https://github.com/sajjad425/kmeans_clustering

The dataset includes the following columns: Id, SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm, and Species. We will use the Sepal and Petal measurements to predict the optimum number of clusters using the KMeans algorithm.
https://github.com/sajjad425/kmeans_clustering

kmeans-clustering kmeans-clustering-algorithm

Last synced: 2 days ago
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The dataset includes the following columns: Id, SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm, and Species. We will use the Sepal and Petal measurements to predict the optimum number of clusters using the KMeans algorithm.

• Host: GitHub
• URL: https://github.com/sajjad425/kmeans_clustering
• Owner: sajjad425
• Created: 2024-07-12T19:13:13.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-07-12T19:18:57.000Z (4 months ago)
• Last Synced: 2024-07-12T21:37:41.085Z (4 months ago)
• Topics: kmeans-clustering, kmeans-clustering-algorithm
• Language: Jupyter Notebook
• Homepage:
• Size: 105 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# kmeans_clustering

## Summary of Steps:

1. **Import necessary libraries.**

2. **Preprocess the data:** Standardized the features to ensure equal contribution.

3. **Determine the optimal number of clusters using the Elbow method:** The Elbow graph indicated 3 as the optimal number.

4. **Apply KMeans clustering:** Clustered the data into 3 clusters.

5. **Visualize the clusters:** Created a scatter plot to visually represent the clusters.