KMN_on_Iris

In this project , After downloading Iris Dataset From kaggle.com , for begining , the KMN method is applied on the dataset and then the efficiency of clustring method is calculated.

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sb from sklearn import datasets from sklearn.datasets import load_iris from sklearn.cluster import KMeans

	iris = load_iris()
	
	kmn = KMeans(n_clusters=3)
	kmn.fit(iris.data)
	labels = kmn.predict(iris.data)
	
	centroids = kmn.cluster_centers_
	plt.scatter(iris.data[:,0], iris.data[:,2], c=labels)
	plt.scatter(centroids[:,0],centroids[:,2],marker='x',s=150,alpha=0.5)
	plt.show()
	

	inertia_list = []
	for k in np.arange(1, 6):
		kmn = KMeans(n_clusters=k)
		kmn.fit(iris.data)
		inertia_list.append(kmn.inertia_)
	inertia_list
	
plt.plot(np.arange(1,6),inertia_list,'ro-')
plt.xlabel('number of clusters')
plt.ylabel('Inertia')
plt.show()