Comparison of decision tree algorithms in predicting consumer confidence index Abstract The economic conditions and future expectations of individuals in a country can influence their spending and/or saving behaviors. The reflections of these behavioral patterns on the economy can be measured through the consumer confidence index. The aim of this study is to determine the most suitable algorithm for predicting the consumer confidence index by comparing various decision tree algorithms. Independent variables such as unemployment rate, BIST100 index, housing price index, exchange rate, and consumer price index, which are thought to impact the consumer confidence index, were used in the study. In the analyses, monthly data for the period of 01.2014-08.2024 were used, and 70% of the data was separated for training and 30% for testing. Decision tree-based algorithms, including Random Forest, XGBoost, LightGBM, and CatBoost, were applied to these data to develop predictive models. MSE, RMSE, MAE and MAPE error criteria were used to evaluate the performance of the algorithms. Based on the MSE and RMSE results, the RF algorithm, and based on the MAE and MAPE results, the XGBoost algorithm have both been determined as suitable decision tree algorithms for CCI prediction. Keywords: Consumer confidence index, CatBoost, LightGBM, Random forest, XGBoost.