Research on Data Analysis of Efficient Innovation and Entrepreneurship Practice Teaching Based on LightGBM Classification Algorithm

Abstract

AbstractThe transformation of innovation and entrepreneurship practice teaching and education methods has put forward higher requirements for the intelligence and personalization of online education platforms. The aim of this study is to predict learning outcomes based on students' learning outcomes and habits, identify weak areas of knowledge, and provide targeted guidance and recommend the most suitable teaching resources. According to the concept of LightGBM model and the method of Feature selection, the research puts forward an integrated classification model ELO–LightGBM based on Elo Rating System (ELO) scoring system and Light Gradient Boosting Machine (LightGBM), trying to further mine the potential information of the practical teaching management data set. The model obtained a score of 0.7928 when using the dataset training, and a large number of comparative experiments were carried out between the ELO–LightGBM model and other classification models in different public datasets. The experimental results proved that the ELO–LightGBM model is more accurate than other classification models. In the comparative experiment on the practical teaching data set, the accuracy of the ELO–LightGBM model also surpassed the LightGBM model and the linear support vector machine model that performed well in small data sets, and the model was in the accuracy rate. The accuracy rate of winners in the comparison of micro-average is as high as 82.6%. It can be seen that the ELO–LightGBM model is of great significance to the intelligence and personalization of the online education platform.