"Inside Out 2" was the most profitable movie of 2024 at the global box office, with a net profit of 650 million U.S. dollars. The animated sequel garnered a total revenue of almost 1.7 billion U.S. dollars, recouping more than twice its expenses. In second position was another Disney animated movie, "Moana 2", with net profit exceeding 400 million U.S. dollars.

As of June 2025, "Avatar" (2009) was the most commercially successful movie of all time, grossing about 2.92 billion U.S. dollars at box offices across the globe. "Avengers: Endgame" (2019) and "Avatar: The Way of Water " (2022) followed, with revenues of around 2.8 billion and 2.32 billion dollars, respectively. Franchises and the pandemic impact In 2021, worldwide box office revenue grew by over 80 percent and reached 21.3 billion dollars. Yet the figure amounts to less than 51 percent of the value recorded in 2019, before the COVID-19 outbreak. Yet the success of "Spider-Man: No Way Home" (2021) suggests that the performance of major movie franchises at the box office may continue strong in a post-pandemic scenario. The format, which includes cases like the Marvel Cinematic Universe and Star Wars, has been a safe bet for Hollywood as it tends to pursue fans already interested in films part of consolidated brands. U.S. & Canada as a movie market Moviegoers in Canada and the U.S. usually turn to the largest players of this industry. North American cinema circuits such as AMC, Cineworld, Cinemark, and Cineplex have over 1.5 thousand sites spread across the two countries. Historically, movie genres like adventure and action attract most of the audience in the U.S. and Canada. Meanwhile, niches such as horror, romantic comedy (romcoms), and musicals rely largely on the most enthusiastic fans of each segment.

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As of January 2025, the four biggest movie flops of all time were Disney productions. "Turning Red" (2022), "Jungle Cruise" (2021), "Mars Needs Moms" (2011), and "Mulan" (2020) recorded losses of more than 140 million U.S. dollars at the global box office. Film releases and bad timing Out of the 10 movies listed, eight were released in or after March 2020, a month remembered for the worldwide outbreak of COVID-19. These films went big with the budget but went literally home as streaming became a safer bet for movie premieres amidst the pandemic. The coronavirus impacted movie ticket sales worldwide. In 2021, the box office revenue in the United States and Canada amounted to less than 40 percent of the value reported in 2019, before the outbreak. The largest cinema market on the planet seems to have experienced a swifter recovery. China saw its box office revenue add up to 47.3 billion yuan in 2021, or almost 74 percent of the 2019 figure. Can Disney afford flopping at the box office? Although the media Leviathan had also struggled during the core months of the COVID-19 outbreak, the Walt Disney Company's revenue started to bounce back and continues to amount to dozens of billions of dollars. In the meantime, the corporation invested even more in publicizing its new releases. Disney's advertising spending stood at 5.5 billion dollars in 2021 – a value almost two times higher than the ad expense recorded in 2019. The expansion indicates that the company could manage eventual failures at the box office while profiting from its blockbusters and non-film-related divisions.

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All prices are in INR CRORES.

Movie Name: The name of the Bollywood movie.

Worldwide Gross: The total box office earnings of the movie globally, including both domestic (India) and international markets.

India Gross: The total box office earnings of the movie in India, indicating its popularity and success in the domestic market.

India Net: The net box office earnings of the movie in India, after deducting taxes and other expenses.

Verdict: The verdict or classification given to the movie based on its box office performance, indicating whether it was a blockbuster, hit, average, flop, or any other categorization.

This dataset provides comprehensive information about Bollywood movies, their financial performance at the box office, and their verdicts, allowing for analysis and insights into the success and popularity of different films in the Indian film industry.

Description: ChatGPT

Context

I love movies.

I tend to avoid marvel-transformers-standardized products, and prefer a mix of classic hollywood-golden-age and obscure polish artsy movies. Throw in an occasional japanese-zombie-slasher-giallo as an alibi. Good movies don't exist without bad movies.

