Screen Australia - QBUS6600 Project Outline
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Screen Australia - QBUS6600 Project Outline
Background
An important source of Australian box office data for movies is collated on a weekly basis by a company calledNumero. The University of Sydney Business School has obtained this database for student project use.
Screen Australia, our industry collaborator for this project, has expressed interest in our analysis of the movie data and assisted with the formulation of the questions for this project. Screen Australia is the AustralianFederal Government'skey funding body for the Australian screen production industry, created under the Screen Australia Act 2008. Screen Australia supports the development, production, promotion and distribution of Australian narrativeanddocumentaryscreen content. The organisation has a research division which analyses various aspects of the Australian film industry and has an interest in the performance of movies as revealed by Australian box office statistics.
The market for cinema-based movies is dynamic and has changed considerably over time as consumer tastes evolve and the nature of distributors and cinemas respond (for example streaming has captured audience share and although there are fewer actual movie theatres, there are now many more screens than was the case historically). One of the key questions facing the industry is how to better understand the drivers of financial success for movies in the post-COVID- 19 lockdown times, in particular, how to predict total revenues.
Problem Description
You have been provided with a dataset (see ‘Data Description’ below) that contains Australian cinema box office information from January 2010 to February 2023.
In this project, you will:
• Use exploratory data analysis to identify the key attributes for predicting the total Lifetime Gross revenue and to investigate how movies screened in the Australian theatres (cinemas) changed over time.
You should aim to find or reveal all relevant properties, characteristics, patterns, and statistics hidden in the dataset. Because the final task below focuses on the post-COVID- 19 era, we ask that you compare the characteristics of the movies (including box office performance) over 3 consecutive time periods that are up to you to determine: “ pre-COVID- 19” , “significant restrictions and lockdowns” , and a “ post-COVID- 19 lockdown” period. Explain and justify how your identified these periods - you are welcome to use publicly available information about lockdowns in major Australian cities, for example:https://www.timeout.com/melbourne/things- to-do/a-timeline-of-covid-19-in-australia-two-years-onorhttps://en.wikipedia.org/wiki/COVID-
• Develop a regression model for predicting the Lifetime Gross revenue of a movie on its opening day.
Implement any statistical or machine learning approaches that you feel are appropriate and use the RMSLE to evaluate the performance of your final model. Ensure that you justify the selection of your model and interpret the model in terms of the key attributes for predicting the lifetime gross movie revenue. Assume that the “Preview Gross” and the opening week’s theatre/screens data are available (i.e., can be used for prediction), while the opening day box office revenue is not yet available.
Because the final task below focuses on the post-COVID- 19 era, we ask that you also investigate whether (and in what ways) your model implies that the “ post-COVID- 19 lockdown” period is different from the “ pre-COVID- 19” period. If your model is too complex for this interpretation, we suggest that you also consider well-performing interpretable models (for example, linear models) for predicting the movie revenue.
• Based on your analysis, outline a strategy for maximizing Australian box office revenue of movies in the post-COVID- 19 era.
Your strategy should take advantage of the key attributes that you have identified for predicting the movie revenue and the models that you have built and validated. As part of your proposed strategy, you should include a discussion of the attributes (available by the opening day) that are likely to increase the box office revenue.
Data Description
You have been provided one tabular dataset in CSV format on Australian box office data.
Movie box office
This dataset has ~7. 1k rows (~ 1.3MB), one row per movie, covering the time period from January 1, 2010 to February 22, 2023. The data contains fields including name and genre of movie, country of origin, main actors and other characteristics of the movie, screening dates, number of screens, and information on the box office revenues generated.
Additional Information
The data was extracted from theNumero’s All Films Research database and reflects the
information provided by the movie distributors. Prior to 2014, this information was collected by theMotion Picture Distributors Association of Australia.
In theircinema trends analysis, Screen Australia considers the following movie categories based on Numero’s Opening day screens data: Limited (0- 19 screens); Speciality (20-99 screens); Mainstream 100- 199 screens); Wide (200-399 screens); Blockbuster (400+screens).
Information on movie ratings can be found here:www.classification.gov.au/classification-ratings/what-do-ratings-mean. Furthermore, Numero has noted the following regarding some of the entries in the ‘Rating’ column of the dataset:
EX: is Exempt. Alternative content titles such as concerts, live theatre on stage and Ballet's are exempt from Classification in Australia.
TBC: is to be confirmed. These titles were not rated by the Office of Film and Literature Classification (OFLC) at the time of release.
VARIOUS: is used when more than one film is bundled together for that title. For example, marathons or a showcase of films might have different ratings for each film. This is not a classification but is only used for the purposes of reporting.
2023-03-25