Leverage unsupervised machine-learning techniques (K-means) to segment mall customers
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.
📊 Analyze mall customers through machine learning to discover key segments by age, income, and spending, enhancing targeted marketing and revenue.
Implementation of the core concept/algorithm used Hierarchical clustering.
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
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.
Repository for various clustering projects including mall customer segmentation and more. Explore data analysis and clustering techniques
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