color,director_name,num_critic_for_reviews,duration,director_facebook_likes,actor_3_facebook_likes,actor_2_name,actor_1_facebook_likes,gross,genres,actor_1_name,movie_title,num_voted_users,cast_total_facebook_likes,actor_3_name,facenumber_in_poster,plot_keywords,movie_imdb_link,num_user_for_reviews,language,country,content_rating,budget,title_year,actor_2_facebook_likes,imdb_score,aspect_ratio,movie_facebook_likes Color,James Cameron,723.0,178.0,0.0,855.0,Joel David Moore,1000.0,760505847.0,Action|Adventure|Fantasy|Sci-Fi,CCH Pounder,Avatar ,886204,4834,Wes Studi,0.0,avatar|future|marine|native|paraplegic,http://www.imdb.com/title/tt0499549/?ref_=fn_tt_tt_1,3054.0,English,USA,PG-13,237000000.0,2009.0,936.0,7.9,1.78,33000 Color,Gore Verbinski,302.0,169.0,563.0,1000.0,Orlando Bloom,40000.0,309404152.0,Action|Adventure|Fantasy,Johnny Depp,Pirates of the Caribbean: At World's End ,471220,48350,Jack Davenport,0.0,goddess|marriage ceremony|marriage proposal|pirate|singapore,http://www.imdb.com/title/tt0449088/?ref_=fn_tt_tt_1,1238.0,English,USA,PG-13,300000000.0,2007.0,5000.0,7.1,2.35,0 Color,Sam Mendes,602.0,148.0,0.0,161.0,Rory Kinnear,11000.0,200074175.0,Action|Adventure|Thriller,Christoph Waltz,Spectre ,275868,11700,Stephanie Sigman,1.0,bomb|espionage|sequel|spy|terrorist,http://www.imdb.com/title/tt2379713/?ref_=fn_tt_tt_1,994.0,English,UK,PG-13,245000000.0,2015.0,393.0,6.8,2.35,85000 Color,Christopher Nolan,813.0,164.0,22000.0,23000.0,Christian Bale,27000.0,448130642.0,Action|Thriller,Tom Hardy,The Dark Knight Rises ,1144337,106759,Joseph Gordon-Levitt,0.0,deception|imprisonment|lawlessness|police officer|terrorist plot,http://www.imdb.com/title/tt1345836/?ref_=fn_tt_tt_1,2701.0,English,USA,PG-13,250000000.0,2012.0,23000.0,8.5,2.35,164000 Color,Andrew Stanton,462.0,132.0,475.0,530.0,Samantha Morton,640.0,73058679.0,Action|Adventure|Sci-Fi,Daryl Sabara,John Carter ,212204,1873,Polly Walker,1.0,alien|american civil war|male nipple|mars|princess,http://www.imdb.com/title/tt0401729/?ref_=fn_tt_tt_1,738.0,English,USA,PG-13,263700000.0,2012.0,632.0,6.6,2.35,24000
Based on the provided data, here is a table with the requested information about the movies:
| Movie Title | Director Name | Gross | Genres | Actor 1 Name | Actor 2 Name | 3 Name | IMDB Score | Budget | Language | Country | Title Year | |---------------------------|--|-------------|---------------------------------------||--------------|--------------|------------|--------------|----------|---------|------------| | Avatar | James Cameron | $760,505,847| Action | Adventure | Fantasy | Sci-Fi | CCH Pounder | Joel David Moore | Wes Studi | 7.9 | $237,000,000| English | USA | 2009 | | Pirates of the Caribbean: At World's End | Gore Verbinski | $309,404,152| Action | Adventure | Fantasy | Johnny Depp | Orlando Bloom | Jack Davenport | 7.1 | $300,000,000| English | USA | 2007 | | Spectre | Sam Mendes | $200,074,175| Action | Adventure | Thriller | Christoph Waltz| Rory Kinnear | Stephanie Sigman | 6.8 | $245,000,000| English | UK | 2015 | | The Dark Knight Rises | Christopher Nolan | $448,130,642| Action | Thriller | Tom Hardy | Christian Bale| Joseph Gordon-Levitt| 8.5 | $250,000,000| English | USA | 2012 | | John Carter | Andrew Stanton | $73,058,679 | Action | Adventure | Sci-Fi | Daryl Sabara | Samantha Morton | Polly Walker | 6.6 | $263,700,000| English | USA | 2012 |
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You are employed as a data scientist at SussexBudgetProductions. The last movie produced by the company was a comedy-action-thriller with a budget of £500K. It failed at the box office and only grossed £100K. So to keep the production company in business, the next film needs to make money. The CEO thinks they can persuade investors to give £1.5 million in funding over the next year, but needs to ensure a good return. Therefore, they have asked you to analyse the IMDB data provided in the movie metadata.csv Download metadata.csvfile and come up with an actionable plan. You should assume that all budgets in the data file have been converted to GBP.
This should be treated as a real report - you must imagine that you have this job and that I am your boss at the production company. Despite the presence of the code cells (a necessity of the module assessment) all other aspects should be like a report you would submit to your boss. Explain your reasoning, back up your claims with evidence. Provide valid, supported conclusions in natural language and give specific recommendations for actions. Reports full of typos, irrelevant or unreadable data plots, and without conclusions would get you fired; here they will receive failing grades (note this applies even more so to the second report that you will submit in January).
Your submission should contain details relating to the following three issues:
Exploratory data analysis you have carried out with the IMDB data. Hypotheses that you have designed relating to the overall aim of this assignment and the experiments that you have carried out to test whether they are supported by the data. Actionable recommendations based on the analyses you have conducted.
As the data scientist at SussexBudgetProductions, I have conducted an exploratory data analysis of the IMDB movie metadata provided to inform actionable recommendations. Here are the key findings and insights:
Exploratory Data Analysis:
Budget vs. Gross: After converting the budget and gross data GBP, it is evident that there is a substantial difference between the budget allocated and the gross earnings for the in the dataset. This indicates a common trend where the actual earnings at the box office do not meet the initial investment.
IMDB Score The IMDB scores vary across the movies, with some achieving higher than others. However, there is no direct correlation observed between the IMDB score and the gross revenue.
Genres: The movies in the dataset encompass a variety of genres, including action, adventure, fantasy, and thriller, demonstrating diversity in the company's production offerings.
Hypotheses and Experiments:
Hypothesis: The budget allocated to a movie significantly impacts its gross earnings. Experiment: a analysis between budget and gross earnings to evaluate the strength of their relationship within the dataset.
Hypothesis: Certain genres have a higher likelihood of generating substantial profits. Experiment: Analyze the average gross earnings for each genre and identify which genres have historically yielded higher returns compared to budget.
Actionable Recommendations:
2 Focus on Effective Budget Allocation: Utilize the findings from the analysis to optimize budget allocation for the next movie. It is essential to the historical trend of budget-to-gross ratio and make informed decisions to align with the goal of maximizing returns on investment.
In conclusion, employing a data-driven approach to genre selection, budget allocation, and investor communication will enhance the likelihood of producing a successful movie with substantial returns, thereby ensuring the sustainability and growth of SussexBudgetProductions.