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

Import libraries

Import dataset

Create Normalized data frame

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)

Build Cluster algorithm using K=4 and K=3

Assign clusters to the data set

Compute the centroids

Plot the clusters

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P30. Unsupervised ML - K-Means Clustering (Non-Hierarchical Clustering) (Univ.)-ver-01.ipynb		P30. Unsupervised ML - K-Means Clustering (Non-Hierarchical Clustering) (Univ.)-ver-01.ipynb
P30. Unsupervised ML - K-Means Clustering (Non-Hierarchical Clustering) (Univ.).ipynb		P30. Unsupervised ML - K-Means Clustering (Non-Hierarchical Clustering) (Univ.).ipynb
README.md		README.md

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