Machine Learning-XGBoost Analysis of Fertility Intension

This study is based on data from the World Values Survey (WVS 7) and employs the artificial intelligence algorithm model XGBoost to explore the roles of various factors influencing individual fertility intentions in cross-cultural contexts. The research survey included 87,507 adult participants, and the results revealed the following findings: With 90% of the data used as a training set, an algorithm model is established to predict the remaining 10% of the data, and the results show an accuracy of 98.56% in predictions. Furthermore, XGBoost can rank the importance of independent variables, allowing for more timely and effective prediction and adjustment of Fertility Intension based on important factors.