Analysis of implementation for advanced tools in sales prediction

As a matter of fact, since the 20th century, companies have started using statistical methods to predict the future sales of their product in order to adjust sales strategy as well as optimize the unitality of the corporations. In recent years, with the help of machine learning thanks to the rapid development of computation ability, firms could predict the future sales of their products with a much higher precision based on the state-of-art scenarios and models. To be specific, machine learning models such as Decision Trees help firms predict the trend of their sales with high efficiency. On this basis and with this in mind, this study will investigate the implementation and application of advanced tools in sales prediction. With the appearance of neuron networks such as LSTM-RNN, firms are now able to predict with high accuracy. According to the analysis, this study concludes the result of research carried out on Decision trees, GBDTs, as well as RNNs. At the same time, the current limitations and prospects are demonstrated and illustrated.