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This document discusses the applications of supervised learning algorithms in chemical engineering, focusing on techniques such as Linear Regression, Logistic Regression, and k-Nearest Neighbors (k-NN). It outlines the basic concepts, advantages, disadvantages, and appropriate use cases for each algorithm, emphasizing their relevance in predictive modeling, process optimization, and quality control within the chemical industry. The lecture is part of a series presented by faculty members at Cairo University

Author

andrewnader56

Language

EN