CredSecure Credit Card Fraud Detection Application using Random Forest

This project implements a fraud detection application using a Random Forest model, built with Flask and Python to predict fraudulent credit card transactions. The model outperforms others like XGBoost and Decision Trees in accuracy, recall, and ROC-AUC, making it ideal for handling imbalanced datasets in real-time fraud detection.

Model Comparison:

• Random Forest: Best overall performance with high accuracy, recall, and ROC-AUC, making it ideal for fraud detection in imbalanced datasets.
• XGBoost: Performs well in accuracy but suffers from overfitting and slight recall drop, leading to missed fraud cases.
• Decision Tree: Easy to interpret but prone to overfitting, with poor generalization.
• Logistic Regression: Simple and interpretable, but fails to capture complex patterns, resulting in lower accuracy and recall.

Key Features:

• Real-time fraud detection using a trained Random Forest model
• Simple, user-friendly interface built with Flask
• High accuracy, precision, and recall for accurate predictions
• Scalable and easy to deploy in production environments

Technologies Used:

• Python
• Flask
• Random Forest (for fraud detection)

Setup & Installation:

1. Clone the repository:

git clone https://github.com/KrishnenduMR/CredSecure.git
cd CredSecure

2. install dependencies

pip install -r requirements.txt

3. Run the app

python app.py

Now the app should be running locally on http://127.0.0.1:5000/.

License:

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