Tareq, Wadhah Zeyad TareqDavud, Muhammed2025-04-182025-04-182024Tareq, W. Z. T., & Davud, M. (2024). Classification and clustering. In Decision-Making Models (pp. 351-359). Academic Press.978-044316147-6, 978-044316148-3https://hdl.handle.net/20.500.12713/6567Data analysis is the process of understanding or extracting patterns from raw data. One of the widely used methods in data analysis is machine learning. Machine learning is a system or model that can learn from raw data to make decisions without human intervention. Classification and clustering are the most popular machine learning technologies for the analysis of data. Each technology involves many algorithms that aim to categorize objects into classes depending on the object's features. In this chapter, we introduce a guide to both classification and clustering technology by applying different algorithms to different datasets. The classification dataset differs from the clustering dataset. The reason here is to explain the suitable type of data for each technology. For clustering, the k-means clustering algorithm is applied. For classification, the decision tree algorithm is applied. The results showed the efficiency of different machine learning algorithms for data analysis and decision-making. © 2024 Elsevier Inc. All rights reserved.eninfo:eu-repo/semantics/closedAccessBook Chapter3513592-s2.0-8520290442010.1016/B978-0-443-16147-6.00024-4N/A