Lottery user product participation prediction method, system and device, and storage medium

Abstract

The invention discloses a lottery user product participation prediction method, which comprises the following steps: obtaining original user data, extracting and converting the original user data, andloading the original user data into a database in a specified format according to classification; preprocessing the original user data stored in the database to obtain multi-dimensional user data; obtaining a prediction feature set related to a user product participation degree according to the multi-dimensional user data; inputting the prediction feature set into a pre-trained fusion predictionmodel to predict the user product participation degree, wherein the fusion prediction model is at least generated by fusion of a Bayesian classifier, a stochastic forest classifier and an iterative decision tree classifier. Correspondingly, the invention also discloses a lottery user product participation prediction system, a terminal device and a computer-readable storage medium. The technical proposal of the invention can reduce the prediction difficulty of the participation degree of the lottery user product and improve the prediction accuracy rate.

Description

technical field [0001] The present invention relates to the technical field of data mining, and in particular to a method, a system, a terminal device, and a computer-readable storage medium for predicting the product participation degree of lottery users. Background technique [0002] There are many types of products in the lottery industry, and the profit value of each product to the lottery company is different, that is, there are high and low rake ratios. Lottery companies hope to discover users' betting preferences and attract users to participate more in high-margin lottery products, thereby bringing more profits. Therefore, predicting the participation of lottery users in high-margin products is gradually gaining attention. [0003] According to the degree of user participation in high-margin products, users can be divided into two categories, namely, low-participation users and high-participation users. These two types of reasons are the changes in the total amount...

AI Technical Summary

Problems solved by technology

The learning process of the neural network is a process of continuously adjusting the weights through iterative operations. The goal of learning is to correctly label the input tuples through the adjustment of the weights. Compared with other commonly used data mining techniques, neural networks are used for classification Problem prediction has a good prediction ability, but the shortcomings cannot be ignored, such as the black box of the neural network itself, difficult to explain, and high requirements for computing power, etc., making prediction more difficult

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