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Analyzes a comprehensive IMDb database to uncover trends and insights in the film industry. Utilizes SQL for data querying and Python for data manipulation and visualization, focusing on movie genres, actor careers, and director contributions.

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Ashfadi/IMDb-Database-Analysis-Project

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IMDb Database Analysis Project

Overview

This project involves querying and analyzing a relational database of IMDb movie and TV episode data. The database includes millions of rows, covering movie titles, genres, directors, actors, ratings, and more. The project explores various questions through SQL queries to uncover insights into movie production trends, dual actor/director contributions, and career trajectories of actors and actresses.

Objectives

  1. Analyze movie production trends for selected genres over a specific time period.
  2. Identify actors who have also directed movies in a specific genre.
  3. Study the career trajectory of a specific actor/actress.

Tools and Technologies

  • DBMS: Oracle DBMS (Accessed via Omega at UTA).
  • Language: SQL for querying the database.

Analyses

Analysis 1: Movie Production Trends (2000–2015)

  • Query:
    SELECT COUNT(TITLETYPE) AS Movies_produced, GENRES, STARTYEAR
    FROM sharmac.TITLE_BASICS
    WHERE GENRES IN ('Crime', 'Thriller', 'Mystery') 
      AND TITLETYPE = 'movie' 
      AND STARTYEAR BETWEEN '2000' AND '2015'
    GROUP BY GENRES, STARTYEAR
    ORDER BY STARTYEAR;
    

Objective:

Identify the number of movies produced for each year (2000–2015) for three genres: Crime, Thriller, and Mystery.

Key Insights:

  • Thriller movies were produced at a higher rate compared to Crime and Mystery genres.
  • Movie production increased consistently across all genres during the period.

Analysis 2: Actors as Directors

  • Query:
    SELECT COUNT(TITLE_BASICS.TITLETYPE), TITLE_BASICS.ORIGINALTITLE, TITLE_BASICS.STARTYEAR, NAME_BASICS.PRIMARYNAME
    FROM TITLE_CREW_DIR
    INNER JOIN NAME_BASICS ON NAME_BASICS.NCONST = TITLE_CREW_DIR.DIRECTORS
    INNER JOIN TITLE_PRINCIPALS ON TITLE_PRINCIPALS.TCONST = TITLE_CREW_DIR.TCONST
    INNER JOIN TITLE_BASICS ON TITLE_BASICS.TCONST = TITLE_CREW_DIR.TCONST
    WHERE TITLE_BASICS.GENRES LIKE '%Crime%' 
      AND TITLE_BASICS.TITLETYPE = 'movie'
    GROUP BY TITLE_BASICS.ORIGINALTITLE, TITLE_BASICS.STARTYEAR, NAME_BASICS.PRIMARYNAME;
    

Objective:

Identify actors who directed movies within the Crime genre.

Example Result:

  • Orson Welles directed and acted in several movies, including Citizen Kane and The Lady from Shanghai.

Analysis 3: Career Trajectory of Emma Thompson

  • Query:
    SELECT TITLE_BASICS.ORIGINALTITLE, TITLE_BASICS.STARTYEAR, COUNT(TITLE_BASICS.TITLETYPE)
    FROM TITLE_PRINCIPALS
    INNER JOIN NAME_BASICS ON NAME_BASICS.NCONST = TITLE_PRINCIPALS.NCONST
    INNER JOIN TITLE_BASICS ON TITLE_BASICS.TCONST = TITLE_PRINCIPALS.TCONST
    WHERE NAME_BASICS.NCONST = 'nm0000668' 
      AND TITLE_BASICS.TITLETYPE = 'movie'
    GROUP BY TITLE_BASICS.ORIGINALTITLE, TITLE_BASICS.STARTYEAR;
    

Objective:

Analyze the career of Emma Thompson based on her movie appearances.

Key Insights:

  • Emma Thompson consistently appeared in movies from 1989 to 2017.
  • Peaks in her career were observed in 2013 and 2016, with three movies released each year.

About

Analyzes a comprehensive IMDb database to uncover trends and insights in the film industry. Utilizes SQL for data querying and Python for data manipulation and visualization, focusing on movie genres, actor careers, and director contributions.

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