Project 1: Explanatory Data Analysis & Data Presentation (Movies Dataset)
Part 1 Complete the project by: Filter the Dataset and find the best/worst Movies with the
- Highest Revenue
- Highest Budget
- Highest Profit (=Revenue - Budget)
- Lowest Profit (=Revenue - Budget)
- Highest Return on Investment (=Revenue / Budget) (only movies with Budget >= 10)
- Lowest Return on Investment (=Revenue / Budget) (only movies with Budget >= 10)
- Highest number of Votes
- Highest Rating (only movies with 10 or more Ratings)
- Lowest Rating (only movies with 10 or more Ratings)
- Highest Popularity
Part 2
Find your next Movie Filter the Dataset for movies that meet the following conditions:
- Search 1: Science Fiction Action Movie with Bruce Willis (sorted from high to low Rating)
- Search 2: Movies with Uma Thurman and directed by Quentin Tarantino (sorted from short to long runtime)
- Search 3: Most Successful Pixar Studio Movies between 2010 and 2015 (sorted from high to low Revenue)
- Search 4: Action or Thriller Movie with original language English and minimum Rating of 7.5 (most recent movies first)
Part 3
Are Franchises more successful? Analyze the Dataset and find out whether Franchises (Movies that belong to a collection) are more successful than stand-alone movies in terms of:
- mean revenue
- median Return on Investment
- mean budget raised
- mean popularity
- mean rating
Part 4
- Most Successful Franchises
- Find the most successful Franchises in terms of
- total number of movies
- total & mean budget
- total & mean revenue
- mean rating
Part 5
- Most Successful Directors
- Find the most successful Directors in terms of
- total number of movies
- total revenue
- mean rating
Dictionary
Some additional information on Features/Columns:
- id: The ID of the movie (clear/unique identifier).
- title: The Official Title of the movie.
- tagline: The tagline of the movie.
- release_date: Theatrical Release Date of the movie.
- genres: Genres associated with the movie.
- belongs_to_collection: Gives information on the movie series/franchise the particular film belongs to.
- original_language: The language in which the movie was originally shot in.
- budget_musd: The budget of the movie in million dollars.
- revenue_musd: The total revenue of the movie in million dollars.
- production_companies: Production companies involved with the making of the movie.
- production_countries: Countries where the movie was shot/produced in.
- vote_count: The number of votes by users, as counted by TMDB.
- vote_average: The average rating of the movie.
- popularity: The Popularity Score assigned by TMDB.
- runtime: The runtime of the movie in minutes.
- overview: A brief blurb of the movie.
- spoken_languages: Spoken languages in the film.
- poster_path: The URL of the poster image.
- cast: (Main) Actors appearing in the movie.
- cast_size: number of Actors appearing in the movie.
- director: Director of the movie.
- crew_size: Size of the film crew (incl. director, excl. actors).