Citation

Tai, Tong Ern and Haw, Su Cheng and Ng, Kok Why and Naveen, Palanichamy and Al-Tarawneh, Mutaz (2023) Performance Evaluation on Resolution Time Prediction Using Decision Tree, Random Forest and eXtreme Gradient Boosting. In: 2023 International Conference on Computer Applications Technology (CCAT), 15-17 September 2023, Guiyang, China.

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Abstract

—Providing excellent customer service is always important for an organisation, and one of the biggest demands from customers is the speed of resolution. Customers anticipate a prompt, simple, and efficient response when they submit a service ticket to request service. While the customer service representatives attempt to resolve problems assigned to them while simultaneously giving clients a nice experience and upholding the company's excellent reputation, they receive several tickets each day. On-time services may boost customer satisfaction, foster client loyalty, and grow the number of prospective loyal consumers. The best option for speeding up ticket resolution in this case will be a prediction model that makes use of Machine Learning techniques. Experimental evaluations revealed that eXtreme Gradient Boosting works better than other strategies by achieving a low Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and MAE (Mean Absolute Error) scores with good model accuracy.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Machine Learning,
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	27 Mar 2024 02:34
Last Modified:	27 Mar 2024 02:34
URII:	http://shdl.mmu.edu.my/id/eprint/12205

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