KMeans_clustering.py

#K-MEANS CLUSTERING
#working code

#IMPORTING LIBRARIES
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

#IMPORTING THE DATASET
dataset=pd.read_csv('Mall_Customers.csv')
x=dataset.iloc[:,[3,4]].values  #independent variable x
y=dataset.iloc[:,3].values

#SPLITTING THE DATASETINTO TRAINING+TEST
from sklearn.cluster imporpt KMeans
wcss=[]
for i in range(1,11):
    kmeans=KMeans(n_clusters=i,init='k-means++',random_state=42)
    kmeans.fit(x)
    wcss.append(kmeans.inertia_)
plt.plot(range(1,11),wcss)
plt.title('THE ELBOW METHOD')
plt.xlabel('number of clusters')
plt.ylabel('WCSS')
plt.show()

#FITTING KMEANS TO DATASET

kmeans=KMeans(n_clusters=5,init='k-means++',random_state=42)
y_kmeans=kmeans.fit_predict(x)

#VISUALISING CLUSTERS
plt.scatter(x[y_kmeans==0,0],x[y_kmeans==0,1],s=100,c='red',label='cluster1')
plt.scatter(x[y_kmeans==1,0],x[y_kmeans==1,1],s=100,c='blue',label='cluster2')
plt.scatter(x[y_kmeans==2,0],x[y_kmeans==2,1],s=100,c='green',label='cluster3')
plt.scatter(x[y_kmeans==3,0],x[y_kmeans==3,1],s=100,c='cyan',label='cluster4')
plt.scatter(x[y_kmeans==4,0],x[y_kmeans==4,1],s=100,c='magenta',label='cluster5')
plt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],s=300,c='yellow',label='centroids')
plt.title('CLUSTER OF CUSTOMERS')
plt.xlabel('ANNUAL INCOME')
plt.ylabel('SPENDING SCORE')
plt.legend()
plt.show()