Forecast Parcel Volume

Chiam, Ee Min (2021) Forecast Parcel Volume. Final Year Project (Bachelor), Tunku Abdul Rahman University College.

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Abstract

Data, in today’s business and technology world, is indispensable. Nowadays, organisations are turning to predictive analytics to help in analysing the data and also helps in extracting this valuable information from the organisations data to develop predictive models that can advance business performance through the effective delivery of products and services. However, a massive amount of data is produced every day by businesses and users. Raw data cannot be directly used for analysis and the vast and voluminous data sets may be structured or unstructured and sometimes there is missing data. As a result, predictive analytics generated from these data becomes inaccurate. Besides, it is also a hassle having to pull data from different systems and trying to make sense out of it. This may result in doing many manual reports. In order to solve this problem, we came out with a graph by using R Shiny apps and algorithms such as Long-Short Term Memory. This could help DHL Express to do predictive analysis more effectively.