Comparison of machine learning algorithms in restaurant revenue prediction

Stepan Gogolev; Evgeniy M. Ozhegov

doi:10.1007/978-3-030-39575-9_4

Comparison of machine learning algorithms in restaurant revenue prediction

Stepan Gogolev^*, Evgeniy M. Ozhegov

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

18 Downloads (Pure)

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.

Original language	English
Title of host publication	Analysis of Images, Social Networks and Texts - 8th International Conference, AIST 2019, Revised Selected Papers
Editors	Wil M.P. van der Aalst, Vladimir Batagelj, Dmitry I. Ignatov, Valentina Kuskova, Sergei O. Kuznetsov, Irina A. Lomazova, Michael Khachay, Andrey Kutuzov, Natalia Loukachevitch, Amedeo Napoli, Panos M. Pardalos, Marcello Pelillo, Andrey V. Savchenko, Elena Tutubalina
Publisher	Springer, Cham
Pages	27-36
Number of pages	10
ISBN (Print)	9783030395742
DOIs	https://doi.org/10.1007/978-3-030-39575-9_4
Publication status	Published - 2020
Externally published	Yes
Event	8th International Conference on Analysis of Images, Social Networks and Texts - Kazan Federal University, Kazan, Russian Federation Duration: 17 Jul 2019 → 19 Jul 2019 Conference number: 8 https://2019.aistconf.org/

Publication series

Series	Communications in Computer and Information Science
Volume	1086CCIS
ISSN	1865-0929

Conference

Conference	8th International Conference on Analysis of Images, Social Networks and Texts
Abbreviated title	AIST 2019
Country/Territory	Russian Federation
City	Kazan
Period	17/07/19 → 19/07/19
Internet address	https://2019.aistconf.org/

Keywords

Machine learning
Revenue prediction
Weighted regression

Access to Document

10.1007/978-3-030-39575-9_4

Full TextFinal published version, 228 KBLicence: Taverne

Cite this

Gogolev, S., & Ozhegov, E. M. (2020). Comparison of machine learning algorithms in restaurant revenue prediction. In W. M. P. van der Aalst, V. Batagelj, D. I. Ignatov, V. Kuskova, S. O. Kuznetsov, I. A. Lomazova, M. Khachay, A. Kutuzov, N. Loukachevitch, A. Napoli, P. M. Pardalos, M. Pelillo, A. V. Savchenko, & E. Tutubalina (Eds.), Analysis of Images, Social Networks and Texts - 8th International Conference, AIST 2019, Revised Selected Papers (pp. 27-36). Springer, Cham. https://doi.org/10.1007/978-3-030-39575-9_4

Gogolev, Stepan ; Ozhegov, Evgeniy M. / Comparison of machine learning algorithms in restaurant revenue prediction. Analysis of Images, Social Networks and Texts - 8th International Conference, AIST 2019, Revised Selected Papers. editor / Wil M.P. van der Aalst ; Vladimir Batagelj ; Dmitry I. Ignatov ; Valentina Kuskova ; Sergei O. Kuznetsov ; Irina A. Lomazova ; Michael Khachay ; Andrey Kutuzov ; Natalia Loukachevitch ; Amedeo Napoli ; Panos M. Pardalos ; Marcello Pelillo ; Andrey V. Savchenko ; Elena Tutubalina. Springer, Cham, 2020. pp. 27-36 (Communications in Computer and Information Science, Vol. 1086CCIS).

@inproceedings{19cfed2537d6427c8a96c7998b45d39b,

title = "Comparison of machine learning algorithms in restaurant revenue prediction",

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.",

keywords = "Machine learning, Revenue prediction, Weighted regression",

author = "Stepan Gogolev and Ozhegov, {Evgeniy M.}",

note = "Funding Information: 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”. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 8th International Conference on Analysis of Images, Social Networks and Texts, AIST 2019 ; Conference date: 17-07-2019 Through 19-07-2019",

year = "2020",

doi = "10.1007/978-3-030-39575-9_4",

language = "English",

isbn = "9783030395742",

series = "Communications in Computer and Information Science",

publisher = "Springer, Cham",

pages = "27--36",

editor = "{van der Aalst}, {Wil M.P.} and Vladimir Batagelj and Ignatov, {Dmitry I.} and Valentina Kuskova and Kuznetsov, {Sergei O.} and Lomazova, {Irina A.} and Michael Khachay and Andrey Kutuzov and Natalia Loukachevitch and Amedeo Napoli and Pardalos, {Panos M.} and Marcello Pelillo and Savchenko, {Andrey V.} and Elena Tutubalina",