On average I watch 200+ movies each year, with peaks at more than 500 movies. Nine years ago I started to log my movies to avoid watching the same movie twice, and also assign scores. Over the years, it gave me a couple insights on my viewing habits but nothing more than what a tenth-grader would learn at school.

I've recently suscribed to Netflix and it pains me to see the global inefficiency of recommendation systems for people like me, who mostly swear by "La politique des auteurs". It's a term coined by famous new-wave french movie critic André Bazin, meaning that the quality of a movie is essentially linked to the director and it's capacity to execute his vision with his crew. We could debate it depends on movie production pipeline, but let's not for now. Practically, what it means, is that I essentially watch movies from directors who made films I've liked.

I suspect Neflix calibrate their recommandation models taking into account the way the "average-joe" chooses a movie. A few months ago I had read a study based on a survey, showing that people chose a movie mostly based on genre (55%), then by leading actors (45%). Director or Release Date were far behind around 10% each. It is not surprising, since most people I know don't care who the director is. Lots of US blockbusters don't even mention it on the movie poster. I am aware that collaborative filtering is based on user proximity , which I believe decreases (or even eliminates) the need to characterize a movie. So here I'm more interested in content based filtering which is based on product proximity for several reasons :

• Users tastes are not easily accessible. It is, after all, Netflix treasure chest

• Movie offer on Netflix is so bad for someone who likes author's movies that it wouldn't help

• Modeling a movie intrinsic qualities is a nice challenge

Enough.

"*The secret of getting ahead is getting started*" (Mark Twain)

https://img11.hostingpics.net/pics/117765networkgraph.png" alt="network graph">

Content

The primary source is www.themoviedb.org. If you watch obscure artsy romanian homemade movies you may find only 95% of your movies referenced...but for anyone else it should be in the 98%+ range.

• movies details are from www.themoviedb.org API : movies/details

• movies crew & casting are from www.themoviedb.org API : movies/credits

• both can be joined by id

• they contain all 350k movies up, from end of 19th century to august 2017. If you remove short movies from imdb you get similar amounts of movies.

• I uploaded the program to retrieve incremental movie details on github : https://github.com/stephanerappeneau/scienceofmovies/tree/master/PycharmProjects/GetAllMovies (need a dev API key from themoviedb.org though)

• I have tried various supervised (decision tree) / unsupervised (clustering, NLP) approaches described in the discussions, source code is on github : https://github.com/stephanerappeneau/scienceofmovies

• As a bonus I've uploaded the bio summary from top 500 critically-acclaimed directors from wikipedia, for some interesting NLTK analysis

Here is overview of the available sources that I've tried :

• Imdb.com free csv dumps (ftp://ftp.funet.fi/pub/mirrors/ftp.imdb.com/pub/temporaryaccess/) are badly documented, incomplete, loosely structured and impossible to join/merge. There's an API hosted by Amazon Web Service : 1€ every 100 000 requests. With around 1 million movies, it could become expensive also features are bare. So I've searched for other sources.

• www.themoviedb.org is based on crowdsourcing and has an excellent API, limited to 40 requests every 10 seconds. It is quite generous, well documented, and enough to sweep the 450 000 movies in a few days. For my purpose, data quality is not significantly worse than imdb, and as imdb key is also included there's always the possibility to complete my dataset later (I actually did it)

• www.Boxofficemojo.com has some interesting budget/revenue figures (which are sorely lacking in both imdb & tmdb), but it actually tracks only a few thousand movies, mainly blockbusters. There are other professional sources that are used by film industry to get better predictive / marketing insights but that's beyond my reach for this experiment.

• www.wikipedia.com is an interesting source with no real cap on API calls, however it requires a bit of webscraping and for movies or directors the layout and quality varies a lot, so I suspected it'd get a lot of work to get insights so I put this source in lower priority.

• www.google.com will ban you after a few minutes of web scraping because their job is to scrap data from others, than sell it, duh.

