https://github.com/rohit1901/py-cluster

Classifier and Cluster Analysis in Data Science
https://github.com/rohit1901/py-cluster

classification clustering data-science k-means-clustering machine-learning pytest python python3 ruff scikit-learn

Last synced: 7 days ago
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Classifier and Cluster Analysis in Data Science

• Host: GitHub
• URL: https://github.com/rohit1901/py-cluster
• Owner: rohit1901
• License: mit
• Created: 2023-09-15T13:21:58.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-17T11:56:37.000Z (over 1 year ago)
• Last Synced: 2024-11-16T19:33:39.554Z (2 months ago)
• Topics: classification, clustering, data-science, k-means-clustering, machine-learning, pytest, python, python3, ruff, scikit-learn
• Language: Python
• Homepage:
• Size: 123 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# K-Means Clustering Application

![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

![Rohit](https://github.com/rohit1901/py-cluster/actions/workflows/test.yml/badge.svg)

This Python application demonstrates K-Means clustering on various datasets and provides a modularized structure for loading data and performing clustering. The code is organized into two modules: `data_loader` and `clustering`.

## Getting Started

These instructions will help you set up and run the project on your local machine.

### Prerequisites

- Python 3.x

- NumPy

- scikit-learn

- seaborn

- matplotlib

- ruff

- pytest

You can install the required dependencies using pip:

```

pip install numpy scikit-learn seaborn matplotlib ruff pytest

```

### Installation

1. Clone the repository:

```

git clone https://github.com/rohit1901/py-cluster.git

cd py-cluster

```

2. Run the main script:

```

python main_1.py

python main_2.py

```

## Code Structure

- `data_utils` module: Responsible for loading data and extracting dimensions and samples.

- `clustering` module: Implements K-Means clustering and related functions.

- `classify_unknown_samples` module: Implements a function to classify unknown samples using a trained model.

- `main_1` script: Demonstrates classification of unknown samples using nearest neighbour classification.

- `main_2` script: Demonstrates K-Means clustering on various datasets.

## Testing

To run unit tests for the application, use the following commands:

```

pytest

```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgments

- This project was inspired by the need to understand K-Means clustering and its implementation in Python.

- Thanks to the contributors and open-source libraries that made this project possible.