Prediction of National Innovation Using Data Scientific Approach

Countries have traditionally relied on various innovation measures to plan national-wise strategies and analysis, which hinders their ability to take proactive actions beforehand. This study introduces an advanced model for predicting national innovativeness using machine learning techniques. Utilizing a comprehensive dataset that includes 1,410 observation points and 20 variables from 141 countries spanning from 2011 to 2020, the data were sourced from the World Bank Open Data, Global Entrepreneurship Monitor (GEM) and Global Innovation Index (GII). The proposed model employs machine learning algorithms such as regression tree, random forest, support vector machine and extreme gradient boosting (XGBoost), and benchmarks their performance against the traditional linear regression analysis method. The findings reveal that all five machine learning models significantly outperform the traditional linear regression model in terms of correlation, root mean square error (RMSE) and mean absolute error (MAE), with XGBoost demonstrating superior performance. The XGBoost analysis highlights university and industry research collaboration, knowledge workers and logistics performance as the most critical variables influencing national innovativeness. This research not only presents a robust predictive model leveraging machine learning but also contributes to theoretical advancements by uncovering previously overlooked variables, offering new insights and practical implications for enhancing national innovation strategies.