• It's worth mentionning that there are a few dumps of Netflix anonymized user tastes on kaggle, because they've organised a few competitions to improve their recommendation models. https://www.kaggle.com/netflix-inc/netflix-prize-data

• Online databases are largely white anglo-saxon centric, meaning bollywood (India is the 2nd bigger producer of movies) offer is mostly absent from datasets. I'm fine with that, as it's not my cup of tea plus I lack domain knowledge. The sheer amount of indian movies would probably skew my results anyway (I don't want to have too many martial-arts-musicals in my recommendations ;-)). I have, however, tremendous respect for indian movie industry so I'd love to collaborate with an indian cinephile ! https://img11.hostingpics.net/pics/340226westerns.png" alt="Westerns">

Inspiration

Starting from there, I had multiple problem statements for both supervised / unsupervised machine learning

• Can I program a tailored-recommendation system based on my own criteria ?

• What are the characteristics of movies/directors I like the most ?

• What is the probability that I will like my next movie ?

• Can I find the data ?

One of the objectives of sharing my work here is to find cinephile data-scientists who might be interested and, hopefully, contribute or share insights :) Other interesting leads : use tagline for NLP/Clustering/Genre guessing, leverage on budget/revenue, link with other data sources using the imdb normalized title, etc.

https://img11.hostingpics.net/pics/977004matrice.png" alt="Correlation matrix">

Motivation, Disclaimer and Acknowledgements

• I've graduated from an french engineering school, majoring in artificial intelligence, but that was 17 years ago right in the middle of A.I-winter. Like a lot of white male rocket scientists, I've ended up in one of the leading european investment bank, quickly abandonning IT development to specialize in trading/risk project management and internal politics. My recent appointment in the Data Office made me aware of recent breakthroughts in datascience, and I thought that developing a side project would be an excellent occasion to learn something new. Plus it'd give me a well-needed credibility which too often lack decision makers when it comes to datascience.

• I've worked on some of the features with Cédric Paternotte, a fellow friend of mine who is a professor of philosophy of sciences in La Sorbonne. Working with someone with a different background seem a good idea for motivation, creativity and rigor.

• Kudos to www.themoviedb.org or www.wikipedia.com sites, who really have a great attitude towards open data. This is typically NOT the case of modern-bigdata companies who mostly keep data to themselves to try to monetize it. Such a huge contrast with imdb or instagram API, which generously let you grab your last 3 comments at a miserable rate. Even if 15 years ago this seemed a mandatory path to get services for free, I predict one day governments will need to break this data monopoly.

[Disclaimer : I apologize in advance for my engrish (I'm french ^-^), any bad-code I've written (there are probably hundreds of way to do it better and faster), any pseudo-scientific assumption I've made, I'm slowly getting back in statistics and lack senior guidance, one day I regress a non-stationary time series and the day after I'll discover I shouldn't have, and any incorrect use of machine-learning models]

https://img11.hostingpics.net/pics/898068408x161poweredbyrectanglegreen.png" alt="powered by themoviedb.org">

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As of January 2025, the Marvel Cinematic Universe series was the highest-grossing film franchise with total worldwide box office revenue of ***** billion U.S. dollars. "Avengers: Endgame" (2019) was Marvel's highest-grossing movie with *** billion dollars in global revenue. The "Star Wars" and "Harry Potter" series followed with worldwide box office revenues of about ***** billion and **** billion dollars, respectively. May the profit be with you The Star Wars series started in the late 1970s and has captured the attention of nerds and moviegoers in general ever since. Released just before the COVID-19 outbreak, the latest film of the saga, "The Rise of Skywalker," recorded a global box office revenue of over *** billion dollars. This is almost **** times more than the production of the movie had cost. The success of the franchise goes beyond the realm of cinema. The video game Star Wars: Squadrons, for instance, sold over *** million digital copies worldwide in the first month of its release in October 2020. Fantastic box offices: where to find them again? While Star Wars has a history of peaks and valleys, the Harry Potter franchise seems to be jinxed since the launching of its prequel series, Fantastic Beasts. The box office revenue of "Fantastic Beasts: The Crimes of Grindelwald" (2018) stood below *** million dollars worldwide. This is the lowest revenue generated by a movie from the Harry Potter universe since the brand debuted on the big screen in 2001. The amount is also nearly ** percent below the global box office of its predecessor, "Fantastic Beasts and Where to Find Them" (2016).

