DC Field | Value | Language |
dc.contributor.author | Baria, A. | - |
dc.contributor.author | Globa, L. S. | - |
dc.contributor.author | Moroz, A. | - |
dc.date.accessioned | 2019-02-26T10:42:32Z | - |
dc.date.available | 2019-02-26T10:42:32Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Baria, A. Approach to Prediction of Mobile Operators Subscribers Churn / A. Baria, L. Globa, A. Moroz // Открытые семантические технологии проектирования интеллектуальных систем = Open Semantic Technologies for Intelligent Systems (OSTIS-2019) : материалы международной научно-технической конференции, Минск, 21 - 23 февраля 2019 г. / Белорусский государственный университет информатики и радиоэлектроники; редкол.: В. В. Голенков (гл. ред.) [и др.]. - Минск, 2019. - С. 155 - 160. | ru_RU |
dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/34531 | - |
dc.description.abstract | This article presents an approach to the description of machine learning methods for predicting the
outflow of telecom operator subscribers. Describes the
parameters characterizing the interaction of the mobile
operator with end users. The parameters that have the
greatest influence on the client’s decision to refuse the
services of a mobile operator have been determined. The
originality of the approach lies in the use of such mathematical methods that allow you to determine the main set
of parameters, due to which specific subscribers are prone
to changing the mobile operator. The proposed approach
allows you to organize a system in which it is possible to
determine the main parameters characterizing the tendency
of customers to outflow and acting on them using various
methods to try to increase subscriber loyalty. A comparative
analysis of the results obtained using the analyzed logistic
regression methods, Bootstrap aggregating and random
forest showed that the spread of prediction errors does not
exceed 6%. However, the advantage of the random forest
method is the ability to determine the set of parameters
that make the greatest contribution to making decisions by
the subscriber to change the mobile operator. Therefore,
for analyzing customer loyalty, a random forest method
can be recommend, which showed on the test sample an
improvement in the accuracy of the predictions in the
sample to 6-7%. | ru_RU |
dc.language.iso | en | ru_RU |
dc.publisher | БГУИР | ru_RU |
dc.subject | материалы конференций | ru_RU |
dc.subject | telecom operator | ru_RU |
dc.subject | churn | ru_RU |
dc.subject | machine learning | ru_RU |
dc.subject | random forest | ru_RU |
dc.subject | prediction mathematical methods | ru_RU |
dc.title | Approach to Prediction of Mobile Operators Subscribers Churn | ru_RU |
dc.title.alternative | Предсказание оттока абонентов от операторов мобильной связи | ru_RU |
dc.type | Статья | ru_RU |
Appears in Collections: | OSTIS-2019
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