Abstract
A Machine Learning Based Method for Customer Behavior Prediction
Highlights
With the arrival of the area of "Internet plus" and the explosive growth of all kinds of data, the age of big data has come
The results show that the prediction effect of decision tree is better than clustering analysis and Naive Bayesian algorithm, and has a higher promotion degree
Data mining technology is applied to the target user prediction analysis of purchasing scooter
Summary
With the arrival of the area of "Internet plus" and the explosive growth of all kinds of data, the age of big data has come. Liu Weixiao [2] proposed a kind of hybrid intelligent prediction algorithms combining artificial neural network (ANN) and discrete grey prediction model (DGM(1,1)) He obtained influencing variables with high correlation degree by correlation degree analysis. Guo et al [12] used the data mining method based on ant colony algorithm to optimize the logistics distribution path, and verified the effectiveness of the algorithm, which provided the basis for decision analysis and data processing. The paper uses decision trees, cluster analysis and Bayesian algorithms to deeply analyze user behavior characteristics, explores the commonalities and characteristics of the attributes of these customers, and finds the algorithm model with higher degree of promotion, which is conducive to improving sales performance and company efficiency, so as to improve the scientific and effective decision-making of market departments
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