Using machine learning to predict sellers’ activity next month at an online auction platform

dc.contributorAalto Universityen
dc.contributorAalto-yliopistofi
dc.contributor.advisorLiu, Yong
dc.contributor.advisorYanqing, Lin
dc.contributor.authorHuynh, Thi
dc.contributor.departmentTieto- ja palvelujohtamisen laitosfi
dc.contributor.schoolKauppakorkeakoulufi
dc.contributor.schoolSchool of Businessen
dc.date.accessioned2022-01-30T17:01:44Z
dc.date.available2022-01-30T17:01:44Z
dc.date.issued2022
dc.description.abstractThe survival and growth of online auction platform depends on the ability of predicting the sellers’ activity as accurately as possible. With the results of determining how sellers will be active in the future, the marketing effort and many operational decisions will target the right sellers at the right time. This thesis attempts to identify how to measure the sellers’ activity and how it will be next month at an online auction platform by using machine learning models. The model is built on the historical data on the platform, which is collected and aggregated by SQL. After identifying what sellers’ activity is and how to measure it, Linear Regression and Random Forest Regressor are applied to the data. The performance of two algorithms will be compared to achieve the model with the highest accuracy. The result is the reusable model that predicts the sellers’ activity next month and the recommendations how to improve the model in the next research.en
dc.format.extent40 + 4
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/112612
dc.identifier.urnURN:NBN:fi:aalto-202201301511
dc.language.isoenen
dc.locationP1 Ifi
dc.programmeInformation and Service Management (ISM)en
dc.subject.keywordmachine learningen
dc.subject.keywordonline auctionen
dc.subject.keywordsellers' activityen
dc.subject.keywordrandom forest regressoren
dc.subject.keywordlinear regressionen
dc.subject.keywordactivity predictionen
dc.subject.keywordfeature engineeringen
dc.titleUsing machine learning to predict sellers’ activity next month at an online auction platformen
dc.typeG2 Pro gradu, diplomityöfi
dc.type.ontasotMaster's thesisen
dc.type.ontasotMaisterin opinnäytefi
local.aalto.electroniconlyyes
local.aalto.openaccessno
Files