KEYWORDS: Data mining, Analytical research, Data modeling, Mining, Process modeling, Power supplies, Library classification systems, Data storage, Switches, Solids
The traditional early warning method of power user complaints, the complaint classification management model is not perfect, and the prediction accuracy rate is low. For this reason, an early warning method for power user complaints is designed that integrates GBDT and Logistic. This paper obtains grid customer behavior preference data, focusing on mining potential customers. On this basis, customer satisfaction characteristics are extracted, which promotes customer demand feedback. This paper divides power customers into different categories and builds a complaint classification management model. This paper uses a decision tree as the basic learner, using GBDT and Logistic to establish a complaint early warning mode. The experimental results show that the average prediction accuracy of this method is 83.367% in the 5dB noise environment. The average prediction accuracy of this method is 53.602% in the 10dB noise environment. The prediction accuracy of this method is higher than the two compared methods, which shows that the method in this paper can achieve the purpose of improving the prediction accuracy.
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