K-means Clustering Using the Elbow Method.

Introduction

Clustering or cluster analysis is machine learning technique, which groups the unlabeled dataset. It can be said that as "way of grouping the data points into different clusters, consisting of similar data points. The objects with the possible similarities remain in a group and those that have less or no similarities with another group"

Let's understand the clustering technique with the real-world example of Mall. When customers visit any shopping mall, we can observe that the things with similar usage are grouped together. Such as the t-shirts are grouped in one section, and trousers are at other sections, similarly, at vegetable sections, apple, bananas, Mangoes, e.t.c are grouped in a separate section, so that customers can easily find out the things. The clustering technique also works in the same way. Other examples of clustering are grouping of documents according to topics.

Python Implementation of K-means Clustering Algorithm.

Prerequisites

• What is K-means Clustering Algorithm.
• How does the k-means algorithm work?
• How to find and choose the value of "k: number of clusters in k-means clustering.
• Data preprocessing.
• Standardization and feature scaling.
• Fitting the training and Data Transformation.
• Training the K-means Algorithm on the Training Dataset.
• Make Predictions.
• Inspect the coordinates of the 5 centroids
• Finding the Optimal (k) number of clusters using the Elbow Method.
• Visualizing the Clusters
• Summary Findings

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