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https://github.com/moindalvs/assignment_pca_wine_dataset
Case Summary Perform Principal component analysis and perform clustering using first 3 principal component scores (both Heirarchical and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we have ignored at the begining who shows it has 3 clusters)
https://github.com/moindalvs/assignment_pca_wine_dataset
data-science feature-selection jupyter-notebook pca pca-analysis python tsne
Last synced: about 11 hours ago
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Case Summary Perform Principal component analysis and perform clustering using first 3 principal component scores (both Heirarchical and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we have ignored at the begining who shows it has 3 clusters)
- Host: GitHub
- URL: https://github.com/moindalvs/assignment_pca_wine_dataset
- Owner: MoinDalvs
- Created: 2022-05-09T14:12:38.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-05-09T14:14:08.000Z (over 2 years ago)
- Last Synced: 2023-03-08T12:08:12.245Z (over 1 year ago)
- Topics: data-science, feature-selection, jupyter-notebook, pca, pca-analysis, python, tsne
- Language: Jupyter Notebook
- Homepage:
- Size: 4.21 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0