  Movie-making has become one of the most costly and risky endeavors in the
entertainment industry. Continuous change in the preference of the audience
makes it harder to predict what kind of movie will be financially successful at
the box office. So, it is no wonder that cautious, intelligent stakeholders and
large production houses will always want to know the probable revenue that will
be generated by a movie before making an investment. Researchers have been
working on finding an optimal strategy to help investors in making the right
decisions. But the lack of a large, up-to-date dataset makes their work harder.
In this work, we introduce an up-to-date, richer, and larger dataset that we
have prepared by scraping IMDb for researchers and data analysts to work with.
The compiled dataset contains the summery data of 7.5 million titles and detail
information of more than 200K movies. Additionally, we perform different
statistical analysis approaches on our dataset to find out how a movie's
revenue is affected by different pre-released attributes such as budget,
runtime, release month, content rating, genre etc. In our analysis, we have
found that having a star cast/director has a positive impact on generated
revenue. We introduce a novel approach for calculating the star power of a
movie. Based on our analysis we select a set of attributes as features and
train different machine learning algorithms to predict a movie's expected
revenue. Based on generated revenue, we classified the movies in 10 categories
and achieved a one-class-away accuracy rate of almost 60% (bingo accuracy of
30%). All the generated datasets and analysis codes are available online. We
also made the source codes of our scraper bots public, so that researchers
interested in extending this work can easily modify these bots as they need and
prepare their own up-to-date datasets.
