Use an easy to understand real-world dataset to demonstrate how to implement K-means clustering with Scikit-learn library and visualize the results using pandas, Matplotlib and seaborn.
K-means clustering is a popular unsupervised machine learning algorithm used for clustering data points into groups or clusters based on their similarities. The algorithm aims to partition a dataset into k clusters, where k is a predetermined number of clusters.
The k-means algorithm works by iteratively assigning each data point to the nearest centroid or center of a cluster, based on a distance metric (usually Euclidean distance). The centroid of each cluster is then updated as the mean of all the data points assigned to that cluster. This process continues until the centroids converge, i.e., until the assignment of data points to clusters does not change.
K-means clustering is widely used in various applications such as image segmentation, customer segmentation, and anomaly detection. However, it is sensitive to the initial selection of centroids, and the resulting clusters can vary based on the initial random assignment of centroids. Therefore, it is common to run the algorithm multiple times with different random initializations to improve the chances of finding the best clustering.
Scikit-learn is a popular Python library for machine learning, which includes an implementation of the k-means clustering algorithm. In this article, we will demonstrate how to implement a K-means clustering using scikit learn with an easy understanding real-world example.