📊 Credit Score Checking

🔗 Live Demo: https://creditscorechecking-11.onrender.com/

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📝 Project Overview

This project predicts creditworthiness using a Logistic Regression model trained on financial data. Users can input their financial information through a web interface and receive a credit score prediction along with high-risk customer identification.

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🛠️ Technologies Used

• Backend: Flask
• Machine Learning: scikit-learn, joblib
• Data Processing: pandas, numpy
• Deployment: Render

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🚀 Features

• User-friendly web interface for CSV input
• Real-time credit score prediction
• High-risk customer identification
• Model retraining capability

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📂 Project Structure

. ├── app.py # Flask application ├── credit_scoring.py # Model training script ├── data/ # Dataset ├── outputs/ # Model and predictions ├── requirements.txt # Project dependencies └── .gitignore # Git ignore rules

yaml Copy code

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📥 Installation

1. Clone the repository

git clone https://github.com/yourusername/CreditScoreChecking.git
cd CreditScoreChecking

Set up a virtual environment

python -m venv env

Activate on Linux/Mac

 source env/bin/activate

Activate on Windows

env\Scripts\activate

Install dependencies

pip install -r requirements.txt

Run the application

python app.py

🎯 Using the Web App

• Upload your CSV file with financial data.

• Click Submit to generate predictions.

• Download the predictions CSV containing:

• predicted_class → 0 = High Risk, 1 = Low Risk

• predicted_proba → Probability of being low-risk (if available)