🛒💳 Customer Segmentation Using Clustering

Customer Segmentation with Unsupervised Machine Learning Techniques

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📌 What is Customer Segmentation?

Customer segmentation is the practice of dividing a customer base into distinct groups based on common characteristics such as demographics, purchase behavior, and income. It helps businesses deliver personalized marketing and improve overall engagement.

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🎯 Objective

This project demonstrates how unsupervised machine learning techniques can be used to identify meaningful customer segments in a mall dataset. Multiple clustering methods were explored and visualized to evaluate grouping effectiveness.

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💡 Key Highlights

• ✅ Implemented K-Means, Hierarchical, Gaussian Mixture, Mini-batch KMeans, and DBSCAN for segmentation
• ✅ Visualized clusters using PCA and 2D scatter plots
• ✅ Evaluated cluster quality using Elbow Method and Silhouette Score
• ✅ Created marketing personas to assist business decision-making

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🧰 Tech Stack & Tools Used

• Python 🐍
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• PCA
• Power BI (for business-level dashboards)

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📂 Dataset

This project uses a public dataset from Kaggle:
🔗 Customer Segmentation Dataset

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🧠 Author

Made with 💻 by Ayush Kumar
GitHub | LinkedIn

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📄 License

This project is licensed under the MIT License.