IT Support Ticket Completion Time Prediction

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.