EXP:8 Implementation-of-K-Means-Clustering-for-Customer-Segmentation

Date :

AIM:

To write a program to implement the K Means Clustering for Customer Segmentation.

Equipments Required:

Hardware – PCs
Anaconda – Python 3.7 Installation / Jupyter notebook

Algorithm:

1.Import pandas and matplot libraries.

2.Import Kmeans algorithm to solve customer segmentation.

3.Using the for loop cluster the given data.

4.Predict the output and plot data graphs.

5.Display the outputs

Program:

Program to implement the K Means Clustering for Customer Segmentation.

Developed by:Kathirvelan.K

RegisterNumber:212221220026

import pandas as pd
import matplotlib.pyplot as plt
data=pd.read_csv("/content/Mall_Customers (1).csv")

data.head()

data.info()

data.isnull().sum()

from sklearn.cluster import KMeans
wcss= []

for i in range(1,11):
    kmeans = KMeans(n_clusters =i,init="k-means++")
    kmeans.fit(data.iloc[:,3:])
    wcss.append(kmeans.inertia_)
    
plt.plot(range(1,11),wcss)
plt.xlabel("No of Clusters")
plt.ylabel("wcss")
plt.title("Elbow Method")

km=KMeans(n_clusters = 5)
km.fit(data.iloc[:,3:])

y_pred=km.predict(data.iloc[:,3:])
y_pred

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 (k$)"],df0["Spending Score (1-100)"],c="red",label="cluster0")
plt.scatter(df1["Annual Income (k$)"],df1["Spending Score (1-100)"],c="black",label="cluster1")
plt.scatter(df2["Annual Income (k$)"],df2["Spending Score (1-100)"],c="blue",label="cluster2")
plt.scatter(df3["Annual Income (k$)"],df3["Spending Score (1-100)"],c="green",label="cluster3")
plt.scatter(df4["Annual Income (k$)"],df4["Spending Score (1-100)"],c="magenta",label="cluster4")
plt.legend()
plt.title("Customer Segments")