An Intelligent Approach Using Machine Learning Techniques to Predict Flow in People Pegalajar Jiménez, María Del Carmen Baca Ruiz, Luis Gonzaga Machine learning Artificial neural networks Flow Psychology Data Mining The goal of this study is to estimate the state of consciousness known as Flow, which is associated with an optimal experience and can indicate a person’s efficiency in both personal and professional settings. To predict Flow, we employ artificial intelligence techniques using a set of variables not directly connected with its construct. We analyse a significant amount of data from psychological tests that measure various personality traits. Data mining techniques support conclusions drawn from the psychological study. We apply linear regression, regression tree, random forest, support vector machine, and artificial neural networks. The results show that the multilayer perceptron network is the best estimator, with an MSE of 0.007122 and an accuracy of 88.58%. Our approach offers a novel perspective on the relationship between personality and the state of consciousness known as Flow. 2023-07-26T08:33:45Z 2023-07-26T08:33:45Z 2023-04-04 journal article Pegalajar, M.C.; Ruiz, L.G.B.; Pérez-Moreiras, E.; Boada-Grau, J.; Serrano-Fernandez, M.J. An Intelligent Approach Using Machine Learning Techniques to Predict Flow in People. Big Data Cogn. Comput. 2023, 7, 67. [https://doi.org/10.3390/ bdcc7020067] https://hdl.handle.net/10481/84006 10.3390/bdcc7020067 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI