Floor Heating Customer Prediction Model Based on Random Forest

As an important branch, floor heating service brings a lot of economic benefits to gas companies every year. With the aggravation of market-oriented competition, the gas companies are actively seeking service transformation. It is of great significance to gas companies to be able to forecast those customers willing to use floor heating. In this paper, we establish a floor heating customer prediction model that helps indicate the potential customers using floor heating, based on analyzing existing floor heating customers’ behavior. The prediction model uses random forest. We exploit data coming from the actual running of a Shanghai based gas company. Experiments show that the random forest model has better performance than those using KNN (k-nearest neighbor) or logistic regression.