Machine learning system and medium for calculating passenger values of airline

摘要

A machine learning system for calculating customer values is disclosed. A feature database is preset with feature algorithms in one-to-one correspondence with feature parameters forming a parameter set 101. Historical customer information is combined with the feature database to generate two data sets: a training set and a test set 102. The training set is input into the XGBoost algorithm engine to generate a reference model 103, which is cross validated with the test set to generate a value assessment model 104. Customer information is input into the value assessment model to generate customer value scores 105. The customers may be airline passengers, and the customer values may comprise passenger value scores. The system may employ a passenger value table comprising passenger value sections and estimated passenger values, enabling passenger value scores to be associated with estimated passenger values. Potential passenger values may be judged against a potential value threshold to determine if a passenger can be classified into a high-end passenger database. The feature parameters may comprise information relating to a customer’s travels, bookings, bad experiences, social influence, social age, interests, or membership level. A model hyper-parameter database and error index may be used to iteratively train the model.