Abstract: This project examines a tree-based machine learning approach to predict customer behavior outcomes in e-commerce, using a large dataset. The project compares different classification methods to solve three Customer Relationships Management problems: predicting customer satisfaction, churn modeling, and the next product to buy modeling. The analysis is fully automated, making it easy for small e-retailers to implement. The study employs decision tree, random forest, and gradient boosting techniques.
Keywords: machine learning, churn, decision tree, random forest, gradient boosting.
Çite:
Hind Khalid Alghamdi, Salma Mahjoub Omar, Hanaa Namankani, "Predicting the Customer Behaviour Utilizing Tree Based Machine Learning Algorithms", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 11, pp. 125-130, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121118.
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DOI:
10.17148/IJARCCE.2023.121118
