Predicting food demand in food courts by decision tree approaches

Ahmet Selman Bozkir; Ebru Akcapinar Sezer

doi:10.1016/j.procs.2010.12.125

Predicting food demand in food courts by decision tree approaches

Research output: Contribution to journal › Conference article › peer-review

34 Citations (Scopus)

Abstract

Fluctuations and unpredictability in food demand generally cause problems in economic point of view in public food courts. In this study, to overcome this problem and predict actual consumption demand for a specified menu in a selected date, three decision tree methods (CART, CHAID and Microsoft Decision Trees) are utilized. A two year period dataset which is gathered from food courts of Hacettepe University in Turkey is used during the analyses. As a result, prediction accuracies up to 0.83 in R² are achieved. By this study, it's shown that decision tree methodology is suitable for food consumption prediction.

Original language	English
Pages (from-to)	759-763
Number of pages	5
Journal	Procedia Computer Science
Volume	3
DOIs	https://doi.org/10.1016/j.procs.2010.12.125
Publication status	Published - 2011
Event	1st World Conference on Information Technology, WCIT-2010 - Istanbul, Turkey Duration: 6 Oct 2010 → 10 Oct 2010

Keywords

CART
CHAID
Decision Trees
Food demand
Microsoft Decision Trees

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10.1016/j.procs.2010.12.125

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title = "Predicting food demand in food courts by decision tree approaches",

abstract = "Fluctuations and unpredictability in food demand generally cause problems in economic point of view in public food courts. In this study, to overcome this problem and predict actual consumption demand for a specified menu in a selected date, three decision tree methods (CART, CHAID and Microsoft Decision Trees) are utilized. A two year period dataset which is gathered from food courts of Hacettepe University in Turkey is used during the analyses. As a result, prediction accuracies up to 0.83 in R2 are achieved. By this study, it's shown that decision tree methodology is suitable for food consumption prediction.",

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AU - Bozkir, Ahmet Selman

AU - Sezer, Ebru Akcapinar

PY - 2011

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N2 - Fluctuations and unpredictability in food demand generally cause problems in economic point of view in public food courts. In this study, to overcome this problem and predict actual consumption demand for a specified menu in a selected date, three decision tree methods (CART, CHAID and Microsoft Decision Trees) are utilized. A two year period dataset which is gathered from food courts of Hacettepe University in Turkey is used during the analyses. As a result, prediction accuracies up to 0.83 in R2 are achieved. By this study, it's shown that decision tree methodology is suitable for food consumption prediction.

AB - Fluctuations and unpredictability in food demand generally cause problems in economic point of view in public food courts. In this study, to overcome this problem and predict actual consumption demand for a specified menu in a selected date, three decision tree methods (CART, CHAID and Microsoft Decision Trees) are utilized. A two year period dataset which is gathered from food courts of Hacettepe University in Turkey is used during the analyses. As a result, prediction accuracies up to 0.83 in R2 are achieved. By this study, it's shown that decision tree methodology is suitable for food consumption prediction.

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KW - CHAID

KW - Decision Trees

KW - Food demand

KW - Microsoft Decision Trees

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Y2 - 6 October 2010 through 10 October 2010

ER -

Predicting food demand in food courts by decision tree approaches

Abstract

Keywords

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