在线食品配送的预测数据分析方法

2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS) Pub Date : 2022-11-29 DOI:10.1109/SNAMS58071.2022.10062613

Mariam Al Akasheh, Nehal Eleyan, Gürdal Ertek

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引用次数: 0

摘要

由于对在线食品配送的需求不断增长，在线食品配送(OFD)已经成为一个受欢迎和有利可图的电子商务类别。人们越来越多地在网上订餐，尤其是在城市地区和大学校园。利用在线送餐服务的数据，可以分析和预测关键绩效指标(kpi)的值。在本文提出的研究中，我们开发了一种系统的方法来使用各种分类和回归算法来分析和预测这些kpi。我们发现，对于我们分析的案例研究，随机森林(RF)在预测大多数kpi时始终被评为回归和分类的最佳算法。我们在本文中介绍和说明的方法可以适用并扩展到类似的问题，以揭示潜在的操作问题并确定此类问题的可能根源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Predictive Data Analytics Methodology for Online Food Delivery

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.

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来源期刊

2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)

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