A robust and scalable system for real-time transaction monitoring using Kafka, MongoDB, and Matplotlib. This project generates synthetic transaction data, processes it for suspicious activity detection, and provides real-time visualizations of transaction trends.

• Data Generation: Uses the Faker library to generate realistic synthetic transaction data.
• Real-Time Processing: Kafka-based producer-consumer architecture ensures seamless transaction streaming.
• Suspicious Activity Detection: Automatically flags transactions with amounts exceeding 30,000ドル.
• Database Integration: Stores processed transactions in MongoDB for further analysis.
• Live Visualization: Dynamic Matplotlib charts visualize transaction trends in real time.

1. Install Kafka , Zookeeper , and Kafka Manager .
2. Install MongoDB.
3. Clone this repository:

   git clone https://github.com/yourusername/transmon.git
   cd transmon

4. Install Python dependencies:

   pip install -r requirements.txt

1. Start the Kafka producer:

   python producer.py

2. Start the Kafka consumer with real-time visualization:

   python consumer.py

• Producer: Generates and streams transaction data to Kafka.
• Consumer: Processes data, detects anomalies, and stores results in MongoDB while updating live visualizations.
• Database: MongoDB stores transaction data for persistence and analytics.

• Python 3.8+
• Kafka and Zookeeper
• MongoDB
• Python dependencies listed in requirements.txt

1. Modify transaction_data.py (or equivalent) to customize the data schema or generation logic.
2. Adjust thresholds for suspicious activity detection in consumer.py if needed.
3. Use db_connection.py for database querying and analytics.

Contributions are welcome! Feel free to fork this repository and submit a pull request with your enhancements or fixes.

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

YousefMohammad
Contact: ymanger34@gmail.com