/Movie-Data-Analysis-Project-1-

Project 1: Explanatory Data Analysis & Data Presentation (Movies Dataset) Project Brief for Self-Coders Here you´ll have the opportunity to code major parts of Project 1 on your own. If you need any help or inspiration, have a look at the Videos or the Jupyter Notebook with the full code. Keep in mind that it´s all about getting the right results/conclusions. It´s not about finding the identical code. Things can be coded in many different ways. Even if you come to the same conclusions, it´s very unlikely that we have the very same code. Data Import and first Inspection Import the movies dataset from the CSV file "movies_complete.csv". Inspect the data.

Primary LanguageJupyter Notebook

Movie-Data-Analysis-Project-1-

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).