IT Support Ticket Completion Time Prediction - GANİZ, MURAT CAN (My University, 2022)
© 2022 IEEE.Prediction of the time that will be spent on IT support tickets is very important for planning and optimization of IT support services that are usually bound with service level agreements. Predicting completion time of a ticket is a difficult problem, which requires substantial experience and technical expertise if done manually by a human. However, it is possible to automate this task using supervised machine learning models given we have a large amount of labeled data. In this study, we employ supervised machine learning algorithms to predict completion time of tickets for IT support. We use a real-world dataset that includes about 17 thousand tickets. We employ data science approaches to preprocess and transform the input and feed to supervised machine learning algorithms for learning models for ticket completion time prediction. More specifically we use Linear Regression, Decision Trees Regression, Random Forest Regression, Support Vector Machines Regression, and Multiple Regression algorithms. For the evaluation of these supervised models, we use several metrics such as MAE, MSE, and MAPE. Our results show varying success levels with different supervised machine learning algorithms for this difficult task. Among the models we train, the Decision Trees and Random Forest Regression show promising results.
Keywords: Bilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği; Bilgisayar Bilimleri; Algoritmalar; Mühendislik ve Teknoloji; Information Systems, Communication and Control Engineering; Computer Sciences; algorithms; Engineering and Technology; Mühendislik, Bilişim ve Teknoloji (ENG); Bilgisayar Bilimi; Mühendislik; BİLGİSAYAR BİLİMİ, YAPAY ZEKA; TELEKOMÜNİKASYON; Engineering, Computing & Technology (ENG); COMPUTER SCIENCE; ENGINEERING; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; TELECOMMUNICATIONS; Yapay Zeka; Fizik Bilimleri; Bilgisayar Ağları ve İletişim; Bilgisayar Bilimi Uygulamaları; Bilgisayarla Görme ve Örüntü Tanıma; Artificial Intelligence; Physical Sciences; Computer Networks and Communications; Computer Science Applications; Computer Vision and Pattern Recognition; Data Science; IT Support; Machine Learning; Prediction; Regression; Supervised Learning