Implementation of K-Means Clustering Algorithm

Aim

To write a python program to implement K-Means Clustering Algorithm.

Equipment’s required:

Hardware – PCs
Anaconda – Python 3.7 Installation

Algorithm:

Step1

Step1:

Load the CSV into a DataFrame.

Step2:

Print the number of contents to be displayed using df.head().

Step3:

The number of rows returned is defined in pandas option settings.

Step4:

Check your system's maximum column with the pd.options.display.max_column statement.

Step5:

Increase the maximum number of rows to display the entire DataFrame.

Program:

#Name:Praneet.S
#Ref. No:21500603

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')

x1 = pd.read_csv('clustering.csv.csv')
print(x1.head(2))
x2 = x1.loc[:, ['ApplicantIncome', 'LoanAmount']]
print(x2.head(2))

x = x2.values
sns.scatterplot(x[:,0], x[:, 1])
plt.xlabel('Income')
plt.ylabel('Loan')
plt.show()

kmean=KMeans(n_clusters=4)
kmean.fit(x)

print('Cluster Centers:', kmean.cluster_centers_)
print('Labels:', kmean.labels_)

predicted_class = kmean.predict([[9000,120]])
print('The cluster group for Applicant Income 9000 and loanamount 120'

Output:

Result

Thus the K-means clustering algorithm is implemented and predicted the cluster class using python program.

Praneet002 / K-Means-Clustering-algorithm