sa1mpleritgithub / implementation-of-k-means-clustering-for-customer-segmentation

Implementation-of-K-Means-Clustering-for-Customer-Segmentation

AIM:

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

Equipments Required:

1. Hardware – PCs
2. Anaconda – Python 3.7 Installation / Moodle-Code Runner

Algorithm

Program:

/*
Program to implement the K Means Clustering for Customer Segmentation.
Developed by: 
RegisterNumber:  
*/
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
wcss = [] 
#WCSS stands for Withitn Cluster Sum of Square. It is sum of squared distance each point and centroid in the cluster.

for i in range(1,11):
    kmeans = KMeans(n_clusters = i,init = "k-means++")
    kmeans.fit(data.iloc[:,3:])
    wcss.append(kmeans.inertia_)
wcss

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:])
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="yellow",label="cluster1")
plt.scatter(df2["Annual Income (k$)"],df2["Spending Score (1-100)"],c="pink",label="cluster2")
plt.scatter(df3["Annual Income (k$)"],df3["Spending Score (1-100)"],c="green",label="cluster3")
plt.scatter(df4["Annual Income (k$)"],df4["Spending Score (1-100)"],c="purple",label="cluster4")
plt.legend()
plt.title("Customer Segments")

Output:

Result:

Thus the program to implement the K Means Clustering for Customer Segmentation is written and verified using python programming.