🛍️ Customer Segmentation using Hierarchical Clustering

This project demonstrates Hierarchical Clustering for customer segmentation using a dataset of annual income and spending scores. It visually identifies customer groups based on their purchasing behavior.

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📊 Dataset

• Features Used:
  • Annual Income (k$)
  • Spending Score (1-100)
• Example source: Mall Customer Segmentation Dataset (Kaggle or synthetic)

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🧠 Methods Used

• Dendrogram Plotting to determine the optimal number of clusters
• Agglomerative Clustering using Ward linkage
• 2D Visualization of clusters

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🧰 Tools Used

• Python (Jupyter Notebook)
• pandas, numpy – data handling
• matplotlib, scipy – dendrogram and plotting
• scikit-learn – Agglomerative Clustering

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📌 Key Takeaway

Hierarchical clustering effectively segments customers into distinct groups based on income and spending behavior — useful in marketing strategy, loyalty targeting, and personalized recommendations.

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