🛍️ Customer Segmentation using K-Means Clustering

📌 Project Overview

This project applies the K-Means Clustering Algorithm, an unsupervised machine learning technique, to segment customers of a retail store based on their purchase behavior. Using the Mall Customer Dataset, customers are grouped into different clusters based on their Annual Income and Spending Score.

Customer segmentation helps businesses understand their customers better and design targeted marketing strategies.

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🎯 Objective

The goal of this project is to:

• Analyze customer purchasing patterns
• Identify groups of similar customers
• Apply K-Means clustering to form meaningful customer segments

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🧠 Machine Learning Concept Used

• Unsupervised Learning
• K-Means Clustering Algorithm
• Elbow Method to determine the optimal number of clusters

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📊 Dataset Information

The dataset used in this project is the Mall Customers Dataset which contains the following attributes:

Feature	Description
CustomerID	Unique ID of the customer
Gender	Male / Female
Age	Customer age
Annual Income (k$)	Customer yearly income
Spending Score (1-100)	Score assigned by the mall based on purchasing behavior

For clustering, the features used are:

• Annual Income
• Spending Score

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🛠️ Technologies Used

• Python
• Pandas – Data processing
• Matplotlib – Data visualization
• Scikit-learn – Machine learning algorithms

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⚙️ Project Workflow

1. Import required Python libraries
2. Load the dataset using Pandas
3. Select relevant features (Income & Spending Score)
4. Use the Elbow Method to find the optimal number of clusters
5. Apply the K-Means Algorithm
6. Visualize customer segments using scatter plots

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📈 Results

The algorithm groups customers into five distinct clusters, representing different purchasing behaviors such as:

• High income – High spending customers
• High income – Low spending customers
• Low income – High spending customers
• Low income – Low spending customers
• Average customers

This segmentation helps businesses improve marketing strategies and customer targeting.

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📂 Project Structure

SCT_ML_2
│
├── Mall_Customers.csv
├── kmeans_customer_segmentation.py
└── README.md

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🚀 How to Run the Project

1️⃣ Clone the repository

git clone https://github.com/codewithRakz/SCT_ML_2.git

2️⃣ Install required libraries

pip install pandas matplotlib scikit-learn

3️⃣ Run the program

python kmeans_customer_segmentation.py

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📷 Output

The project generates:

• Elbow Method Graph
• Customer Segmentation Visualization

These plots show how customers are grouped based on their purchasing behavior.

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💡 Key Learnings

• Understanding unsupervised machine learning
• Implementing K-Means clustering
• Using data visualization for insights
• Applying the Elbow Method for cluster optimization

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🔗 Internship Task

This project was completed as part of an internship task focused on machine learning and customer analytics.

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