Performance Evaluation on Resolution Time Prediction Using Machine Learning Techniques

Tong Tong-Ern - Multimedia University, 63100, Cyberjaya, Malaysia
Haw Su-Cheng - Multimedia University, 63100, Cyberjaya, Malaysia
Ng Kok-Why - Multimedia University, 63100, Cyberjaya, Malaysia
Mutaz Al-Tarawneh - Mutah University, 61710, Jordan
Tong Gee-Kok - Multimedia University, 63100, Cyberjaya, Malaysia

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The quality of customer service emphasizes support tickets. An excellent support ticket system qualifies businesses to provide clients with the finest level of customer support. This enables enterprises to guarantee the consistency of quality customer service delivered successfully, ensuring all clients have a good experience regardless of the nature of their inquiry or issue. To further achieve a higher efficiency of resource allocation, this is when the prediction of ticket resolution time comes into place. The advancing technologies, including artificial intelligence (AI) and machine learning (ML), can perform predictions on the duration required to tackle specific problems based on past similar data. ML enables the possibility of automatically classifying tickets, making it possible to anticipate the time resolution for cases. This paper explores various ML techniques widely applied in the Resolution Time Prediction system and investigates the performance of three selected ML techniques via the benchmarking dataset obtained from the UCI Machine Learning Repository. Implementing selected techniques will involve creating a graphical user interface and data visualization to provide insight for data analysis. The best technique will be concluded after performing the ML technique evaluation. The evaluation metrics involved in this step include Root Mean Square Error (RMSE) and Root Mean Absolute Error (MAE). The experimental evaluation shows that the best performance among the selected ML techniques is Random Forest (RF). 


Resolution Time Prediction; Machine Learning; Ticketing System; Customer Service; Recommender System

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