booktitle = "Analysis of Images, Social Networks and Texts - 8th International Conference, AIST 2019, Revised Selected Papers",

address = "Switzerland",

url = "https://2019.aistconf.org/",

}

Gogolev, S & Ozhegov, EM 2020, Comparison of machine learning algorithms in restaurant revenue prediction. in WMP van der Aalst, V Batagelj, DI Ignatov, V Kuskova, SO Kuznetsov, IA Lomazova, M Khachay, A Kutuzov, N Loukachevitch, A Napoli, PM Pardalos, M Pelillo, AV Savchenko & E Tutubalina (eds), Analysis of Images, Social Networks and Texts - 8th International Conference, AIST 2019, Revised Selected Papers. Springer, Cham, Communications in Computer and Information Science, vol. 1086CCIS, pp. 27-36, 8th International Conference on Analysis of Images, Social Networks and Texts, Kazan, Russian Federation, 17/07/19. https://doi.org/10.1007/978-3-030-39575-9_4

Comparison of machine learning algorithms in restaurant revenue prediction. / Gogolev, Stepan; Ozhegov, Evgeniy M.
Analysis of Images, Social Networks and Texts - 8th International Conference, AIST 2019, Revised Selected Papers. ed. / Wil M.P. van der Aalst; Vladimir Batagelj; Dmitry I. Ignatov; Valentina Kuskova; Sergei O. Kuznetsov; Irina A. Lomazova; Michael Khachay; Andrey Kutuzov; Natalia Loukachevitch; Amedeo Napoli; Panos M. Pardalos; Marcello Pelillo; Andrey V. Savchenko; Elena Tutubalina. Springer, Cham, 2020. p. 27-36 (Communications in Computer and Information Science, Vol. 1086CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

TY - GEN

T1 - Comparison of machine learning algorithms in restaurant revenue prediction

AU - Gogolev, Stepan

AU - Ozhegov, Evgeniy M.

N1 - Conference code: 8

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Machine learning

KW - Revenue prediction

KW - Weighted regression

UR - http://www.scopus.com/inward/record.url?scp=85090491928&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-39575-9_4

DO - 10.1007/978-3-030-39575-9_4

M3 - Conference article in proceeding

AN - SCOPUS:85090491928

SN - 9783030395742

T3 - Communications in Computer and Information Science

SP - 27

EP - 36

BT - Analysis of Images, Social Networks and Texts - 8th International Conference, AIST 2019, Revised Selected Papers

A2 - van der Aalst, Wil M.P.

A2 - Batagelj, Vladimir

A2 - Ignatov, Dmitry I.

A2 - Kuskova, Valentina

A2 - Kuznetsov, Sergei O.

A2 - Lomazova, Irina A.

A2 - Khachay, Michael

A2 - Kutuzov, Andrey

A2 - Loukachevitch, Natalia

A2 - Napoli, Amedeo

A2 - Pardalos, Panos M.

A2 - Pelillo, Marcello

A2 - Savchenko, Andrey V.

A2 - Tutubalina, Elena

PB - Springer, Cham

T2 - 8th International Conference on Analysis of Images, Social Networks and Texts

Y2 - 17 July 2019 through 19 July 2019

ER -

Gogolev S, Ozhegov EM. Comparison of machine learning algorithms in restaurant revenue prediction. In van der Aalst WMP, Batagelj V, Ignatov DI, Kuskova V, Kuznetsov SO, Lomazova IA, Khachay M, Kutuzov A, Loukachevitch N, Napoli A, Pardalos PM, Pelillo M, Savchenko AV, Tutubalina E, editors, Analysis of Images, Social Networks and Texts - 8th International Conference, AIST 2019, Revised Selected Papers. Springer, Cham. 2020. p. 27-36. (Communications in Computer and Information Science, Vol. 1086CCIS). doi: 10.1007/978-3-030-39575-9_4

Comparison of machine learning algorithms in restaurant revenue prediction

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Cite this