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 called Numero. The University of Sydney Business School has obtained this
database going back to January 2000, for student project use. Additional data has been
obtained from OpusData to enrich the box office information supplied by Numero.
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 Australian Federal Government's key 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
narrative and documentary screen content. The organisation has a research division which
analyses various aspects of Australian films 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 different
types of movies, in particular, how to predict total revenues.
Problem Description
You have been provided with a dataset (see ‘Data Description’ below) that contains
Australian theatrical (cinema) box office information from January 1, 2000 to January 31,
2022.
In this project, you will:

• Use exploratory data analysis to identify the key attributes for predicting the total
Lifetime Gross revenues earned by movies 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. For the ‘change over time’ investigation, we suggest that you compare
the characteristics of the movies (including box office performance) over several similarly
sized consecutive time periods (e.g., ~5-year periods). Because the final task below focuses
on Australian, Asian and European movies, we ask that you also investigate whether (and in
what ways) those groups of movies are different from each other and from the rest.

• Develop a regression model for predicting the Lifetime Gross movie revenue.

Use any statistical or machine learning approaches that you feel are appropriate. We suggest
that you use the RMSLE to evaluate the performance of your final model. Ensure that you
justify the selection of your final model and interpret the final model in terms of the key
attributes for predicting the lifetime gross movie revenue. Because the final task below
focuses on Australian, Asian and European movies, we ask that you also investigate whether
(and in what ways) your model implies that those groups of movies are different from each
other and from the rest. 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, highlight differences between Australian, Asian and
European movies and outline strategies for maximizing box office revenue of
Australian, Asian and European movies.

Your strategy should take advantage of the key movie 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 movie attributes (other than
the early box office performance) 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 is ~9.3K rows (~2.5MB), one row per movie, covering the time period from
January 1, 2000 to January 31, 2022. The data contains fields including name and genre of
movie, country of origin, main actors and other characteristics of the movie, production
budget (for a subset of all movies), screening dates, number of screens, and information on
the box office revenues generated.

Additional Information
Most of the data was extracted from the Numero’s All Films Research database and reflects
the information provided by the movie distributors. Prior to 2014, this information was
collected by the Motion Picture Distributors Association of Australia. Information provided in
the last 6 columns (production_budget, creative_type, source, production_method,
sequel, and running_time) was extracted from the OpusData database.
As indicated in Screen Australia reports on trends in the cinema industry, the number of
screens in Australia has increased over the years. For example, the number of screens
available in 1980 was 829; by 2020 it had increased to 2,241. During the same period, the
number of cinemas has reduced from 713 to 473. The net result of these changes is that the
number of cinema patron seats has remained relatively stable over this period.
In their cinema 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).