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@article{169102,
        author = {Jaydeep Bohra and Ved bansal},
        title = {A Comparative Analysis of Machine Learning Algorithms for Customer Segmentation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {720-722},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169102},
        abstract = {This study investigates the effectiveness of various machine learning algorithms in the context of customer segmentation, a crucial aspect of Customer Relationship Management (CRM). With the increasing complexity of consumer behaviour and preferences, traditional segmentation methods are becoming less effective. We focus on four widely used algorithms: Logistic Regression, Decision Trees, Random Forests, and AdaBoost. By applying these algorithms to a retail customer dataset, we evaluate their performance based on accuracy, precision, and other relevant metrics. Our findings indicate that AdaBoost outperforms the other algorithms in terms of accuracy, while Logistic Regression demonstrates strong performance in scenarios with less complexity. This paper discusses the strengths and limitations of each algorithm, providing insights for researchers and practitioners in the field of customer analytics and marketing strategy.},
        keywords = {},
        month = {November},
        }