Comparison of machine learning methods in predicting female labor force participation rates

The aim of this thesis is to determine which of the machine learning methods performs the best in predicting women's labour force participation rates and to determine which factors are the most significant in women's labour force participation. For this purpose, the second, third, fourth, fifth, sixth, and seventh waves of the World Values Survey which cover the period 1990-2022 are analyzed. Logistic Regression, Naïve Bayes, KNN (k-Nearest Neighbours), Decision Trees, SVM (Support Vector Machines), Random Forests, GBM (Gradient Boosting Machines), and XGBoost were used as machine learning algorithms. The methods used were compared based on performance metrics such as Accuracy, Precision, Recall, F1-Score, AUC, and ROC values. The results show that the best-performing methods are XGBoost and Random Forest. Along with these results, machine learning methods have shown that a woman's education level, age, number of children, and income level are the most important factors in her labour force participation decision, while a woman's patriarchal level and religiosity have limited effect on her labour force participation decision.