Introduction

Customer segmentation is one of the most important applications of Machine Learning in business analytics. It helps companies understand their customers and create targeted marketing strategies.

In this lesson, you will build a Customer Segmentation model using K-Means Clustering.

Problem Statement

The goal is to group customers into different segments based on their behavior.

Example Features

• Age
• Annual Income
• Spending Score

This is an unsupervised learning problem because there are no predefined labels.

Step 1: Import Libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

Step 2: Load Dataset

df = pd.read_csv(“customers.csv”)

Explore Data

df.head()
df.info()

Step 3: Select Features

X = df[[“AnnualIncome”, “SpendingScore”]]

Step 4: Find Optimal Number of Clusters (Elbow Method)

wcss = []

for i in range(1, 11):
 kmeans = KMeans(n_clusters=i)
 kmeans.fit(X)
 wcss.append(kmeans.inertia_)

plt.plot(range(1,11), wcss)
plt.xlabel(“Number of Clusters”)
plt.ylabel(“WCSS”)
plt.show()

Step 5: Train K-Means Model

kmeans = KMeans(n_clusters=5)
y_kmeans = kmeans.fit_predict(X)

Step 6: Visualize Clusters

plt.scatter(X.iloc[:,0], X.iloc[:,1], c=y_kmeans)
plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s=100)
plt.show()

Step 7: Interpretation

• Each cluster represents a group of similar customers
• Businesses can target each group differently
• Helps in marketing and personalization

Key Concepts Used

• Unsupervised learning
• K-Means clustering
• Elbow method
• Data visualization

Real-World Applications

• Customer segmentation in marketing
• Personalized recommendations
• Market analysis
• Business strategy

Improvements You Can Make

• Add more features (location, purchase history)
• Scale data before clustering
• Use other clustering algorithms
• Analyze cluster characteristics

Conclusion

Customer segmentation helps businesses make data-driven decisions. This project demonstrates how clustering can uncover hidden patterns in data.

In the next lesson, you will learn how to deploy Machine Learning models using Flask.

FAQs

What is customer segmentation?

It is grouping customers based on similar characteristics.

Which algorithm is used here?

K-Means clustering.

Why use the elbow method?

To find the optimal number of clusters.

Is this supervised learning?

No, it is unsupervised learning.

Can this project be used in real business?

Yes, it is widely used in marketing and analytics.

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