50 Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). SOME ASPECTS OF MACHINE LEARNING IN LOCATION TASKS Kateryna Kononova 1 1 Department of Economic Cybernetics and Applied Economics, V. N. Karazin Kharkiv National University, e-mail: kateryna.kononova@karazin.ua Abstract. As a result of the study, locations for Pan-Asian food delivery service in Kharkiv have been found so that their network evenly covered the entire city; and different units were at an acceptable distance from each other. The company’s order database allowed us to apply ML algorithms, in particular, clustering methods to find optimal locations. Three clustering models were developed and a series of experiments were conducted with each of them. The analysis of the model results allowed us to confirm both hypotheses put forward in the paper, namely: 1) reducing dimension does not skew clustering results obtained on the full database; 2) urban traffic has a significant impact on clustering results. This made us recommend pre-group the data and consider urban traffic in location tasks for the referred company. Keywords: Location task, Machine Learning, Clustering, Shift Means, K- means, API, Google maps. 1. Introduction The company’s success significantly depends on the location. It affects not only the cost of rent, access to materials, workers, transportation, but also the perception of the brand and expansion of the customer number. Location databases have enabled companies to do initial screening themselves, hence reducing their need to rely on external experts to providing only very specific information on locations [7]. Machine Learning (ML) algorithms are effectively used for finding the right locations using accumulated companies’ data; especially, clustering methods, which within the geomarketing approach, use spatial data (coordinates, address, registry or other bindings) along with general information. Various theoretical aspects of ML application in the location tasks are explored in the scientific literature. Montejano et al. overviewed different location models used within the geomarketing field, exemplifying it through the use of Geographic Information Systems (GIS) [8]. Serajnik et al. performed the statistical analysis, evaluated geodata and carried out spatial analysis with a subsequent cartographic visualization to define mall location strategy [11]. Rosu et al. used quantitative models for measuring accessibility to the existing shopping centers in the city, calculating thus their catchment area, for identifying a suitable location for a new shopping center [9].