https://github.com/torodata/k-means-clustering-web-application
Explore K-means clustering with my interactive web app. Visualize and cluster data points, learn its applications, and best practices.
https://github.com/torodata/k-means-clustering-web-application
Last synced: 3 months ago
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Explore K-means clustering with my interactive web app. Visualize and cluster data points, learn its applications, and best practices.
- Host: GitHub
- URL: https://github.com/torodata/k-means-clustering-web-application
- Owner: ToroData
- License: mit
- Created: 2023-12-03T19:10:13.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-03T19:14:47.000Z (over 2 years ago)
- Last Synced: 2025-03-12T14:18:31.665Z (over 1 year ago)
- Language: HTML
- Size: 6.84 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# K-means-Clustering-Web-Application
This web application demonstrates the K-means clustering algorithm, a fundamental technique in machine learning and data analysis. With an interactive and user-friendly interface, users can generate random data points, visualize them in 2D, and apply K-means clustering to group data points into clusters. The application provides insights into how K-means works, its real-world applications, and best practices for interpreting results.
## Key Features:
- Generate Random Data: Create random data points in 2D and 3D for clustering.
- Interactive Visualization: Visualize data points, cluster centroids, and the clustering process.
- Real-world Applications: Explore the use cases of K-means in customer segmentation, genetic data analysis, document classification, and image compression.
- Easy-to-understand Tutorial: Learn about the algorithm's principles, its advantages, and how to interpret results.
- Best Practices: Discover best practices for data normalization, choosing the number of clusters, and interpreting clusters effectively.
## Technologies Used:
- HTML, CSS, JavaScript for the web interface
- D3.js for data visualization
- Python (Flask) for server-side processing of K-means clustering
## License
This project is licensed under the MIT License - see the [MIT LICENSE](https://choosealicense.com/licenses/mit/) file for details.
## Author
- [@RicardSantiagoRaigadaGarcía](https://www.thedatascientist.digital/)