The world of IoT is becoming increasingly popular as it makes everyday tasks easier but can lead to large data sets. We need to process and analyze the obtained data, and this is where the field of machine learning can further extend the functionality of our systems. At the same time, IoT and machine learning are becoming part of industrial plants, where speed and quality of processing are primary considerations. The aim of this diploma thesis is to measure and analyze the electric current of an electric food mixer and predict the content of the device using machine learning algorithms. An electric mixer is merely a cheap and practical substitute for real industrial devices. We conducted several measurements of individual input ingredients of the mixer, which were subsequently used in four machine learning methods. The methods were then evaluated for their execution time and prediction accuracy. While we were often able to accurately predict the state of the device, we observed the greatest difficulty in predicting input ingredients that were most similar in density or viscosity.
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