چكيده به لاتين
In this research, a framework has been developed for identifying tax evasion using multiple variables with machine learning techniques. Three prediction models, namely Decision Tree, KNN, and Neural Network, have been used for this purpose. Based on the comparison of the models and the results of predictive validation, it can be concluded that factors other than those related to the tax system and tax rate (such as complexity of tax laws, risk and penalty reduction, financial opacity, lack of international cooperation, and underground economy) influence tax evasion. This is because in each model, including Decision Tree, KNN, and Neural Network, the percentage of predicting other activities is 96.47%, 80.67%, and 87.64% respectively. Considering the percentages of predictions, it is observed that the Decision Tree has the highest prediction rate. In other words, the Decision Tree performs best in prediction. Furthermore, after the Decision Tree, the tax system and tax rate have the best prediction with a percentage of 87.64%. On the other hand, the KNN model has the weakest performance among the three models, with a prediction rate of 80.67% for other factors. Based on the obtained results, it can be seen that KNN performs worse than other models, while the Decision Tree and Neural Network models are similar to each other overall. It should be noted that in terms of details and considering the validation of the two models, the Decision Tree model predicts the tax system and tax rate category better, while the Neural Network model predicts other factors better.
