Best Project for Real-Time Online Payments Fraud Detection Using Machine Learning

Online Payments Fraud Detection

📊 Project Details

💡 Project Summary

In today’s digital economy, online payment fraud continues to threaten individuals and organizations. This Online Payments Fraud Detection web application is a free, open-source, and professionally built solution developed to predict fraudulent online transactions in real-time using machine learning models.

The heart of this application is an XGBoost-based fraud detection model, which delivers an impressive AUC ROC score of 0.9556. The interface, built with Streamlit, allows users to input transaction details and get immediate insights into whether a transaction is likely to be fraudulent or genuine.

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🚀 Key Highlights

• ✅ Real-time Predictions: Instantly checks whether a transaction is fraudulent.
• ⚙️ Machine Learning Engine: Powered by XGBoost, fine-tuned for high accuracy.
• 📉 Dimensionality Reduction: Features reduced from 394 to 53 for better performance.
• 🧠 Advanced Feature Engineering: Efficient preprocessing and scaling.
• 💻 Streamlit Web App: Interactive, browser-based UI for easy use.
• 📈 Insightful Visualizations: Clearly presents model outputs and predictions.

🔎 Available Features

• Real-time fraud prediction form
• Input fields for:
  • Transaction ID
  • Transaction Amount
  • Device Type
  • Card Type (Visa, Mastercard, American Express, Discover)
  • Transaction Type (Debit/Credit)
• One-click prediction result (Fraudulent or Genuine)
• Transaction analysis visuals
• Model explanation and background documentation (inbuilt)
• Ready-to-run Streamlit app interface

🧰 Technologies Used

• Python 3.8+
• XGBoost – ML Model
• Streamlit – Frontend Web Interface
• Pandas & NumPy – Data Manipulation
• Scikit-learn – Preprocessing & Evaluation
• Matplotlib & Seaborn – Visualizations