Anomaly Detection in E-Commerce Transactions

This project aims to detect anomalies in e-commerce transactions to identify fraudulent activities. By leveraging advanced machine learning algorithms, the system can flag transactions that deviate significantly from normal behavior patterns, providing an essential tool for fraud prevention.

Features

• Utilizes state-of-the-art models like Isolation Forest, Autoencoders, and Local Outlier Factor.
• Real-time anomaly detection to ensure swift identification of suspicious transactions.
• Scalable architecture that integrates seamlessly with existing e-commerce platforms.
• Customizable threshold settings for fine-tuning anomaly sensitivity.

Installation

To set up the anomaly detection system on your local machine, follow these steps:

1. Clone the Repository

   git clone https://github.com/username/ecommerce-anomaly-detection.git
   cd ecommerce-anomaly-detection

2. Set Up Virtual Environment (Optional but Recommended)

   python3 -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install Required Packages

   pip install -r requirements.txt

Usage

To start the anomaly detection system, run the main script:

python anomaly_detection.py

The system will process transaction data and flag anomalies in real-time, which will be visible in the results section below.

Live Detection Demo