CreditGuard

Full-stack credit card fraud detection dashboard using React + Flask + Machine Learning

Tech Stack

Frontend:

• React (with React Router)
• Custom CSS (gradient blur theme)
• Chart.js for visualization
• Flash messaging system

Backend:

• Flask + Flask-CORS
• Pandas (CSV parsing)
• Scikit-learn (ML pipeline)
• Git LFS (for model storage)

How to Run the Project Locally

Step 1: Clone the Repository

git clone https://github.com/your-username/CreditGuard.git
cd CreditGuard

Step 2: Install Dependencies

Backend (Flask API)

cd backend
pip install -r requirements.txt

Then run the Flask server:

python app.py

Frontend (React App)

Open a new terminal window:

cd frontend
npm install
npm start


To clone the repo and pull the model:
```bash
git lfs clone https://github.com/your-username/CreditGuard.git

What It Does

• Upload CSV files to predict fraud using a pre-trained model
• View top merchants, summary stats, and a fraud chart
• See a preview of your uploaded dataset
• Dashboard displays training accuracy, precision, and recall

Status

• Upload and prediction functional
• Model metrics displayed
• Styling complete
• Login and analytics planned for future (Coming Soon)