Application research of machine learning in Missing Value Imputation – based on the empirical analysis of information technology and public services

Abstract:
Missing data is a growing concern in social science research. This paper introduces novel machine-learning methods to explore imputation efficiency and its effect on missing data. The authors used the Internet and public service data as the test examples. The empirical results show that the method not only verified the robustness of the positive impact of Internet penetration on the public service, but also further ensured that the machine-learning imputation method greatly improved the model’s explanatory power. The panel data after machine-learning imputation with better continuity in the time trend is feasibly analyzed, which can also be analyzed using the dynamic panel model. The long-term effects of the Internet on public services were found to be significantly stronger than the short-term effects. In short, in the era of big data, machine-learning methods can effectively improve the efficiency of data utilization and imputation accuracy, and further ensure the feasibility of the model in the actual study and the robustness of the regression results.