Abstract
The increasing trend in online shopping urges the need of continuous enhancing and improving user experience in many aspects and on-time delivery of goods is one of the key area. This paper explores the adoption of machine learning in predicting late delivery of goods on Malaysia national courier service named Poslaju. The prediction model also enables the visualization of the delivery trends for Poslaju Malaysia. Meanwhile, data extraction, transformation, experimental setup and performance comparison of various machine learning methods will be discussed in this paper.
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Acknowledgement
The authors would like to thank Universiti Sains Malaysia for supporting the publication of this paper through USM Research University Grant scheme 1001/PKOMP/814254.
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Quah, J.W., Ang, C.H., Divakar, R., Idrus, R., Abdullah, N.L., Chew, X. (2019). Timing-of-Delivery Prediction Model to Visualize Delivery Trends for Pos Laju Malaysia by Machine Learning Techniques. In: Yap, B., Mohamed, A., Berry, M. (eds) Soft Computing in Data Science. SCDS 2018. Communications in Computer and Information Science, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-3441-2_7
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