Credit_Score_Prediction

📌 Overview

Credit Score Prediction is a machine learning project aimed at predicting an individual's credit score based on various financial and personal attributes. This tool can help financial institutions assess creditworthiness and make informed lending decisions.

🚀 Features

• Predict credit score categories (e.g., Poor, Fair, Good, Excellent)
• Uses machine learning models such as Logistic Regression, Random Forest, and XGBoost
• Data preprocessing and feature engineering
• Model evaluation and performance metrics
• Interactive web application for real-time predictions (optional)

📂 Project Structure

├── data/               # Dataset and preprocessing scripts
├── models/             # Trained models and evaluation scripts
├── notebooks/          # Jupyter notebooks for EDA and model training
├── src/                # Main source code for data processing & ML pipeline
│   ├── preprocess.py   # Data preprocessing scripts
│   ├── train.py        # Model training script
│   ├── predict.py      # Prediction script
├── app/                # Web application (if applicable)
├── requirements.txt    # Required dependencies
├── README.md           # Project documentation

📊 Dataset

The dataset contains various features such as:

• Income Level
• Credit History
• Debt-to-Income Ratio
• Loan Amount
• Number of Open Accounts

📥 Data Source

The dataset can be obtained from Kaggle or other open data sources.

🔧 Installation

1️⃣ Clone the Repository

git clone https://github.com/yourusername/credit-score-prediction.git
cd credit-score-prediction

2️⃣ Create a Virtual Environment (Optional but Recommended)

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3️⃣ Install Dependencies

pip install -r requirements.txt

🏃‍♂️ Usage

Train the Model

python src/train.py

Make Predictions

python src/predict.py --input sample_data.csv

Run Web App (Optional)

If a web interface is included, run:

streamlit run app/app.py

📈 Model Performance

The models are evaluated using:

• Accuracy
• Precision, Recall, and F1-Score
• ROC-AUC Score

🤝 Contributing

Contributions are welcome! Feel free to fork the repo, create a branch, and submit a pull request.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.