Titlebook: Artificial Intelligence and Machine Learning with R; Applications in the Bernd Heesen Textbook 2024 The Editor(s) (if applicable) and The

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https://doi.org/10.1007/978-3-658-45392-3R; AI (Artificial Intelligence); Machine Learning; R Environment; Data Science; Modeling; Predictions; Arti

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978-3-658-45391-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wies

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Bernard S. Matisoff P.E., CMfgEowledge to other data and situations after the learning phase has ended. Algorithms build a statistical model based on training data during machine learning. This means that the model does not simply remember the data with which it was trained, but it recognizes patterns and regularities in the trai

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Models for Strategic Forest Management the name of the function, in which parameters can possibly be passed to a function, e.g., ., to calculate the average for the variable "employee". In addition, data types, data structures, operations, functions, and control structures are presented.

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Andres Weintraub,Carlos Romero,Jaime Mirandaocessed for the subsequent analysis as part of a transformation. A data transformation includes, among other things, the selection of the data required for the analysis, validation, fusion with other data, reshaping, supplementation, and summarization. Once the data is cleaned, it is necessary to be

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Shared Fish Stocks and High Seas Issuesf various methods for regression, classification, and clustering. Furthermore, it explains how Unsupervised Learning helps in the creation of association rules (shopping basket analysis), which then serve as the basis for recommendation systems and the optimization of cross-selling procedures.

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Basics of the R Programming Language, the name of the function, in which parameters can possibly be passed to a function, e.g., ., to calculate the average for the variable "employee". In addition, data types, data structures, operations, functions, and control structures are presented.

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Application of Machine Learning with R,f various methods for regression, classification, and clustering. Furthermore, it explains how Unsupervised Learning helps in the creation of association rules (shopping basket analysis), which then serve as the basis for recommendation systems and the optimization of cross-selling procedures.