Machine Learning Methods for Revenue Prediction in Google Merchandise Store

Vahid Azizi and Guiping Hu ()
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Vahid Azizi: Iowa State University
Guiping Hu: Iowa State University

A chapter in Smart Service Systems, Operations Management, and Analytics, 2020, pp 65-75 from Springer

Abstract: 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.

Keywords: Feature engineering; GBM; XGBoost; CatBoost; LightGBM (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-030-30967-1_7

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DOI: 10.1007/978-3-030-30967-1_7

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