Apklausų dalyvių aktyvumo analizė, pritaikant įvairius binarinio klasifikavimo algoritmus /

To conduct a comprehensive market research, it is crucial to have high-quality data. A rapidly growing method for collecting such data is through panel platforms. For companies that develop panel platforms, it is essential that panelists not only actively participate in surveys but also provide quality responses. The aim of this study was to test various binary classification algorithms to determine whether a survey response would be of high quality or not. To achieve this goal, five binary classification algorithms were selected: logistic regression, K-nearest neighbors, decision tree, support vector machine, and XGBoost classifiers. The best results were obtained using the XGBoost classifier, with oversampling applied to the training dataset. The best model achieved a sensitivity (recall) metric of 82 % and a specificity of 87 %. The worst results were obtained using logistic regression, with a sensitivity metric of 78 % and a specificity of 75 %.

Grantor dc:publisher

Institutional Repository of Vilnius University

Year dc:date

2024

Author dc:creator

• Šukytė, Ema,

Statement dc:rights

• info:eu-repo/semantics/openAccess

Language dc:language

lit

Repository record dc:identifier

https://repository.vu.lt/VU:ELABAETD210643726&prefLang=en_US

OAI identifier oai:identifier

oai:vu.lt:elaba:210643726