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: 6 days 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: 2024-11-17T05:28:17.414Z (2 months ago)
• Topics: data-science, feature-selection, jupyter-notebook, pca, pca-analysis, python, tsne
• Language: Jupyter Notebook
• Homepage:
• Size: 4.21 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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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)

### Data Description: 

This dataset is adapted from the Wine Data Set from https://archive.ics.uci.edu/ml/datasets/wine by removing the information about the types of wine for unsupervised learning.

The following descriptions are adapted from the UCI webpage:

These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.

Number of Attributes: 13 numeric, predictive attributes and the class

Attribute Information:

Alcohol

Malic acid

Ash

Alcalinity of ash

Magnesium

Phenols

Flavanoids

Nonflavanoid phenols

Proanthocyanins

Color intensity

Hue

Dilution

Proline