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The highest grossing movie in the ‘The Lord of the Rings’ trilogy is ‘The Lord of the Rings: The Return of the King’, which generated over *** billion U.S. dollars in box office revenue worldwide. Second was ‘The Hobbit: An Unexpected Journey’ which made **** billion at the global box office. All three ‘The Lord of the Rings’ movies performed impressively at the box office despite comparatively low production costs, with the third movie outperforming every other LOTR film including those in ‘The Hobbit’ trilogy. ‘The Lord of the Rings’: further information‘The Lord of the Rings’ is a fantasy novel written by British author J. R. R. Tolkien, published in 1954. The book is highly regarded by children, adults and scholars alike and has found its way into schools and universities across the globe. The first movie adaptation of Tolkien’s written work came in the form of 2001 epic ‘The Fellowship of the Ring’, directed by Peter Jackson and featuring some of Hollywood’s best talent including Liv Tyler, Ian McKellen, Viggo Mortensen and Sean Bean to name just a few. ‘The Fellowship of the Ring’ was met with critical acclaim and made over *** million U.S. dollars at box offices worldwide. Despite the movie’s grand scale, spectacular shooting locations and detailed mise-en-scène, production costs amounted to just ** million U.S. dollars. Whilst to anyone outside of the film industry this amount of money may sound incredible, in the movie business this is a relatively small budget by today’s standards. Production costs for movies can reach over *** million U.S. dollars – the ‘Pirates of the Caribbean’ and ‘Avengers’ films are notorious for being expensive to make. ‘The Hobbit: The Battle of the Five Armies’ cost *** million to produce, but high production costs do not always translate into impressive revenues (the movie grossed just ****** million dollars at the North American box office). ‘The Hobbit’ films in general are a hotly contested topic among fans for their use of CGI and divided opinions about Tolkien’s shortest LOTR book having been adapted into three separate films.

Context

A friend of mine once said there must be some type of correlation between the data in movies. Not knowing what it was, and being quite skill-less and naive, I thought web scraping a list from IMDB would have the answer! Unfortunately no, but I am much more confident navigating the maze that is Pandas!

Content

On IMDB's website is a list of the top 250 highest rated movies ever made. Each movie includes cast members, budget, net gross earned and others. It was very interesting to scrape IMDB's page for this info, but looking at it contained more interesting tidbits. For instance, Tom Hanks, Leonardo DiCaprio and Robert DeNiro are the most featured actors in the most popular movies! I sense a collaboration!

Acknowledgements

IMDB, of course.

Inspiration

I really like movies, and there is some quantifiable data that goes along with them. So let's look at it!

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Statistics illustrates consumption, production, prices, and trade of Cellular Plates, Sheets and Films of Regenerated Cellulose in San Marino from Jan 2019 to May 2025.

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"The Force Awakens", the first "Star Wars" movie released since the acquisition of Lucasfilm in 2012, remains the most profitable production out of the sequel trilogy and other spin-offs. With a total net spending of 475.1 million U.S. dollars, it collected more than one billion U.S. dollars at the global box office. Three years later, the spin-off "Solo" was the only loss-making film produced by Disney.

In 2022, Disney animated movie "Strange World" recorded a total loss of 197.4 million U.S. dollars, making it the lowest grossing movie released in theaters that year. "Amsterdam" and "Lightyear" followed in second and third positions with 108.4 and 106 million dollars in revenue losses, respectively.

Ibom Journal of Social Issues

Statistics illustrates the net export volume of Non-Cellular Polypropylene Films, Sheets, Foil and Strip in Morocco from 2007 to 2024 by trade partner.

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