1. About

Analysing and presenting a 40k movie dataset.

1.1 The Dataset

• 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).

1.2 Libraries

• Pandas
• Numpy
• Matplotlib

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2. Findings

2.1 Release Date

2.2 Vote Average

2.3 Highest Revenue

2.4 Highest Budget

2.5 Highest Profit

2.6 Least Profit

2.7 Highest Return on Investment

3. Contact

Erol Gelbul - Website

Project Link: Movies' Data

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