Program to implement the K Means Clustering for Customer Segmentation.
Developed by: H.Syed Abdul Wasih 
Register Number: 212221240057

import pandas as pd
import matplotlib.pyplot as plt
data=pd.read_csv("Mall_Customers.csv")
data.head()
data.info()
data.isnull().sum()
from sklearn.cluster import KMeans
wess=[]
for i in range(1,11):
    kmeans=KMeans(n_clusters=i,init="k-means++")
    kmeans.fit(data.iloc[:,3:])
    wess.append(kmeans.inertia_)
plt.plot(range(1,11),wess)
plt.xlabel("no of clusters")
plt.ylabel("wess")
plt.title("Elbow Method")
km=KMeans(n_clusters=5)
km.fit(data.iloc[:,3:])
y_pred=km.predict(data.iloc[:,3:])
data["cluster"]=y_pred
df0=data[data["cluster"]==0]
df1=data[data["cluster"]==1]
df2=data[data["cluster"]==2]
df3=data[data["cluster"]==3]
df4=data[data["cluster"]==4]
plt.scatter(df0["Annual Income"],df0["Score"],c="red",label="cluster0")
plt.scatter(df1["Annual Income"],df1["Score"],c="black",label="cluster1")
plt.scatter(df2["Annual Income"],df2["Score"],c="blue",label="cluster2")
plt.scatter(df3["Annual Income"],df3["Score"],c="green",label="cluster3")
plt.scatter(df4["Annual Income"],df4["Score"],c="magenta",label="cluster4")
plt.legend() 
plt.title("Customer Segments")