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

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

About

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

Languages

Language:Jupyter Notebook 100.0%