A Predictive Data Analytics Methodology for Online Food Delivery

Mariam Al Akasheh; Nehal Eleyan; Gurdal Ertek

doi:10.1109/SNAMS58071.2022.10062613

A Predictive Data Analytics Methodology for Online Food Delivery

Mariam Al Akasheh, Nehal Eleyan, Gurdal Ertek

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Online food delivery (OFD) has become a popular and profitable e-business category due to the rising demand for online food delivery. People are increasingly ordering food online, especially in urban areas and on college campuses. Using data from online food delivery services, one can analyze and predict the values of key performance indicators (KPIs). In the study presented in this paper, we developed a systematic methodology to analyze and predict such KPIs using various classification and regression algorithms. We found that, for the case study we analyzed, Random Forest (RF) consistently ranked as the best algorithm for regression and classification in predicting most of the KPIs. The methodology we introduce and illustrate in the paper can be adapted and extended to similar problems to reveal potential operational issues and identify the possible root causes of such problems.

Original language	English
Title of host publication	2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022
Editors	Paolo Ceravolo, Christian Guetl, Yaser Jararweh, Elhadj Benkhelifa
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350320480
DOIs	https://doi.org/10.1109/SNAMS58071.2022.10062613
Publication status	Published - 2022
Externally published	Yes
Event	9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022 - Milan, Italy Duration: Nov 28 2022 → Dec 1 2022

Publication series

Name	2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022

Conference

Conference	9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022
Country/Territory	Italy
City	Milan
Period	11/28/22 → 12/1/22

Keywords

data analytics
data science pipeline
food delivery services
machine learning
online food delivery

ASJC Scopus subject areas

Media Technology
Computer Networks and Communications
Information Systems and Management
Safety, Risk, Reliability and Quality
Education
Communication

Access to Document

10.1109/SNAMS58071.2022.10062613

Cite this

Al Akasheh, M., Eleyan, N., & Ertek, G. (2022). A Predictive Data Analytics Methodology for Online Food Delivery. In P. Ceravolo, C. Guetl, Y. Jararweh, & E. Benkhelifa (Eds.), 2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022 (2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SNAMS58071.2022.10062613

A Predictive Data Analytics Methodology for Online Food Delivery. / Al Akasheh, Mariam; Eleyan, Nehal; Ertek, Gurdal.
2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022. ed. / Paolo Ceravolo; Christian Guetl; Yaser Jararweh; Elhadj Benkhelifa. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Al Akasheh, M, Eleyan, N & Ertek, G 2022, A Predictive Data Analytics Methodology for Online Food Delivery. in P Ceravolo, C Guetl, Y Jararweh & E Benkhelifa (eds), 2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022. 2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022, Institute of Electrical and Electronics Engineers Inc., 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022, Milan, Italy, 11/28/22. https://doi.org/10.1109/SNAMS58071.2022.10062613

Al Akasheh M, Eleyan N, Ertek G. A Predictive Data Analytics Methodology for Online Food Delivery. In Ceravolo P, Guetl C, Jararweh Y, Benkhelifa E, editors, 2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022). doi: 10.1109/SNAMS58071.2022.10062613

Al Akasheh, Mariam ; Eleyan, Nehal ; Ertek, Gurdal. / A Predictive Data Analytics Methodology for Online Food Delivery. 2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022. editor / Paolo Ceravolo ; Christian Guetl ; Yaser Jararweh ; Elhadj Benkhelifa. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022).

@inproceedings{854dca2c2e88447e8639777c7623ad71,

title = "A Predictive Data Analytics Methodology for Online Food Delivery",

abstract = "Online food delivery (OFD) has become a popular and profitable e-business category due to the rising demand for online food delivery. People are increasingly ordering food online, especially in urban areas and on college campuses. Using data from online food delivery services, one can analyze and predict the values of key performance indicators (KPIs). In the study presented in this paper, we developed a systematic methodology to analyze and predict such KPIs using various classification and regression algorithms. We found that, for the case study we analyzed, Random Forest (RF) consistently ranked as the best algorithm for regression and classification in predicting most of the KPIs. The methodology we introduce and illustrate in the paper can be adapted and extended to similar problems to reveal potential operational issues and identify the possible root causes of such problems.",

keywords = "data analytics, data science pipeline, food delivery services, machine learning, online food delivery",

author = "{Al Akasheh}, Mariam and Nehal Eleyan and Gurdal Ertek",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022 ; Conference date: 28-11-2022 Through 01-12-2022",

year = "2022",

doi = "10.1109/SNAMS58071.2022.10062613",

language = "English",

series = "2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Paolo Ceravolo and Christian Guetl and Yaser Jararweh and Elhadj Benkhelifa",

booktitle = "2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022",

}

TY - GEN

T1 - A Predictive Data Analytics Methodology for Online Food Delivery

AU - Al Akasheh, Mariam

AU - Eleyan, Nehal

AU - Ertek, Gurdal

PY - 2022

Y1 - 2022

N2 - Online food delivery (OFD) has become a popular and profitable e-business category due to the rising demand for online food delivery. People are increasingly ordering food online, especially in urban areas and on college campuses. Using data from online food delivery services, one can analyze and predict the values of key performance indicators (KPIs). In the study presented in this paper, we developed a systematic methodology to analyze and predict such KPIs using various classification and regression algorithms. We found that, for the case study we analyzed, Random Forest (RF) consistently ranked as the best algorithm for regression and classification in predicting most of the KPIs. The methodology we introduce and illustrate in the paper can be adapted and extended to similar problems to reveal potential operational issues and identify the possible root causes of such problems.

AB - Online food delivery (OFD) has become a popular and profitable e-business category due to the rising demand for online food delivery. People are increasingly ordering food online, especially in urban areas and on college campuses. Using data from online food delivery services, one can analyze and predict the values of key performance indicators (KPIs). In the study presented in this paper, we developed a systematic methodology to analyze and predict such KPIs using various classification and regression algorithms. We found that, for the case study we analyzed, Random Forest (RF) consistently ranked as the best algorithm for regression and classification in predicting most of the KPIs. The methodology we introduce and illustrate in the paper can be adapted and extended to similar problems to reveal potential operational issues and identify the possible root causes of such problems.

KW - data analytics

KW - data science pipeline

KW - food delivery services

KW - machine learning

KW - online food delivery

UR - http://www.scopus.com/inward/record.url?scp=85158918885&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85158918885&partnerID=8YFLogxK

U2 - 10.1109/SNAMS58071.2022.10062613

DO - 10.1109/SNAMS58071.2022.10062613

M3 - Conference contribution

AN - SCOPUS:85158918885

T3 - 2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022

BT - 2022 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022

A2 - Ceravolo, Paolo

A2 - Guetl, Christian

A2 - Jararweh, Yaser

A2 - Benkhelifa, Elhadj

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022

Y2 - 28 November 2022 through 1 December 2022

ER -

A Predictive Data Analytics Methodology for Online Food Delivery

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this