https://github.com/patilsukanya/assignment-08-pca

Used libraries and functions as follows:
https://github.com/patilsukanya/assignment-08-pca

clustering covarience-matrix data-mining-algorithm decomposition dendogram elbow-plot hierarchical kmeans-clustering pca-analysis preprocessing python scale scatter-plot scipy sklearn variance-plot wcss

Last synced: 3 months ago
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Used libraries and functions as follows:

• Host: GitHub
• URL: https://github.com/patilsukanya/assignment-08-pca
• Owner: PatilSukanya
• Created: 2022-11-01T04:41:58.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-11-01T05:12:14.000Z (over 2 years ago)
• Last Synced: 2025-02-23T13:38:07.707Z (4 months ago)
• Topics: clustering, covarience-matrix, data-mining-algorithm, decomposition, dendogram, elbow-plot, hierarchical, kmeans-clustering, pca-analysis, preprocessing, python, scale, scatter-plot, scipy, sklearn, variance-plot, wcss
• Language: Jupyter Notebook
• Homepage:
• Size: 104 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Assignment-08-PCA

Perform Principal component analysis and perform clustering using first 

3 principal component scores (both heirarchial 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)df

## PCA Implementation

Checking with other Clustering Algorithms

#### Hierarchical Clustering

#### K-Means Clustering

#### Build Cluster algorithm using K=3