- Title
- Machine learning-based modelling for museum visitations prediction
- Creator
- Yap, Norman; Gong, Mingwei; Naha, Ranesh; Mahanti, Aniket
- Date
- 2020
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/195533
- Identifier
- vital:18555
- Identifier
-
https://doi.org/10.1109/ISNCC49221.2020.9297182
- Identifier
- ISBN:9781728156286
- Abstract
- Cultural venues like museums increasingly seek to harness the value of data analytics to make data driven decisions related to exhibitions duration, marketing campaigns, resource planning, and revenue optimization. One key priority is the need to understand the influencing factors behind visitor attendance. Using data collected from a large museum, we investigated whether the weather has a significant impact on visitor attendance or that other factors are more important. We applied the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology to perform the research, developed and built four different types of regression models using R and its machine learning packages to model visitor attendance. The models were trained and evaluated. Predictions of visitor attendance were then generated from each of the four models and forecast accuracy was measured. The extreme gradient boost model was the best model with the highest average forecast accuracy of 93% and lowest forecast variability when benchmarked against the actual visitor attendance from the test data set. The weather was not considered to be as significant in predicting visitor trends and numbers to the museum compared to factors like time of the day, day of the week and school holidays. However, it was still measured to have a slight impact as excluding weather variables resulted in a model with a poorer fit. Weather can potentially have a more marked impact on cultural attractions in more extreme weather environments and outdoor venues.
- Publisher
- IEEE
- Relation
- 2020 International Symposium on Networks, Computers and Communications (ISNCC); Montreal, Canada; 20-22nd October, 2020, p.1-7
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Subject
- Business; Cultural differences; Data analytics; Data mining; Data models; Machine learning; Meteorology; Prediction Model; Predictive models; Weather forecasting
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