Our hypothetical Company has announced that they would like to get into the movie industry. They will be creating a studio, however they have no knowledge of the movie industry. My goal is to collect, clean, and analyze movie data from a variety of sources so that I can provide recommendations to the company that will allow them to be successful in the movie industry.
pandas, numpy, seaborn, matplotlip, datetime
The web-scraped data used in this project was collected from the following sources:
https://www.the-numbers.com/movie/budgets/all/1
https://www.imdb.com/search/title/?title_type=feature&num_votes=5000,&languages=en&sort=boxoffice_gross_us,desc&start=1&explore=genres&ref_=adv_nx
https://www.boxofficemojo.com/chart/most_theaters/?by_studio_type=major
https://www.moviefone.com/movies/2019/?page=1
https://en.wikipedia.org/wiki/List_of_Academy_Award-winning_films
In my analysis I explore and answer the following questions:
i.) What are the most profitable movies and how much should you spend?
ii.) Which movie genres are most commonly produced and does quantity equate to higher net profits?
iii.) What is the best time of the year to release a movie?
iv.) Which actors and directors tend to add the most value?
v.) How much money should you spend to win an Oscar?
vi.) What impact, if any, does runtime and movie rating have on Net Profit, Profit Margin and IMDb rating?
vii.) Sticking to our analysis of Net Profit and Profit Margin, what should our Company determine to be the baseline for sustainable success?
viii.) Based on the success of current competitors, which should we look to for best practices?