Improved C4.5 Algorithm for the Analysis of Sales

A decision tree is an important means of data mining and inductive learning, which is usually used to form classifiers and prediction models. C4.5 is one of the most classic classification algorithms on data mining, but when it is used in mass calculations, the efficiency is very low. In this paper, the rule of C4.5 is improved by the use of L'Hospital Rule, which simplifies the calculation process and improves the efficiency of decision-making algorithm. When calculating the rate of information gain, the similar principle is used, which improves the algorithm a lot. And the application at the end of the paper shows that the improved algorithm is efficient, which is more suitable for the application of large amounts of data, and its efficiency has been greatly improved in line with the practical application.