https://github.com/chhavi-trivedi/prediction-using-unsupervised-ml
K-means clustering of iris dataset with visualization.
https://github.com/chhavi-trivedi/prediction-using-unsupervised-ml
datascience k-means-clustering machine-learning python unsupervised-machine-learning
Last synced: 2 months ago
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K-means clustering of iris dataset with visualization.
- Host: GitHub
- URL: https://github.com/chhavi-trivedi/prediction-using-unsupervised-ml
- Owner: Chhavi-Trivedi
- Created: 2020-11-07T06:14:54.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2020-11-07T06:22:13.000Z (about 5 years ago)
- Last Synced: 2025-10-10T07:32:27.556Z (2 months ago)
- Topics: datascience, k-means-clustering, machine-learning, python, unsupervised-machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 762 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Prediction-using-Unsupervised-ML
**OBJECTIVE-Clustering of Iris Dataset**
From the given ‘Iris’ dataset, predict the optimum number of clusters and
represent it visually.
**ALGORITHM USED - K-Means**
K-means is a centroid-based algorithm, or a distance-based algorithm, where we calculate the distances to assign a point to a cluster. In K-Means, each cluster is associated with a centroid.
**Workflow behind Implemention of K-Means Clustering**
1. Choose the number of clusters k
2. Select k random points from the data as centroids
3. Assign all the points to the closest cluster centroid
4. Recompute the centroids of newly formed clusters
5. Repeat steps 3 and 4