A prosumer is a person who plays the role of a consumer and a producer simultaneously. He shares his knowledge and experience from using a product, gives feedback, and helps to improve products and services. Such an attitude is very desired by the producers as they benefit from it. The most common way to get feedback is to conduct a survey. Sometimes, it is hard to convince clients to fill out the questionnaire. Machine learning and deep learning techniques give opportunities to predict survey responses based on a sample of real survey results. The aim of the research was to determine the possibility of classifying prosumer survey respondents depending on their method of assessing each decision-making factor separately using selected machine learning and deep learning methods. Decision trees, LightGBM, and neural network models were used. The input data were the respondents’ characteristics and the output data was the importance of four factors in the prosumption decision-making process. The accuracy of built models was the highest for prosumption destimulants using DNN, for information contribution using Decision trees, for communication channels using DNN and for stimulants using LightGBM. The presented research shows that there is a possibility of predicting the importance of decision-making factors based on respondents’ characteristics. It may be especially important for smaller companies that do not have much money or time to take large market surveys.