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
Query data from search engines can provide many insights about the human behavior. Therefore, massive
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analyzing Google query database for search terms, we present a method of analyzing large numbers of
search queries to predict outcomes such as movie incomes. Our results illustrate the potential of combining
extensive behavioral data sets that offer a better understanding of collective human behavior.
This presentation presents points to consider for building and using models in the regulated pharmaceutical industry and offers examples of how models can play a part in the Quality by Design (QbD) framework.
Query data from search engines can provide many insights about the human behavior. Therefore, massive
data resulting from human interactions may offer a new perspective on the behavior of the market. By
analyzing Google query database for search terms, we present a method of analyzing large numbers of
search queries to predict outcomes such as movie incomes. Our results illustrate the potential of combining
extensive behavioral data sets that offer a better understanding of collective human behavior.
This presentation presents points to consider for building and using models in the regulated pharmaceutical industry and offers examples of how models can play a part in the Quality by Design (QbD) framework.
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The session also reiterates the fact that Lean & Agile principles are not specific and limited for to any specific industry but the same can be applied to any industry and even in our day today work and get benefited.
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6. Movie Post-mortem vs. Agile Retrospective
7. Film Budgeting vs. Agile Estimation
MOVIE SUCCESS PREDICTION AND PERFORMANCE COMPARISON USING VARIOUS STATISTICAL...ijaia
Movies are among the most prominent contributors to the global entertainment industry today, and they
are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide
films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety
of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic
Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial
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on the training and validation datasets as well as the testing dataset, the availability of new movie
characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered
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Mathematical modeling is an important step for developing many advanced technologies in various domains such as network security, data mining and etc… This lecture introduces a process that the speaker summarizes from his past practice of mathematical modeling and algorithmic solutions in IT industry, as an applied mathematician, algorithm specialist or software engineer , and even as an entrepreneur. A practical problem from DLP system will be used as an example for creating math models and providing algorithmic solutions.
An overview of the economics, financing structures and financial analysis of mid to high budgeted independent films. Presented at the 2015 Entertainment Finance Forum in Hollywood, CA.
The presentation is on the topic- Indian Film Industry, and is mainly concentrated on Bollywood.
It deals with the functioning, structure, history, business, problems, market scenario, future etc. of the Indian Film Industry.
In this research work we have developed a mathematical model for predicting the success class [flop , hit , super hit] of the Indian movies, for doing this we have develop a methodology in which the historical data of each component [e. G actor , actress, director, music ]that influences the success or failure of a movie is given is due weightage and then based on multiple thresholds calculated on the basis of descriptive statistics of dataset of each component it is given class [flop , hit, super hit] label. This dataset is then subjected to neural network [LM] based learning algorithm for automating the process and results in terms of match between actual class labels and predicted labels are evaluated. Results show that our strategy of identifying the class of success is highly effective and accurate which apparent from the classification matrix also.
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"Lean & Agile Movie Making” is a fresh, new and innovative topic (an experience of a novice movie makers team on Lean & Agile) and most importantly it has got a completely new dimension of imparting Agile, Lean & Kanban LIKE best practices from Movie Industry to the IT or other world like we did the same a decade back from Automobile Industry (Toyota).
The session also reiterates the fact that Lean & Agile principles are not specific and limited for to any specific industry but the same can be applied to any industry and even in our day today work and get benefited.
Some of the learning outcome from the session are :
1. How some of the Lean & Agile Like (similar to Lean & Agile) best practices used in movie making for ages can be adopted in IT and vice versa.
2. How Agile along with Six-Sigma Lean & Kanban can act as a Power Pack for cost cutting as well as excellence in production execution in movie making.
3. Importance of special Tools and Techniques in movie making as well as in IT wherever there is a technical challenge or a creative constraint.
5. Movie Production Envisioning vs. Agile Daily Scrum Meeting & backlog replenishment
6. Movie Post-mortem vs. Agile Retrospective
7. Film Budgeting vs. Agile Estimation
MOVIE SUCCESS PREDICTION AND PERFORMANCE COMPARISON USING VARIOUS STATISTICAL...ijaia
Movies are among the most prominent contributors to the global entertainment industry today, and they
are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide
films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety
of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic
Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial
Neural Network. The models stated above were compared on a variety of factors, including their accuracy
on the training and validation datasets as well as the testing dataset, the availability of new movie
characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered
that certain characteristics have a greater impact on the likelihood of a film's success than others. For
example, the existence of the genre action may have a significant impact on the forecasts, although another
genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the
IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best
performing model of all the models discussed.
A project report on Six sigma for filmmaking processSachin Pandit
This is my MBA six sigma Project. I am a film maker and Six Sigma Professional. Tried to apply Lean and Six Sigma technique to film making process. Applied it on one of my short film Dand. The penalty Watch it at -
https://youtu.be/lQvtwG5Cm_g
Article develops model to predict movie's success through discriminant analysis . 'Word cloud' developed through Sentiment Analysis. R programming used
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Want to give us a social research brief? Check out this link: https://kimola.com/social-research
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Forecast Model for Box-Office Revenue of Bollywood Feature Films
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