Vendor Performance Data Analytics

This project analyzes vendor performance data to help organizations make data-driven decisions about their suppliers. It includes data ingestion, database management, and analytics scripts using Python, pandas, and SQLAlchemy.

Features

• Ingests large datasets into a SQL database
• Cleans and preprocesses vendor data
• Performs analytics and generates performance reports
• Jupyter notebooks for interactive data exploration

Project Structure

├── data/                # Place your raw data files here (not included in repo)
├── notebooks/           # Jupyter notebooks for analysis
├── src/                 # Source code (Python scripts)
│   └── main.py
├── requirements.txt     # Python dependencies
├── environment.yml      # (Optional) Conda environment file
└── README.md            # Project documentation

Getting Started

1. Clone the repository:

   git clone https://github.com/yourusername/vendor-performance-data-analytics.git
   cd vendor-performance-data-analytics
   

2. Create and activate a virtual environment:

   python -m venv venv
   venv\Scripts\activate   # On Windows
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Add your dataset:

   • Place your dataset in the data/ folder.
   • Note: Large datasets are not included in this repository. Please download or request access separately.

5. Run the main script or open notebooks:

   python src/main.py
   

   or open and run Jupyter notebooks in the notebooks/ folder.

Notes

• Do not upload large datasets or database files to the repository.
• Update the .gitignore file to exclude sensitive or large files.

License

This project is licensed under the MIT License.

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*For questions or contributions, please open an issue or pull