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Devices in the Internet of Things (IoT) generate and gather a vast amount of data. Smart devices with constrained resources cannot execute large data‐based machine learning (ML) algorithms. Therefore, in this paper, we propose, analyze, and evaluate data reduction at the Fog level. As a matter of fact, the focus of this paper is twofold: The first objective is to process the reduced datasets by ML, and the second aim is to segregate the irrelevant data and preserve the quality of the ML models. The naïve Bayesian classifier is used for model analysis. For data (attributes) reduction, the state‐of‐the‐art approaches of CF Subset Evaluation, Info Gain Evaluation, Gain Ratio Attribute Evaluation, and the principal component analysis (PCA) approaches are employed. From the implementation point of view, the naïve Bayesian classifier is used to learn the class distribution and the correlation of the classes with the rest of …