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Improved Random Forest Based on Grid Search for Customer Satisfaction Prediction
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Abstract
Customer satisfaction is an important influencing factor that affects the survival and development of an enterprise, especially a service-oriented enterprise, then predicting customer satisfaction can achieve a continuous and steady improvement of customer satisfaction of an enterprise. In this paper, we improve the random forest algorithm based on grid search, and use the improved random forest model to predict airline customer satisfaction scores and rank the importance of customer satisfaction influencing factors by minimizing the average root mean square error of the model as the goal of the preferred parameters. The research results show that airlines should focus on Online boarding, In-flight Wi-Fi Service, Type of Travel and Class when improving customer satisfaction, which provides a reference for service-oriented companies to improve their economic efficiency.
Keywords
customer satisfaction, random forest, grid search
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Cite this article
Luo,R. (2023). Improved Random Forest Based on Grid Search for Customer Satisfaction Prediction. Advances in Economics, Management and Political Sciences,38,198-207.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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