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title: FinRisk-AI
emoji: ⚡
colorFrom: indigo
colorTo: green
sdk: docker
sdk_version: 0.0.1
app_file: app.py
pinned: false
license: mit
---
# FinRisk-AI / Credit Score Classification
## 1. Problem Definition
The objective of this project is to build a machine learning model to classify customers' credit scores into three categories: **Good, Standard, and Poor**. This automated system aims to reduce manual underwriting time and improve risk assessment accuracy.
## 2. Project Scope
### 2.1.Documentation
- [Setup & Installation](docs/00_setup.md)
- [Data Overview](docs/01_data_overview.md)
- [Baseline Models](docs/02_baseline.md)
- [Feature Engineering](docs/03_feature_engineering.md)
- [Model Optimization](docs/04_model_optimization.md)
- [API Deployment](docs/api_deployment.md)
## 3. Deployment
**Try the Model Instantly:**
[Link to Live Demo (Simulated)] (e.g., HuggingFace Spaces URL)
To run locally:
1. Install dependencies: `pip install -r requirements.txt`
2. Run the app: `python src/app.py`
3. Open browser at `http://localhost:7860`
## 4. Key Findings & Results
* **Baseline Score**: 60% Accuracy (Logistic Regression).
* **Final Score**: **80% Accuracy** (XGBoost).
* **Top Predictors**: Outstanding Debt, Credit Mix, and Interest Rate.
* **Business Impact**: Potential to reduce default rates by 15% and cut processing time by 90%.
## 5. Repository Structure
```
FinRisk-AI/
│
├── README.md # Project Overview
├── requirements.txt # Dependencies
├── .gitignore
│
├── data/ # Raw and Processed Data
│ ├── raw/
│ │ ├── train.csv
│ │ └── test.csv
│ └── processed/
│ ├── train_processed.csv
│ └── test_processed.csv
│
├── docs/ # Detailed Documentation
│ ├── 00_setup.md
│ ├── 01_data_overview.md
│ ├── 02_baseline.md
│ ├── 03_feature_engineering.md
│ ├── 04_model_optimization.md
│ └── 05_evaluation_report.md
│
├── notebooks/ # Jupyter Notebooks (EDA -> Pipeline)
│ ├── Analysis/
│ │ └── 00_Data_Preparation_Training.ipynb
│ └── Modeling/
│ ├── 01_EDA.ipynb
│ ├── 02_baseline_model.ipynb
│ ├── 03_feature_engineering.ipynb
│ ├── 04_model_optimization.ipynb
│ └── 05_model_evaluation.ipynb
│
│
├── src/ # Source Code
│ ├── templates/ #UI
│ │ └── index.html
│ ├── models/ # Saved Artifacts
│ │ ├── final_model.pkl
│ │ └── features.json
│ └── tests/
│ ├── app.py # App
│ ├── config.py # Configuration
│ ├── inference.py # Prediction Logic
│ └── pipeline.py # Training Pipeline
│
└── OIG2.png
```
<img width="1862" height="853" alt="image" src="https://github.com/user-attachments/assets/0e259956-69d9-4c82-99d3-0ad0fbb619a3" />
## Contact
* **Author**: Rana Irem Turhan
* **GitHub**: github.com/Rana-Irem-Turhan
* **LinkedIn**: https://www.linkedin.com/in/irem-turhan/ |