=Paper=
{{Paper
|id=Vol-3688/paper17
|storemode=property
|title=Forecasting Demand for Food Delivery Services
|pdfUrl=https://ceur-ws.org/Vol-3688/paper17.pdf
|volume=Vol-3688
|authors=Yurii Kryvenchuk,Nazarii Hryhorash
|dblpUrl=https://dblp.org/rec/conf/colins/KryvenchukH24
}}
==Forecasting Demand for Food Delivery Services==
https://ceur-ws.org/Vol-3688/paper17.pdf
Forecasting Demand for Food Delivery Services
Abstract
Keywords
1
1. Introduction
2. Related Works
COLINS-2024: 8th International Conference on Computational Linguistics and Intelligent Systems, April 12–13, 2024,
Lviv, Ukraine
yurii.p.kryvenchuk@lpnu.ua (Yu. Kryvenchuk); nazarii.hryhorash.knm.2020@lpnu.ua (N. Hryhorash);
0000-0002-2504-5833 (Yu. Kryvenchuk); 0009-0005-7162-1490 (N. Htyhorash)
© 2024 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
3. Methods analysis
3.1. Linear regression
3.2. LASSO
3.3. Gradient Boosting
3.4. Decision tree
3.5. Random forest
4. Dataset
Table 1
Table train.csv
Field Meaning
Id A unique identifier
Week Number of the week
Center_id Unique identifier of the
delivery centr
Meal-id Unique identifier for food
Checkout_price The final price includes
discount, taxes and delivery
costs
Base_price Base price of the meal
Emailer_for_promotion Whether the email discount
was used
Hamepage_featured Whether the food is available
on the homepage
Num_orders Number of orders
Figure 1: Graphics of delivery centres by the number of orders
Table 2
Table fulfilment_center_info.csv
Field Meaning
Center_id Unique identifier of the
delivery centr
City_code Unique city identifier
Region_code Unique region indexer
Center_type Type of delivery centr
Op_area The scope of the delivery centr
Table 3
Table meal_info.csv
Field Meaning
Meal_id A unique indexer for food
Category category
Cuisine Kitchen types
Figure 2: Graphics of delivery centres by number of orders and type
Now I will determine the number of delivery centers for each type see Figure 3.
Figure 3: Graphics of delivery centres by number and type of centres
Figure 4: Chart of the ratio of discount to the number of orders
Figure 5: Diagram of the ratio of the number of orders to the kitchen
Analysis of the results
Figure 6: MSE results
Figure 7: R-squared results
References
[1] M.I. Dziamulych, T.O. Shmatkovska, Influence of modern information systems and
technologies on the formation of the digital economy, Economic Forum (2022): 3-8.
[2] N.Y. Kirlik, Artificial intelligence and its use in logistics processes, Actual Problems of the
Economy (2021): 243-244.
[3] Y. Chalyuk, Determinants of digitalisation of the economy and society, Intellect XXI (2020):
138-143.
[4] Y. Chaliuk, Scenarios of socio-economic development of the EU after BREXIT and COVID,
Scientific Notes of Vernadsky TSU, Series: Economics and Management, 31(70) (2020): 25-
32.
[5] Y. Chalyuk, Digital competitiveness of countries, Market Infrastructure 50, (2020): 23-30.
[6] T.O. Shmatkovska, M.I. Dzyamulych, Modern information and communication technologies
in professional activity in the system of new trends in the digitalisation of the economy,
Economic Sciences18 (2021): 248-255.
[7] T.O. Shmatkovska, O.V. Stashchuk, Big data and business modelling of economic systems,
Effective Economy 5 (2021): 125-133.
[8] M. Dziamulych, I. Sadovska, The study of the relationship between rural population spending
on peasant households with the main socio-economic indicators: a case study of Volyn
region, Ukraine, Management, Economic Engineering in Agriculture and Rural Development,
volume 20, 2020.
[9] O. Stashchuk, T. Shmatkovska, Model for efficiency evaluation of financial security
management of joint stock companies operating in the agricultural sector: a case study of
Ukraine, Management, Economic Engineering in Agriculture and Rural Development (2021):
715–728.
[10] Q. Abdulqader, Applying the Binary Logistic Regression Analysis on The Medical Data,
Science Journal of University of Zakho (2017): 330–334.
[11] N. Altman, M. Krzywinski, Simple linear regression, Nat Methods (2015): 999–1000.
[12] S. Dreiseitl, L. Ohno-Machado, Logistic regression and artificial neural network classification
models: a methodology review, Journal of Biomedical Informatics 35 (2002): 352-359.
[13] Y. Amit, D. Geman, Shape quantization and recognition with randomized trees, Neural
Comput (2019): 1545–1588.
[14] B. Lanz, Machine Learning in R: Expert Techniques for Predictive Analysis, Peter, 2020.
[15] M. Kuhn, K. Johnson, Applied predictive modeling, Springer, NY, 2013.
[16] L. Breiman, Random forests, Mach Learn (2021): 5-32.