We consider the technique to forecast the net revenue collections of a feature film. Previous work on this problem has been addressed majorly to Hollywood films with very limited work on motion pictures developed by the Hindi Film Industry – Bollywood. In this piece of work, we use the parameters governing a movie’s revenue and the historical revenue gross patterns for forecasting. We also show that the model can be used for low budget movies which are usually left out by technology giants like Google, Twitter etc. due to negligible buzz for the movie as compared to that for high-budget ones. Key

1. Forecast Model for Box-Office Revenue of
Bollywood Feature Films using Machine Learning
B. E. Computer Engineering
Netaji Subhas Institute Of Technology,
New Delhi
March 13, 2015
Presented by:
Prerit Kohli
PGP at Indian Institute of Management, Indore

2. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 2/33
PROBLEM STATEMENT
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References

3. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 3/33
Problem Statement
The aim is to forecast the Box-office revenue for a Bollywood
feature film using Machine Learning, by adding the computed
influence of each parameter of a movie that is believed to
affect its revenue.
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References

4. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 4/33
MOTIVATION
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References

5. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 5/33
Motivation
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Bollywood is the world’s largest filmmaking entity, with over 1,000
films produced annually.
Bollywood generated revenue of around Rs. 15,000 crores in 2011
and this figure has been growing by 10 percent a year.

6. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 5/33
Motivation
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Bollywood is the world’s largest filmmaking entity, with over 1,000
films produced annually.
Bollywood generated revenue of around Rs. 15,000 crores in 2011
and this figure has been growing by 10 percent a year.
It has a range of attributes such as the Music-album industry and
the “masala” film genre, distinct from film industries in other
countries.

7. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 6/33
Motivation
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Unlike Hollywood, much research has not been done on forecasting
for Bollywood feature films.

8. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 6/33
Motivation
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Unlike Hollywood, much research has not been done on forecasting
for Bollywood feature films.
Forecast model used to assist film studios as even a single movie
can be the difference between crores of rupees of profit or loss in a
given year[1].
[1] Jeffrey S. Simonoff, Ilana R. Sparrow (2000), Predicting movie grosses: Winners and losers,
blockbusters and sleepers. Stern School of Business, New York University.

9. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 6/33
Motivation
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Unlike Hollywood, much research has not been done on forecasting
for Bollywood feature films.
Forecast model used to assist film studios as even a single movie
can be the difference between crores of rupees of profit or loss in a
given year[1].
Forecast model also used to assist cinema hall/multiplex owners in
planning out movie schedules for forthcoming box-office weekends.
[1] Jeffrey S. Simonoff, Ilana R. Sparrow (2000), Predicting movie grosses: Winners and losers,
blockbusters and sleepers. Stern School of Business, New York University.

10. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 7/33
METHODOLOGY
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References

11. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 8/33
Methodology
Pre-production
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Fig 1 Lifecycle of a Feature Film

12. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 8/33
Methodology
Pre-production
Film shoot
& dubbing
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Fig 1 Lifecycle of a Feature Film

13. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 8/33
Methodology
Pre-production
Film shoot
& dubbing
Post-
production
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Fig 1 Lifecycle of a Feature Film

14. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 8/33
Methodology
Pre-production
Film shoot
& dubbing
Post-
production
Release
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Fig 1 Lifecycle of a Feature Film

15. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 8/33
Methodology
Pre-production
Film shoot
& dubbing
Post-
production
Release Post-release
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Fig 1 Lifecycle of a Feature Film

16. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 9/33
Methodology
Pre-production
Film shoot
& dubbing
Post-
production
Release Post-release
Post-production Method
Ek Villain
June 27, 2014
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References

17. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 9/33
Methodology
Pre-production
Film shoot
& dubbing
Post-
production
Release Post-release
Post-production Method Next-change Method
Ek Villain
June 27, 2014
Holiday
June 6, 2014
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References

18. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 9/33
Methodology
Pre-production
Film shoot
& dubbing
Post-
production
Release Post-release
Post-production Method Post-release MethodNext-change Method
Ek Villain
June 27, 2014
CityLights
May 30, 2014
Holiday
June 6, 2014
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References

19. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 10/33
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
IMPLEMENTATION

20. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 11/33
Regression Analysis
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Regression is the Machine Learning technique used in our forecast
model.

21. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 11/33
Regression Analysis
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Regression is the Machine Learning technique used in our forecast
model.
Datasets of parameters of already-released Movies are built and
fed to the machine.
Actual revenues of the movies are also fed.

22. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 11/33
Regression Analysis
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Regression is the Machine Learning technique used in our forecast
model.
Datasets of parameters of already-released Movies are built and
fed to the machine.
Actual revenues of the movies are also fed.
The machine learns from these datasets, the influence of each
parameter on the movie revenue.
This analysis is used to forecast revenues for upcoming movies.

23. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 12/33
Linear Regression
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
The Linear Regression formula is as follows:
R = β1(P1) + β2(P2) + β3(P3) +... + βn(Pn)
where R is the forecasted revenue for the film, Pn is the value of nth
parameter for the film, and βn is the corresponding coefficient of the
nth parameter[2].
[2] Jae-Mook Lee, Tae-Hyung Pyo, Forecast Model for Box-office Revenue of Motion Pictures, Dec 2009.

24. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 13/33
Post-production Method
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Applied when the film is completed and sent to the studio.
Used by Production houses (eg. Yash Raj Films) for deciding the
marketing budget of an upcoming movie.

25. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 13/33
Post-production Method
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Applied when the film is completed and sent to the studio.
Used by Production houses (eg. Yash Raj Films) for deciding the
marketing budget of an upcoming movie.
Following parameters are considered:
Top Actor/Actress Trending Actor/Actress
Top Director Promising Director
Sequel /Trilogy Top Production House
Movie Genre Movie Budget
Adaptation/Remake buzz Success record of Cast/Crew
Table 1 Parameters for Post-production Method

26. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 14/33
Post-production Method: Average Error
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Fig 2 Average-error plot for Post-production Method

27. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 15/33
Next-change Method
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Applied when the movie prints are sent to the theaters a few days
before the release date.
Used by movie exhibitors (cinema halls) for finalizing on the
number of shows to be devoted to an upcoming movie.

28. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 15/33
Next-change Method
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Applied when the movie prints are sent to the theaters a few days
before the release date.
Used by movie exhibitors (cinema halls) for finalizing on the
number of shows to be devoted to an upcoming movie.
It adds the following parameters of its own, along with those of
previous method:
Music-album popularity Movie Screens across India
Out-of-budget promotion Critics Reviews from Paid-previews
Censor Board Rating (U/UA/A) Competition from movies sharing same release-date
Table 2 More parameters added for Next-change Method

29. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 16/33
Next-change Method: Average Error
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Fig 3 Average-error plot for Next-change Method

30. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 17/33
Post-release Method
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Applied at the end of the first weekend of the release-date.

31. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 17/33
Post-release Method
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Applied at the end of the first weekend of the release-date.
This method adds the following parameters of its own, along with
those of the Post-production and Next-change Methods:
Critics’ Reviews Audience Response
Unexpected promotion post-release Promotion by Govt. (E-tax exemption)
Viral word-of-mouth
Table 3 More parameters added for Post-release Method

32. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 18/33
Post-release Method: Average Error
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Fig 4 Average-error plot for Post-release Method

33. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 19/33
Revenue forecast for 2 States [2014]
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
2 States [2014] References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Trending Actor (m): Arjun Kapoor (0.32)
Trending Actor (f): Alia Bhatt (0.23)

34. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 19/33
Revenue forecast for 2 States [2014]
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
2 States [2014] References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Trending Actor (m): Arjun Kapoor (0.32)
Trending Actor (f): Alia Bhatt (0.23)
Estb. Production House: Dharma Productions (0.22)
Budget: Rs. 36 Crores (0.28)

35. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 19/33
Revenue forecast for 2 States [2014]
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
2 States [2014] References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Trending Actor (m): Arjun Kapoor (0.32)
Trending Actor (f): Alia Bhatt (0.23)
Estb. Production House: Dharma Productions (0.22)
Budget: Rs. 36 Crores (0.28)
Adaptation: Chetan Bhagat’s “2 States” (0.25)
Music album popularity: Very good response (0.24)

36. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 19/33
Revenue forecast for 2 States [2014]
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
2 States [2014] References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Trending Actor (m): Arjun Kapoor (0.32)
Trending Actor (f): Alia Bhatt (0.23)
Estb. Production House: Dharma Productions (0.22)
Budget: Rs. 36 Crores (0.28)
Adaptation: Chetan Bhagat’s “2 States” (0.25)
Music album popularity: Very good response (0.24)
Genre(s): Drama (-0.1) + Romance (0.21)
Censor Board rating: U/A (0.32)

37. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 20/33
Revenue forecast for 2 States [2014]
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
2 States [2014] References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Predicted Revenue: R
Log10 R = 0.32 + 0.23 + 0.22 + 0.28 + 0.25 + 0.24 +
(-0.1) + 0.21 + 0.32
= 1.97

38. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 20/33
Revenue forecast for 2 States [2014]
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
2 States [2014] References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Predicted Revenue: R
Log10 R = 0.32 + 0.23 + 0.22 + 0.28 + 0.25 + 0.24 +
(-0.1) + 0.21 + 0.32
= 1.97
Antilog(1.97) = 93.325
Predicted Gross: R = 93.33 Crores

39. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 20/33
Revenue forecast for 2 States [2014]
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
2 States [2014] References
Regression Analysis
Post-production Method
Next-change Method
Post-release Method
Predicted Revenue: R
Log10 R = 0.32 + 0.23 + 0.22 + 0.28 + 0.25 + 0.24 +
(-0.1) + 0.21 + 0.32
= 1.97
Antilog(1.97) = 93.325
Predicted Gross: R = 93.33 Crores
Actual Gross: 104.04 Crores
Percentage Error = |93.33 – 104.04|/104.04 = 10.29%

40. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 21/33
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
RESULTS

41. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 22/33
Results
Problem Statement Results
Motivation Challenges Faced
Methodology Learning Experience
Implementation Future Work
References
The following are the Average errors in the 3 methods adopted:
Post-production Method: 36.05%
Next-change Method: 19.52%
Post-release Method: 11.74%
This depicts the correlation between the numbers of revenue-
affecting parameters and the accuracy of the revenue forecast.

42. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 23/33
Overview Results
Objective Challenges Faced
Motivation Learning Experience
Methodology Future Work
Experiment References
CHALLENGES FACED

43. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 24/33
Challenges Faced
Overview Results
Objective Challenges Faced
Motivation Learning Experience
Methodology Future Work
Experiment References
> Forecasting revenues for surprise blockbusters such as
Queen [2014]
> Incorporating multiple genres for movies.
> Demarcation for Database Lists. Doubts such as whether to
include Sanjay Dutt in the Top Actors list.
> Computation of loss of revenue for movies sharing the same
release-date.

44. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 25/33
Overview Results
Objective Challenges Faced
Motivation Learning Experience
Methodology Future Work
Experiment References
LEARNING EXPERIENCE

45. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 26/33
Learning Experience
Overview Results
Objective Challenges Faced
Motivation Learning Experience
Methodology Future Work
Experiment References
> Current trends in the multi-billion Bollywood industry.
> Tapping Machine learning techniques for forecasting
revenues of films we see every week.
> Strategies adopted by Film Studios and Film Exhibitors for
maximum revenue generation.
> Statistical verification and graph plotting.

46. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 27/33
Overview Results
Objective Challenges Faced
Motivation Learning Experience
Methodology Future Work
Experiment References
FUTURE WORK

47. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 28/33
Future Work
Overview Results
Objective Challenges Faced
Motivation Learning Experience
Methodology Future Work
Experiment References
> Sub-categorizing the Database for inclusion of prominent
actors such as John Abraham.
> Incorporating the foreign Box-office of a film.
> Exploring more factors that determine movie revenues.

48. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 29/33
Overview Results
Objective Challenges Faced
Motivation Learning Experience
Methodology Future Work
Experiment References
REFERENCES

49. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 30/33
References
[1] Jeffrey S. Simonoff, Ilana R. Sparrow (2000), Predicting movie
grosses: Winners and losers, blockbusters and sleepers. Stern School
of Business, New York University.
[2] Jae-Mook Lee, Tae-Hyung Pyo, Forecast Model for Box-office
Revenue of Motion Pictures, Dec 2009.
[3] Chrysanthos Dellarocas, Xiaoquan (Michael) Zhang, Neveen F.
Awad. (2007, Aug.). Exploring the value of online product reviews in
forecasting sales: The case of motion pictures. Journal of Interactive
Marketing. [Online].
Available: http://blog.mikezhang.com/files/movieratings.pdf.
Overview Results
Objective Challenges Faced
Motivation Learning Experience
Methodology Future Work
Experiment References

50. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 31/33
References
[4] Nikhil Apte, Mats Forssell, Anahita Sidhwa, Predicting Movie
Revenue, Dec 2011.
[5] Mahesh Joshi, Dipanjan Das, Kevin Gimpel, Noah A. Smith, Movie
Reviews and Revenues: An Experiment in Text Regression. Language
Technologies Institute, Carnegie Mellon University.
[6] Alec Kennedy, “Predicting Box Office Success: Do Critical Reviews
Really Matter?”, The University of California, Berkeley.
[7] Márton Mestyán, Taha Yasseri, János Kertész (2013, Aug.). Early
Prediction of Movie Box Office Success Based on Wikipedia Activity
Big Data. Institute of Physics, Budapest University of Technology and
Economics.
Overview Results
Objective Challenges Faced
Motivation Learning Experience
Methodology Future Work
Experiment References

51. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 32/33
References
Overview Results
Objective Challenges Faced
Motivation Learning Experience
Methodology Future Work
Experiment References
[8] “Movie Database", Available at https://www.imdb.com/
[9] “Box Office Revenue Database", Available at https://www.koimoi.com/
[10] “Music Popularity Index Database",
Available at https://www.top10bollywood.com/
[11] “Film Critics Database", Available at
https://www.hindustantimes.com/entertainment/
https://www.ibnlive.in.com/movies/reviews/
https://www.bollywood.bhaskar.com/reviews/
https://zoomtv.indiatimes.com/
https://www.bollywoodhungama.com/reviews/

52. Prerit Kohli, Rajat Taneja, Saumya Bansal Movie Revenue Forecasting 33/33
Thank You
Overview Results
Objective Challenges Faced
Motivation Learning Experience
Methodology Future Work
Experiment References