https://github.com/abdallaabker/k-means-clustering-and-validation-techniques

KMeans Clustering, Elbow method, t-SNE, Silhouette techniques
https://github.com/abdallaabker/k-means-clustering-and-validation-techniques

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KMeans Clustering, Elbow method, t-SNE, Silhouette techniques

• Host: GitHub
• URL: https://github.com/abdallaabker/k-means-clustering-and-validation-techniques
• Owner: AbdallaAbker
• Created: 2021-10-21T03:56:16.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2021-10-21T04:46:56.000Z (about 3 years ago)
• Last Synced: 2024-11-09T21:44:36.557Z (2 months ago)
• Language: Jupyter Notebook
• Size: 271 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# K-means-Clustering-and-validation-techniques

KMeans Clustering, Elbow method, t-SNE, Silhouette techniques

The main goal of this practice is to show-case how to assess the quality of clusters created by the kmeans unsupervised machine learning clustering method. Determining the number of clusters in the dataset can be tricky, thus I utilized various evaluation techniques to help understand and build confidence in choosing the value of k (the number of correct clusters). I used the Elbow-method accompanied by t-SNE method to roughly identify the projected clusters, and finally I utilized the Silhouette score and plots created by the YellowBrick library to confirm the value of k.