## Implementation of K Means Clustering for Customer Segmentation

Aim:

To Implement the K means clustering for customer segmentation.

Theory:

K-means clustering is one of the simplest and popular unsupervised machine learning algorithms.Typically, unsupervised algorithms make inferences from datasets using only input vectors without referring to known, or labelled, outcomes.

K-means clustering aims to partition data into k clusters in a way that data points in the same cluster are similar and data points in the different clusters are farther apart. Similarity of two points is determined by the distance between them. The similarity measure is at the core of k-means clustering.

DATASET:

SOURCE CODE:

import pandas as pd
import matplotlib.pyplot as plt
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:])
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")

OUTPUT :

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