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.
翻译:电影制作已成为娱乐业中最昂贵和风险最大的一项努力。 观众偏好的持续变化使得人们更难预测在拳击办公室中哪种电影在财务上会成功。 因此, 谨慎、 聪明的利益攸关方和大型制作公司总是想知道电影在投资前产生的可能收入。 研究人员一直在努力寻找一种最佳战略来帮助投资者做出正确的决定。 但是缺乏大型的最新数据集使得他们的工作更加困难。 在这项工作中,我们引入了最新的、更丰富和更大的数据集,这是我们为研究人员和数据分析员准备的。 编译的数据集包含750万个标题的夏季数据以及200多部电影的详细信息。 此外,我们在数据集上进行了不同的统计分析,以找出电影收入如何受到预算、运行时间、发布月、内容评级和genreet等不同先前的属性的影响。 在我们的分析中,我们做了一个几乎由IMDB公司和数据分析所准备的60种最新数据。 我们的恒星投影/ directal 数据在模型上做了一个新版本的收入分析。