https://github.com/maemoonfarooq/credit-card-fraud-detector-using-apache-kafka

The credit card fraud detection system which sends transaction data to a Kafka topic, and processes this data to detect fraud using predefined rules or a machine learning model, triggering alerts for fraudulent transactions.
https://github.com/maemoonfarooq/credit-card-fraud-detector-using-apache-kafka

apache-kafka big-data-analytics kafka pyhton3

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The credit card fraud detection system which sends transaction data to a Kafka topic, and processes this data to detect fraud using predefined rules or a machine learning model, triggering alerts for fraudulent transactions.

• Host: GitHub
• URL: https://github.com/maemoonfarooq/credit-card-fraud-detector-using-apache-kafka
• Owner: MaemoonFarooq
• Created: 2024-06-22T10:57:50.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-06-22T11:03:01.000Z (11 months ago)
• Last Synced: 2025-01-28T15:41:05.669Z (4 months ago)
• Topics: apache-kafka, big-data-analytics, kafka, pyhton3
• Language: Python
• Homepage:
• Size: 254 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Credit Card Fraud Detector using Apache Kafka

This project implements a real-time credit card fraud detection system using Apache Kafka in Python. The system consists of a producer that generates and sends transaction data to a Kafka topic and a consumer that processes the transactions to detect fraud.

Table of Contents

- Installation

- Usage

- Components

  - Producer

  - Consumer

- How Kafka Helps

- Configuration

- Contributing

- License

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/credit-card-fraud-detector.git

   cd credit-card-fraud-detector

2. Install dependencies:

   Make sure you have Python installed. Install the required Python packages using pip:

   pip install -r requirements.txt

3. Install Apache Kafka:

   Follow the instructions to download and install Apache Kafka from Kafka's official website: https://kafka.apache.org/quickstart.

Usage

1. Start the Kafka server:

   bin/zookeeper-server-start.sh config/zookeeper.properties

   bin/kafka-server-start.sh config/server.properties

2. Create a Kafka topic:

   bin/kafka-topics.sh --create --topic transactions --bootstrap-server localhost:9092 --partitions 1 --replication-factor 1

3. Run the producer:

   The producer will generate and send transaction data to the Kafka topic.

   python producer.py

4. Run the consumer:

   The consumer will listen to the Kafka topic and process the transactions for fraud detection.

   python consumer.py

Components

Producer

The producer (producer.py) is responsible for generating transaction data and sending it to a Kafka topic. The data can be simulated or obtained from a real-time source. Each transaction is sent as a message to the Kafka topic.

Consumer

The consumer (consumer.py) listens to the Kafka topic for incoming transaction messages. It processes each transaction to detect fraudulent activities based on predefined rules or a machine learning model. The consumer can take actions such as logging, alerting, or blocking fraudulent transactions.

How Kafka Helps

Apache Kafka serves as a highly scalable and reliable messaging system that enables real-time processing of transaction data. By using Kafka, the system ensures that transaction messages are quickly and efficiently delivered from the producer to the consumer. Kafka's distributed architecture provides fault tolerance and high availability, ensuring that the fraud detection system remains robust and can handle large volumes of data. This real-time data pipeline allows for immediate detection and response to potential fraudulent activities.

Configuration

- Kafka Configuration:

  The Kafka server configuration should be updated in the server.properties file.

  

- Producer Configuration:

  Update the Kafka topic and broker details in the producer.py script.

- Consumer Configuration:

  Update the Kafka topic and broker details in the consumer.py script. Define the fraud detection logic based on your requirements.

Contributing

Contributions are welcome! Please submit a pull request or open an issue to discuss your changes.

License

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