Comparative analysis of decision tree algorithms: Random forest and C4.5 for airlines customer satisfaction classification

Abstract

Abstract This article aims to a comparative analysis of decision tree algorithms between random forest and C4.5 for airlines customer satisfaction classification. The comparative study predicts both algorithms have better accuracy, precision, recall AUC (area under the curve) for analyzing data set of customer satisfaction on airlines, which are useful for later if have some same kind set of data set and problem. In this particular comparative analysis, first, need to select the dataset and transform so it can be used for data mining technique classification after choosing the algorithm to analyze the data set. The analyzing of the dataset it will go through validation, testing, and also result for each algorithm used. Then will compare the result from each algorithm, to determine which algorithm are best to use in this particular dataset or problem for customer satisfaction for airlines. The results of the comparative analysis are the best alternative algorithm choice for use in airline customer satisfaction classifications. For this comparison, the Random forest algorithm has a better result than the C4.5 algorithms.