SEGMENTATION OF OUTLETS USING MACHINE LEARNING CLUSTERING AND CLASSIFICATION ALGORITHMS
| dc.contributor.author | Nurmambetov D. | |
| dc.contributor.author | Bogdanchikov A. | |
| dc.date.accessioned | 2023-11-24T05:06:51Z | |
| dc.date.available | 2023-11-24T05:06:51Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Abstract. Segmentation of retail outlets in terms of manufacturing companies’ strategy applied in sales amount and trade activities for each of them is very important. Directed investments into outlets help companies to make more profit and decrease expenses. This study presents a method which can be used for outlets clustering using unsupervised and supervised machine learning algorithms comprising 2 steps – data partition using unsupervised Gaussian Mixture (GM) Model clustering algorithm based on outlets sales amount and further partition for one of them using Logistic Regression (LR) and Neural Networks (NN) classification algorithms, which predict whether outlets will achieve monthly sales plan. Previously, clustering was made without any special methods and clusters were formed using some agreed threshold values for outlets sales amount. The proposed algorithm was tested on real sales data and formed 3 clusters according to business needs. Sales plan achievement prediction gave up to 74% accuracy. | |
| dc.identifier.citation | D. Nurmambetov , A. Bogdanchikov / SEGMENTATION OF OUTLETS USING MACHINE LEARNING CLUSTERING AND CLASSIFICATION ALGORITHMS / СДУ хабаршысы - 2019 | |
| dc.identifier.issn | 2415-8135 | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/854 | |
| dc.language.iso | en | |
| dc.publisher | СДУ хабаршысы - 2019 | |
| dc.subject | clustering | |
| dc.subject | Logistic regression | |
| dc.subject | Neural Networks | |
| dc.subject | GM Model | |
| dc.subject | prediction | |
| dc.subject | СДУ хабаршысы - 2019 | |
| dc.subject | №4 | |
| dc.title | SEGMENTATION OF OUTLETS USING MACHINE LEARNING CLUSTERING AND CLASSIFICATION ALGORITHMS | |
| dc.type | Article |