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Mar 28, 2023 · Over the last two decades, movies with budget information had better revenue performances in the movie market than movies without disclosed budgets. This may indicate a bias in the total population towards wide released movies – a characteristic that is considered in the dataset slices described in Table 3.
Nov 24, 2019 · Since 20,000,000 is the median of the budget dataset, films with a budget greater than 20,000,000 dollars were classified as high budget. The rest were classified as low budget.
- Experimental Analysis, Aiming to Find Areas of Immediate Interest
- Looking at How Movie Budgets and Gross Revenue Differ Depending on Genre
- Now, Looking at How Gross Revenues Have Changed Over Time
- How Have Genre Popularities Developed Over time?
- How Do Genres Compare When Looking at Movie Popularity and Gross Revenue?
- K-Means Cluster Analysis
- K-Means Cluster Plot Visualisation
- Kable Table Displaying Information Created by The K-Means Statistical Object
- So What Did We Do and What Have We Discovered?
In order to point out areas of particular interest it’s worth creating some basic plots which are easy to interpret, this is also a good way for us to explore the structure and format of our data while recognising any immediate trends or patterns. We started by looking at columns such as movie budgets, revenue, and movie genres. From the data, it w...
From these plots alone we can see that action and adventure (with the exception of some science fiction films) excel in terms of revenue, such movies require an initial upfront budget as seen by the second plot. Now we have an idea of what variables can be useful for telling a story about commercial success, whether it be gross revenues within each...
Now that we’ve seen the trend in budgets and revenue, it’s obvious that companies are seeing the benefit of investing large sums as they create even larger returns. But what does this mean for movie popularity among viewers, as time goes on and budgets and revenues become increasingly large, are movie fans also benefiting from these huge investment...
Now looking at the same data but facetted, it’s clear to see the change in popularity of each genre over time, much clearer than the original scatter plot. Thinking analytically, you can see that movies released post 2010 are becoming increasingly popular across all genres, possibly due to modern advancements in technologies such as CGI as well as ...
Now that we have looked at genre success over the years, how can we further analyse the same set of data and tell a more descriptive story? Can we identify what companies perform best overall? With some more data processing and wrangling, we can create an object with the mean revenue, mean popularity and the number of movies released by each of the...
Our next steps were to analyse the entire data set using a unsupervised machine learning technique called K-means clustering, whereby R can arrange each movie based on mean scores for the numerical variables such as budget, revenue, popularity etc, into clusters or groups of similarity. Before doing so, we needed to create a data object of these nu...
These mean values can be seen below, byway of plotting a table using package `kable`. From this we can see what each cluster is in respects to this data. Cluster 5 being the most populated and with the lowest average scores for all of the metrics other than size. Cluster 2 seemingly the most ‘successful’ of the clusters, with high mean scores acros...
Now we have created a k-means cluster for movies based on their commercial success. While all of this information is rather interesting, it’s unclear as to what movies exactly fit inside of each of the five clusters. So with a little bit of wrangling using code in R we can assign each of the movies within the data set to their respective cluster. O...
What we’ve done here is, first experiment with some of the variables in order to find points of interest which may be calling out for further investigation. We figured commercial success was an area of particular interest so we proceeded to conduct an experimental analysis approach, we looked at budgets and revenues over time, noticing huge increas...
Aug 27, 2016 · Then, by matching movie contents with economic performance records, our original approach reveals that offensive contents like profanity or nudity may be a hindrance to achieve economic returns, while violent contents seems to enhance box-office revenues. Further research is needed to clarify the interaction in this regard between production ...
- Pedro Garcia-del-Barrio, Hugo Zarco
- 2017
Out of the variables tested in this study, only movie budget and net profit were found to have a significant correlation with domestic box office performance at a 5% significance level. Movie budget was chosen as one of the variables for the multiple linear regression model because budget can be used practically to predict box office earnings before a movie is released.
Dec 9, 2017 · The data set was originally gathered from IMDb and then sourced directly from Kaggle using 6,820 movies from 1986 to 2016 and includes details such as budget, gross revenue, the production company, country of origin, director, primary genre, movie name, motion picture rating, date released, runtime, IMBd user score, lead star, IMBd user votes, writer, and year released.
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Dec 18, 2015 · Movie Budget and Financial Performance Records Note: Budget numbers for movies can be both difficult to find and unreliable. Studios often try to keep the information secret and will use accounting tricks to inflate or reduce announced budgets. The data we have is, to the best of our knowledge, accurate but there are gaps and disputed figures.
