Knowledge Discovery in Databases (KDD)

Welcome to the KDD repository! This project is designed as a part of a coursework or assignment in the field of Knowledge Discovery in Databases, focusing on data mining, data analysis, and knowledge extraction techniques. The repository includes Jupyter Notebooks that demonstrate key KDD concepts, algorithms, and applications.

Table of Contents

• Overview
• Project Structure
• Installation
• Contributing
• License
• Author

Overview

The goal of this repository is to apply KDD methodologies to a dataset, covering the stages from data preprocessing to knowledge extraction. The main objectives are:

• Data Cleaning and Preparation: Removing noise and handling missing data.
• Data Transformation: Feature engineering and transformation techniques.
• Data Mining: Applying algorithms for pattern recognition and prediction.
• Evaluation: Assessing model performance and extracting meaningful insights.

Project Structure

• 1/ -
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Installation

To run the notebooks locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/Ziyi-star/KDD.git
   cd KDD

2. Set up a virtual environment and install dependencies:

   python3 -m venv kdd_env
   source kdd_env/bin/activate  # On Windows: kdd_env\Scripts\activate
   pip install -r requirements.txt

3. Launch Jupyter Notebook:

   jupyter notebook

Contributing

Contributions are welcome! Please fork this repository and create a pull request for any enhancements, bug fixes, or additional features.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add 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 more information.

Author

• Ziyi Liu - Creator and Maintainer of the KDD project repository