📊 Transaction Anomaly Detector

🌟 About the Project

Transaction Anomaly Detector is a lightweight and beginner-friendly browser-based tool that detects suspicious patterns in financial transactions using core Data Structures & Algorithms (DSA) — with no machine learning involved.

This tool is transparent, explainable, and ideal for those learning DSA or contributing to open-source.

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✨ Features

• ✅ DSA-Powered Detection: Uses algorithms like sliding window, graphs, hash maps, and tries.
• 📈 Real-Time Results: Instant analysis of uploaded transaction data.
• ⚡ No Backend Required: Fully functional in the browser — no server or database needed.
• 🔍 Explainable Logic: Transparent detection with no black-box ML models.

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💻 Technologies Used

• Frontend: HTML, CSS, JavaScript (Vanilla)
• Algorithms Implemented:
  • Sliding Window
  • Graph Cycle Detection
  • Trie for Merchant Flagging
  • Hash Map Analysis

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🧠 How It Works

Transactions are analyzed in real-time using the following DSA techniques:

🧮 Algorithm	🔍 Detection Capability
Sliding Window	Rapid, high-frequency usage in a short time
Graph Cycle Detection	Circular or suspicious fund transfers
Trie Search	Suspicious merchant name patterns
Hash Maps	User behavior outliers and frequency spikes

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📸 Interface Preview

Here's a quick preview of how the tool looks in action:

• Left Panel: Input area for CSV transactions, threshold settings.
• Right Panel: Real-time detection results and summaries.

🤝 Contributing

We welcome contributions from everyone! Whether you're a beginner or an experienced developer, you can help improve the tool or suggest new features. This project is a great way to learn and apply fundamental DSA concepts in a practical setting.

This is a perfect project for GSSoC’25, Hacktoberfest, and other open-source programs.

🧩 How to Contribute

To get started, follow these steps to set up your local development environment and make your first contribution.

1. Fork this repository: Click the "Fork" button in the top-right corner of the repository page. This creates a copy of the project on your GitHub account.

2. Clone your fork to your local machine:

   git clone https://github.com/VaishnaviMelagiri/Transaction-Anomaly-Detector.git

3. Navigate into the project directory:

   cd Transaction-Anomaly-Detector

4. Create a new branch for your changes:

   git checkout -b your-feature-branch

   This ensures your changes are isolated from the main branch. Use a descriptive name like add-sliding-window-optimization or fix-ui-alignment.

5. Make your changes and commit them:

   git add .
   git commit -m "Added: [Your concise and clear description of the changes]"

   Write a clear commit message explaining what you did.

6. Push your new branch to your forked repository:

   git push origin your-feature-branch

7. Create a Pull Request: Go to your fork on GitHub and click the "Compare & pull request" button. Provide a meaningful title and detailed description of your changes, referencing any relevant issues.

🧭 Unsure Where to Start?

If you're new to the project or open-source, here are some great places to begin:

• Look for good first issue labels: These are tasks specifically curated for new contributors.
• Documentation Improvements: Add comments to the code for better clarity.
• UI/UX Enhancements: Suggest and implement improvements to the user interface or experience.
• DSA Optimization Tasks: If you're a DSA enthusiast, try to optimize one of the existing algorithms or suggest a new detection method.

📌 Check the Issues tab to find a task that interests you!

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📜 License

This project is licensed under the MIT License.

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🙌 Acknowledgments

• Inspired by real-world financial fraud detection challenges and the need for transparent, explainable security tools.
• Created with 💙 for learning .