained boosting classifier |
| Support Vector Machine | Margin-based nonlinear classifier |
| Deep Neural Network (DNN) | Multi-layer perceptron-based classifier |
⚖️ Fairness-Aware Evaluation
This work integrates fairness into machine learning model evaluation through:
- Bias Mitigation Pipelines
- Pre/Post Fairness Score Comparison
- Fairness–Accuracy Tradeoff Visualizations
- SHAP & LIME Interpretability Tools
🗂️ Repository Structure
📈 Results Summary (Thesis Chapter 5)
| Table No. | Description |
|---|---|
| 5.1 | AUC-ROC Scores for Traditional Models |
| 5.2 | Performance on Alternative Data Features |
| 5.3 | Model Comparison (AUC, F1) |
| 5.4 | Fairness Metrics across Models |
| 5.5 | Bias Scores (Before vs After Mitigation) |
| 5.6 | Fairness-aware Model Performance (AUC, Bias %) |
All tables are reproducible via linked notebooks and available in /tables/.
📚 How to Cite
Please cite this repository and dataset using the following format:
```bibtex @dataset{shukla_2024_synthetic, author = {Deepa Shukla}, title = {Synthetic Credit Score of Thin-File Consumers}, year = 2024, doi = {10.7910/DVN/6MLVVI}, url = {https://doi.org/10.7910/DVN/6MLVVI} }
@misc{shukla_2025_repo, author = {Deepa Shukla}, title = {ML-Credit-Scoring-ThinFile-Consumers}, year = 2025, howpublished = {\url{https://github.com/Deezpa/ML-Credit-Scoring-ThinFile-Consumers}}, note = {Version 1.0 – Final Thesis Implementation} }