Binary Credit Scoring: Exploratory Data Analysis & Modeling

📌 Project Overview

This project focuses on a binary credit scoring using Exploratory Data Analysis (EDA) and predictive modeling. The goal is to analyze customer credit data, extract meaningful insights, and build a robust model to predict creditworthiness.

📂 Project Structure

📁 credit_scoring_project
│── 📄 README.md          # Project documentation
│── 📂 data               # Raw and processed datasets
│── 📂 notebooks          # Jupyter Notebooks for EDA & modeling
│── 📂 __pycache__        # Cached Python files
│── 📂 .vscode            # VS Code configuration
│── 📄 .dockerignore      # Docker ignore file
│── 📄 .gitignore         # Git ignore file
│── 📄 Dockerfile         # Docker setup
│── 📄 app.py             # Application script
│── 📄 requirements.txt   # Dependencies

📊 Exploratory Data Analysis (EDA)

The EDA phase includes:

• Data Cleaning: Handling missing values, outlier detection, and feature engineering.
• Statistical Summary: Descriptive statistics and data distribution analysis.
• Feature Relationships: Correlations, visualizations, and trend analysis.
• Target Variable Insights: Understanding factors influencing credit risk.

🤖 Credit Scoring Model

Modeling Approach

• Feature Selection & Engineering
• Baseline Models: Logistic Regression, Decision Trees
• Advanced Models: Random Forest, Logistic Regression, Gradient Boosting
• Hyperparameter Tuning & Optimization
• Model Evaluation: AUC-ROC, Precision-Recall, Confusion Matrix

⚡ Installation & Usage

Prerequisites

Ensure you have Python 3.8+ and install dependencies:

pip install -r requirements.txt

Run EDA Notebook

jupyter notebook notebooks/eda.ipynb

Run Application

python app.py

📈 Results & Insights

• Key features affecting credit risk.
• Model performance comparison.
• Business implications of predictions.

🚀 (Possible ? 😆) Future Improvements

• Incorporating alternative data sources
• Explainability with SHAP/LIME

🤝 Contributions

Contributions are welcome! Feel free to submit a PR or open an issue.