K Means_Clustering

 #!/usr/bin/env python

# coding: utf-8

# In[3]:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

df=pd.read_csv(r"C:\Users\91887\OneDrive\Desktop\Machine_Learning\week4_Mall_Customers.csv")

x =df.iloc[:, [3, 4]].values

# In[4]:

from sklearn.cluster import KMeans

wcss_list= []

for i in range(1, 11):

    kmeans = KMeans(n_clusters=i, init='k-means++', random_state= 42)

    kmeans.fit(x)

    wcss_list.append(kmeans.inertia_)

plt.plot(range(1, 11), wcss_list)

plt.title('The Elobw Method Graph')

plt.xlabel('Number of clusters(k)')

plt.ylabel('wcss_list')

plt.show()

# In[ ]: