Income Level Estimation with Light-GBM: Understanding Model Decisions with Explainable AI Techniques Shap and Lime

This study examines the use of machine learning and artificial intelligence algorithms to predictindividuals’ annual incomes. In analyses conducted using the Python programming language, the bestperformance was achieved in models utilizing the "Synthetic Minority Over-sampling Technique (SMOTE)" forimbalanced data sets, with an accuracy of 87.45%, precision of 85.74%, recall of 89.31%, and an F1 score of87.30, using the "Light Gradient Boosting Machines" algorithm. Additionally, the impact of parameters andvariables on income prediction was examined using interpretable artificial intelligence algorithms. The resultsof the study emphasize the importance of employing effective methods and explaining machine learning modelpredictions, as well as addressing imbalanced data sets