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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
  1. install dependencies
pip install -r requirements.txt
  1. 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.