K-Means Clustering in Python: A Beginner’s Guide

K-means clustering is a popular unsupervised machine learning algorithm used to classify data into groups or clusters based on their similarities or dissimilarities. The algorithm works by partitioning the data points into k clusters, with each data point belonging to the cluster that has the closest mean.

In this tutorial, we will implement the k-means clustering algorithm using Python and the scikit-learn library.

Step 1: Import the necessary libraries

We will start by importing the necessary libraries for implementing the k-means algorithm. We will use NumPy for numerical computing, pandas for data manipulation, matplotlib for data visualization, and scikit-learn for the k-means algorithm implementation.

import numpy as np
import pandas as pd
from sklearn.cluster import KMeans 
import matplotlib.pyplot as plt

The above code imports the necessary libraries for implementing k-means clustering in Python.

• numpy (imported as np) is a numerical computing library in Python, used for working with arrays and matrices.
• pandas (imported as pd) is a data manipulation library used for handling and analyzing tabular data.
• KMeans is a class from sklearn.cluster that…