🛍️ Customer Segmentation using Machine Learning

A machine learning project that segments retail customers into meaningful groups using K-Means Clustering and customer behavior analysis. The project includes exploratory data analysis, feature engineering, dimensionality reduction, cluster evaluation, and business recommendations for targeted marketing.

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📌 Overview

Customer segmentation helps businesses understand different customer groups based on purchasing behavior and demographics. This project applies unsupervised machine learning techniques to identify customer segments that can support personalized marketing strategies and improve customer engagement.

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🚀 Features

• Exploratory Data Analysis (EDA)
• Data preprocessing and scaling
• K-Means clustering
• Optimal cluster selection (Elbow Method & Silhouette Score)
• PCA visualization
• Cluster profiling
• Customer behavior analysis
• Business recommendations

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🛠 Tech Stack

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Jupyter Notebook

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📊 Dataset

Mall Customers Dataset

Features include:

• Customer ID
• Gender
• Age
• Annual Income
• Spending Score

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🤖 Machine Learning

Unsupervised Learning

• K-Means Clustering

Evaluation

• Elbow Method
• Silhouette Score
• Davies-Bouldin Index

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📈 Project Workflow

Dataset
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Data Cleaning
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Exploratory Data Analysis
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Feature Scaling
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K-Means Clustering
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Cluster Evaluation
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PCA Visualization
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Customer Insights

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📁 Project Structure

customer-segmentation/

│── customer_segmentation.ipynb
│── README.md
│── requirements.txt
│── dataset/
│── images/
└── outputs/

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📊 Key Insights

• Identified distinct customer groups based on spending patterns.
• Compared clustering performance using multiple evaluation metrics.
• Visualized customer clusters using PCA.
• Generated actionable business recommendations for targeted marketing.

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▶️ Installation

git clone https://github.com/YOUR_USERNAME/customer-segmentation.git

cd customer-segmentation

pip install -r requirements.txt

jupyter notebook

Run:

customer_segmentation.ipynb

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📌 Future Improvements

• Hierarchical Clustering
• DBSCAN Clustering
• Interactive Streamlit Dashboard
• Real-world retail dataset integration
• Customer lifetime value prediction

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📜 License

This project is intended for educational and research purposes.

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👨‍💻 Author

Rithvik Jaswal

Computer Science Engineering Student

Interested in Data Science, Machine Learning, Artificial Intelligence, and Business Analytics.

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