E-commerce Analytics Dashboard

This project generates synthetic e-commerce transaction data and provides an interactive dashboard for analysis. The solution includes data generation, data processing, and visualization components.

Features

• Generate synthetic e-commerce transaction data (100,000+ rows)
• Interactive Streamlit dashboard with multiple views
• Daily profit/loss analysis
• Product and category performance metrics
• Fraud detection analysis
• Data filtering and export capabilities

Prerequisites

• Python 3.8+
• pip (Python package manager)

Installation

1. Clone the repository:

   git clone <repository-url>
   cd OrderLytics

2. Create and activate a virtual environment (recommended):

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

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Generate Synthetic Data

Run the data generation script to create a CSV file with synthetic e-commerce data:

python generate_data.py

This will create a file named ecommerce_orders.csv in the project directory with 100,000+ rows of synthetic data.

2. Run the Dashboard

Start the Streamlit dashboard with:

streamlit run dashboard.py

The dashboard will open automatically in your default web browser. If it doesn't, navigate to http://localhost:8501.

Dashboard Features

Data Exploration

• Upload and explore any CSV file with e-commerce data
• Filter data by date range, categories, and order status
• View raw data with sorting and search capabilities

Profit & Loss Analysis

• Daily revenue and profit trends
• Profit by category
• Monthly financial summary with revenue, cost, and profit margins

Product Analytics

• Top-selling products
• Category performance
• Product trends over time

Fraud Detection

• Identify potential fraudulent orders based on:
  • Unusually large orders
  • Multiple orders with similar details
  • Suspicious payment patterns
  • High-value orders with high discounts

Project Structure

OrderLytics/
├── data/
│   └── ecommerce_orders.csv    # Generated synthetic data
├── src/
│   ├── __init__.py
│   ├── data_generator.py       # Data generation logic
│   └── utils.py               # Utility functions
├── tests/
│   ├── __init__.py
│   └── test_data_generator.py  # Unit tests
├── .gitignore
├── .dockerignore
├── dashboard.py               # Streamlit dashboard
├── generate_data.py           # Data generation script
├── requirements.txt           # Python dependencies
└── README.md                  # This file

Testing

Run the test suite with:

pytest tests/

Docker Support (Optional)

A Dockerfile is provided for containerized deployment:

1. Build the Docker image:

   docker build -t ecommerce-dashboard .

2. Run the container:

   docker run -p 8501:8501 ecommerce-dashboard

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

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