README.md

metadata

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
• Data Overview
• Baseline Models
• Feature Engineering
• Model Optimization
• API Deployment

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

Contact

• Author: Rana Irem Turhan
• GitHub: github.com/Rana-Irem-Turhan
• LinkedIn: https://www.linkedin.com/in/irem-turhan/