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Comparison of Machine Learning Algorithms in Restaurant Revenue Prediction

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Analysis of Images, Social Networks and Texts (AIST 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1086))

Abstract

In this paper, we address several aspects of applying classical machine learning algorithms to a regression problem. We compare the predictive power to validate our approach on a data about revenue of a large Russian restaurant chain. We pay special attention to solve two problems: data heterogeneity and a high number of correlated features. We describe methods for considering heterogeneity—observations weighting and estimating models on subsamples. We define a weighting function via Mahalanobis distance in the space of features and show its predictive properties on following methods: ordinary least squares regression, elastic net, support vector regression, and random forest.

The article was prepared within the framework of the Academic Fund Program at the National Research University Higher School of Economics (HSE University) in 2019–2020 (grant #19-04-048) and within the framework of the Russian Academic Excellence Project “5–100”.

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Notes

  1. 1.

    Data is available at goo-gl.ru/5vIE.

  2. 2.

    Data are taken from gks.ru.

  3. 3.

    Data taken from 2GIS.

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Author information

Authors and Affiliations

  1. Research Group for Applied Markets and Enterprises Studies, National Research University Higher School of Economics, Perm, Russia

    Stepan Gogolev & Evgeniy M. Ozhegov

Authors
  1. Stepan Gogolev
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  2. Evgeniy M. Ozhegov
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Corresponding author

Correspondence to Stepan Gogolev .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  4. Institute of Mathematics and Mechanics Yekaterinburg, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, Moscow, Russia

    Valentina Kuskova

  6. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  9. Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. Loria, Vandoeuvre lès Nancy, France

    Amedeo Napoli

  11. University of Florida, Gainesville, USA

    Panos M. Pardalos

  12. Ca' Foscari University of Venice, Venezia Mestre, Italy

    Marcello Pelillo

  13. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  14. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Gogolev, S., Ozhegov, E.M. (2020). Comparison of Machine Learning Algorithms in Restaurant Revenue Prediction. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Communications in Computer and Information Science, vol 1086. Springer, Cham. https://doi.org/10.1007/978-3-030-39575-9_4

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  • DOI: https://doi.org/10.1007/978-3-030-39575-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39574-2

  • Online ISBN: 978-3-030-39575-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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