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Machine Learning Methods for Revenue Prediction in Google Merchandise Store

In: Smart Service Systems, Operations Management, and Analytics

Author

Listed:
  • Vahid Azizi

    (Iowa State University)

  • Guiping Hu

    (Iowa State University)

Abstract

Machine learning has gained increasing interests from various application domains for its ability to understand data and make predictions. In this paper, we apply machine learning techniques to predict revenue per customer for Google Merchandise Store. Exploratory Data Analysis (EDA) was conducted for the customer dataset and feature engineeringFeature engineering was applied to the find best subset of features. Four machine learning methods, Gradient Boosting MachineGradient Boosting Machine (GBM) (GBM), Extreme Gradient Boosting (XGBoost), Categorical BoostingCategorical Boosting (CatBoost) (CatBoost), and Light Gradient Boosting MachineLight Gradient Boosting Machine (LightGBM) (LightGBM) have been applied to predict revenue per customer. Results show that LightGBM outperforms other methods in terms of RMSE and running time.

Suggested Citation

  • Vahid Azizi & Guiping Hu, 2020. "Machine Learning Methods for Revenue Prediction in Google Merchandise Store," Springer Proceedings in Business and Economics, in: Hui Yang & Robin Qiu & Weiwei Chen (ed.), Smart Service Systems, Operations Management, and Analytics, pages 65-75, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-30967-1_7
    DOI: 10.1007/978-3-030-30967-1_7
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    Cited by:

    1. Tayfun Uyanık & Nur Najihah Abu Bakar & Özcan Kalenderli & Yasin Arslanoğlu & Josep M. Guerrero & Abderezak Lashab, 2023. "A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study," Energies, MDPI, vol. 16(13), pages 1-20, June.

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