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Product Fraud Judgment Based on Random Forest Model

With the rise of IoT technology, more and more companies are using this technology for daily work production. The technology will generate a lot of data in the process of application. If the data can be used reasonably, it will help the company make better decisions. This paper is based on the random forest model to realize the judgment of fraudulent product behavior. Due to the large amount of data given by the material, we first perform feature engineering on the data to obtain processed data that can be used for modeling, and then use the random forest algorithm for data classification and regression. Experiments show that the accuracy of the random forest model is 98. 48. Compared with the logistic regression model and the naive Bayes model, it has better data classification and regression capabilities.