variance-plot

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vaitybharati / Assignment-08-PCA-Data-Mining-Wine-
Assignment-08-PCA-Data-Mining-Wine data. 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)
python pca-analysis data-mining-algorithms decomposition sklearn preprocessing scale covariance-matrix variance-plot scatter-plot hierarchical-clustering scipy dendrogram agglomerative-clustering kmeans-clustering clustering-algorithm elbow-plot wcss
Language:Jupyter Notebook 2
Abhik35 / Assignment-PCA-Data-Mining-Wine-
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) PCA Implementation Checking with other Clustering Algorithms 1.Hierarchical Clustering 2.K-Means Clustering Build Cluster algorithm using K=3
python scale sklearn pca pca-analysis decomposition scipy scatter-plot preprocessing data-mining-algorithms clustering kmeans-clustering covariance-matrix hierarchical-clustering variance-plot wcss dendrogram elbow-plot agglomerative-clustering
Language:Jupyter Notebook 1
PatilSukanya / Assignment-08-PCA
Used libraries and functions as follows:
clustering data-mining-algorithm decomposition dendogram elbow-plot hierarchical kmeans-clustering pca-analysis preprocessing python scale scatter-plot scipy sklearn variance-plot wcss covarience-matrix
Language:Jupyter Notebook

variance-plot

vaitybharati / Assignment-08-PCA-Data-Mining-Wine-

Abhik35 / Assignment-PCA-Data-Mining-Wine-

PatilSukanya / Assignment-08-PCA