K-Means Clustering Hands-on

Introduction 

K-Means clustering is a popular unsupervised machine learning algorithm that is used to group data into clusters based on similarity. The goal of K-Means is to partition n observations into k clusters, where each observation belongs to the cluster with the nearest mean.

FORMULA:

where,

"||xi - vj||" is the Euclidean distance between xi and vj.

"ci" is the number of data points in ith clusters. 

"c" is the number of cluster centers. 

Code : 

Step 1- Importing Dependencies

 import pandas as pd

import numpy as np 
import matplotlib.pyplot as plt

from sklearn.datasets import load_iris

data = load_iris()
x = data.data

Step 3- Elbow Method

from sklearn.cluster import KMeans
elbow = []
for i in range(1,11):
    kmeans = KMeans(n_clusters=i,init='k-means++')
    kmeans.fit(x)
    elbow.append(kmeans.inertia_)
Visualize Elbow
plt.plot(range(1,11),elbow)
plt.show()




Step 4- Creating Model
kmeans = KMeans(n_clusters=3,init='k-means++')
y = kmeans.fit_predict(x)

Visualization






Explaination

In this code, we define the number of clusters to be 3 and load a dataset with two features. We then initialize the KMeans model with the number of clusters and fit it to the data using the fit() method. Finally, we use the predict() method to assign each data point to a cluster and print the resulting cluster labels.