Prediction Model based on Internet News Buzzword Data

Abstract

The partial classification algorithm is mainly used to predict the popularity of network news and to explore the best model to predict the popularity of network news, so as to help network news service providers predict the popularity of news before publication. The popularity of network news is predicted according to the data analysis process: first, UCI data sets are pre-processed; secondly, feature selection is conducted for the data sets by using recursive feature elimination algorithm; then modelling and analysis is carried out, and finally through the confusion matrix, risk map and ROC (Receiver Operating Characteristic) chart performance evaluation, the performance of the model is compared and analyzed. Through comparison, it is found that random forest is the best prediction model.