Research on the Prediction Model of Automobile Brand Preference Based on Big Data Analysis and Cloud Computing

This project intends to build a set of automobile brand preference prediction methods that integrate cloud computing and big data to achieve effective processing of a large amount of user behavior data and accurate prediction of brand preferences. We need to use big data to deeply mine users' shopping habits and preference characteristics to achieve efficient processing of massive data. This method is based on reinforcement learning, and gradually improves the accuracy and robustness of predictions in repeated iterations. Then this paper proposes a method based on reinforcement learning, which can dynamically adjust decisions through interaction with the outside world, thereby improving the accuracy of predicting customers' brand preferences. In addition, this project will also use massive historical data to analyze the brand selection behavior and development trends of various types of consumer groups from multiple perspectives. Finally, a simulation test is conducted on the constructed model to verify its correctness. The experiment proves that this method can effectively predict brand preferences in different scenarios, with an accuracy of more than 85%. This study shows that the model of this study can effectively and robustly analyze consumer behavior data to predict brand preferences.