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The goal is to uncover patterns, group similar customers, and assign scores to support targeted marketing and business strategies.\n\n# Business Understanding\n\nClustering, an unsupervised machine learning technique, is employed for customer segmentation. Clustering aims to discover inherent groups or clusters within data, without prior knowledge of their existence. The following highlights the advantages and disadvantages of utilizing clustering for customer segmentation.\n\nAdvantages:\n  * Reveals hidden or unexpected customer groups\n  * Flexible and adaptable to various datasets\n  * Requires less domain-specific expertise\n  * Fast and scalable for large datasets\n    \nDisadvantages:\n  * Clusters may be hard to interpret\n  * Limited value if behavior data is missing\n\n# Data Description\n\nThe sample Dataset summarizes the usage behavior of about 200 active customers during the last 3 months. The file is at a customer level with 5 behavioral variables.\n\nDataset Link: https://www.kaggle.com/datasets/shrutimechlearn/customer-data/data\n\n# Processing of Analysis\n1. Data Collection\nGather transactional data (e.g., invoices, purchase history).\n\n2. Data Preprocessing\nClean data, handle missing values, and format dates and customer IDs.\n\n3. Feature Engineering\nPerform RFM analysis (Recency, Frequency, Monetary) to extract customer behavior metrics.\n\n4. Normalization\nScale features to ensure equal weight in clustering algorithms.\n\n5. Clustering\nApply K-Means and Hierarchical Clustering to group similar customers.\n\n6. Cluster Evaluation\nUse Elbow Method and Dendrograms to determine optimal clusters.\n\n7. Visualization \u0026 Interpretation\nVisualize clusters and assign labels/scores based on business insights.\n\n# Conclusion\nBy applying unsupervised learning techniques like K-Means and Hierarchical Clustering, this project successfully segmented customers based on purchasing behavior. These clusters provide valuable insights into customer patterns, enabling businesses to personalize marketing strategies, improve customer engagement, and drive growth. Clustering proves to be a powerful, scalable tool for uncovering hidden patterns in customer data—turning raw transactions into strategic decisions.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdineshdhamodharan24%2Fcustomers_segmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdineshdhamodharan24%2Fcustomers_segmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdineshdhamodharan24%2Fcustomers_segmentation/lists"}