💳 Credit Risk Prediction System

A Machine Learning web application built with Streamlit that predicts whether a customer is a Good Credit Risk or Bad Credit Risk using a trained Logistic Regression model.

🚀 Features

• Predict customer credit risk
• Logistic Regression model
• One-Hot Encoding preprocessing
• CSV Upload Support
• Download prediction results
• User-friendly Streamlit interface

📂 Project Structure

Credit_Risk_App/
│── app.py
│── credit_risk_model.pkl
│── feature_names.pkl
│── german_credit_data.csv
│── requirements.txt
│── README.md

📊 Machine Learning Models

• Logistic Regression
• Decision Tree
• Random Forest (Performance Comparison)

Final Model Used

Logistic Regression

Performance:

• Accuracy: 79%
• ROC-AUC: 0.83

📁 Dataset

German Credit Dataset

Features include:

• Account Status
• Credit History
• Credit Amount
• Savings
• Employment
• Age
• Housing
• Purpose
• Job
• Foreign Worker
• and more.

Target Variable:

• Good Credit
• Bad Credit

⚙️ Installation

Clone the repository

git clone <your-repository-link>

Install dependencies

pip install -r requirements.txt

Run the application

streamlit run app.py

🖥️ How to Use

1. Launch the Streamlit application.
2. Upload a CSV file containing customer information.
3. Click upload.
4. View predicted credit risk.
5. Download prediction results as CSV.

📈 Evaluation Metrics

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

🛠 Technologies Used

• Python
• Streamlit
• Pandas
• NumPy
• Scikit-learn
• Joblib

📄 License

This project is developed for educational purposes.