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Topic: "wcss"

ArtichaTM/CS2_WCSSkills

WCSSkills plugin for CS2

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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)

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vaitybharati/P30.-Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ.-

Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ. Use Elbow Graph to find optimum number of clusters (K value) from K values range. The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion WCSS. Plot K values range vs WCSS to get Elbow graph for choosing K (no. of clusters)

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vaitybharati/Assignment-07-K-Means-Clustering-Airlines-

Assignment-07-K-Means-Clustering-Airlines. Perform clustering (K means clustering) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include information on their mileage history and on different ways they accrued or spent miles in the last year. The goal is to try to identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers.

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