Implementation of the core concept/algorithm used Hierarchical clustering.
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Updated
Jan 19, 2026 - Jupyter Notebook
Implementation of the core concept/algorithm used Hierarchical clustering.
Leverage unsupervised machine-learning techniques (K-means) to segment mall customers
📊 Analyze mall customers through machine learning to discover key segments by age, income, and spending, enhancing targeted marketing and revenue.
A comprehensive machine learning project demonstrating hierarchical clustering for customer segmentation on the Mall Customers dataset. Includes EDA, preprocessing, multiple linkage/distance comparisons, and professional visualizations.
A Machine Learning project that groups retail customers based on purchase history using K-Means Clustering. Built with Python, Scikit-Learn, and Pandas to analyze the "Mall Customers" dataset for targeted marketing insights.
Clustering-based customer segmentation using K-Means and DBSCAN on Mall Customers data to identify distinct spending groups.
Training the K- Means and Agglomerative HC clustering models and Visualising the clusters of the all clustering models
Repository for various clustering projects including mall customer segmentation and more. Explore data analysis and clustering techniques
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