Dataset

This dataset was created by Jenny Zhu

Released under Attribution 4.0 International (CC BY 4.0)

Contents

About this dataset

What are the trends in the Walt Disney Studios' box office data? How do certain characters contribute to the success or failure of a movie?

This dataset explores the box office data of Walt Disney Studios from 2000 to 2016. It includes information on the movie title, release date, main character, villain, and songs associated with each film. This dataset provides insights into how different elements contribute to a movie's success or failure

How to use the dataset

When exploring this dataset, there are a few things to keep in mind. Firstly, the data is divided into four different files: disney-characters.csv, disney_movies_total_gross.csv, disney-voice-actors.csv, and disney_revenue_1991-2016.csv. Each of these files contains information on different aspects of Disney's box office data.

In order to get the most out of this dataset, it is important to understand what each file contains and how the data is organized. For example, the file 'disney-characters.csv' includes information on the movie title, release date, main character, villain, and songs associated with each film. This data can be used to answer questions about how different elements contribute to a movie's success or failure

To make things easier, we've created a quick guide that outlines what you can find in each file:

disney-characters.csv: This file contains information on the movie title, release date, main character, villain, and songs associated with each film disney_movies_total_gross.csv: This file includes data on the total gross of Walt Disney Studios movies from 2000 to 2016 disney-voice-actors.csv: This file contains information on the voice actors for Disney characters disney_revenue_1991-2016: This file contains data on the revenue of the Walt Disney Company from 1991 to 2016

Research Ideas

1. Using the data to create predictive models that can suggest which film concepts are most likely to be successful
2. Determining which factors are most important to moviegoers when deciding whether or not to see a particular film
3. Finding similarities between films in Disney's back catalogue in order to make better decisions about what new films to produce

Columns

File: disney_revenue_1991-2016.csv | Column name | Description | |:------------------------------------|:----------------------------------------------------------------------| | Year | The year the movie was released. (Numeric) | | Year | The year the movie was released. (Numeric) | | Studio Entertainment[NI 1] | The studio entertainment segment of the Walt Disney Company. (String) | | Studio Entertainment[NI 1] | The studio entertainment segment of the Walt Disney Company. (String) | | Disney Consumer Products[NI 2] | The consumer products segment of the Walt Disney Company. (String) | | Disney Consumer Products[NI 2] | The consumer products segment of the Walt Disney Company. (String) | | Disney Interactive[NI 3][Rev 1] | The interactive segment of the Walt Disney Company. (String) | | Disney Interactive[NI 3][Rev 1] | The interactive segment of the Walt Disney Company. (String) | | Walt Disney Parks and Resorts | The parks and resorts segment of the Walt Disney Company. (String) | | Walt Disney Parks and Resorts | The parks and resorts segment of the Walt Disney Company. (String) | | Disney Media Networks | The media networks segment of the Walt Disney Company. (String) | | Disney Media Networks | The media networks segment of the Walt Disney Company. (String) | | Total | The total box office gross for the movie. (Numeric) | | Total | The total box office gross for the movie. (Numeric) |

File: disney-director.csv | Column name | Description | |:--------------|:-------------------------------------------| | name | The name of the movie. (String) | | name | The name of the movie. (String) | | director | The name of the movie's director. (String) | | director | The name of the movie's director. (String) |

File: disney_movies_total_gross.csv | Column name | Description | |:-----------------------------|:-----------------------------------------------------| | movie_title | The title of the movie. (String) | | movie_title | The title of the movie. (String) | | **relea...

This dataset provides information about user subscriptions and related details. It includes the following columns:

User ID: A unique identifier for each user.

Serial Number: Another identifier related to the user or their subscription.

Subscription Type: The type of subscription the user has (Basic or Standard).

Monthly Revenue: The revenue generated from the user on a monthly basis.

Join Date: The date when the user joined the subscription service.

Last Payment Date: The date when the user last made a payment.

Country: The country where the user is located.

Age: The age of the user.

Gender: The gender of the user.

Device: The type of device the user typically uses (Tablet, Mobile, Desktop).

Plan Duration: The duration of the user's subscription plan.

IMDB + OTT Platforms Movies Dashboard Dataset 🎬📊

About This Dataset

This comprehensive dataset combines movie and TV show data from major streaming platforms and cinema industries, designed for creating interactive analytics dashboards. Perfect for data analysis, visualization projects, and streaming industry insights.

🎯 What's Inside

Streaming Platforms Covered:

• Netflix (5,329+ titles)
• Amazon Prime Video (9,655+ titles)
• Disney+ Hotstar (1,321+ titles)
• Apple TV+ (167+ titles)

Cinema Industries:

• Hollywood Movies (10,000+ entries with box office data)
• Bollywood Movies (1,300+ entries with revenue insights)

🔍 Key Features & Analysis Potential

Content Analytics

• Genre Distribution: Analyze popular genres across platforms
• Release Trends: Track content release patterns over years
• Platform Comparison: Compare content libraries and strategies
• Geographic Analysis: Content production by country/region

Financial Insights

• Revenue Analysis: Box office performance tracking
• Budget vs Revenue: ROI calculations for movie productions
• Market Performance: Regional and global revenue comparisons

Quality Metrics

• Rating Analysis: User and critic scores across platforms
• Popular Content: Top-performing titles by platform
• Duration Patterns: Runtime distribution analysis

📂 Dataset Files

1. amazon_prime.csv

Amazon Prime Video content library with detailed metadata

Column	Description
show_id	Unique identifier for each show
type	Content type (Movie/TV Show)
title	Name of the content
director	Director(s) name
cast	Main actors/actresses
country	Production country
date_added	Date when added to platform
release_year	Original release year
rating	Content rating (PG, R, etc.)
duration	Runtime (minutes for movies, seasons for shows)
listed_in	Genre categories
description	Content summary

2. netflix_titles.csv

Netflix content catalog with comprehensive details

Column	Description
type	Content type (Movie/TV Show)
title	Name of the content
director	Director(s) name
cast	Main actors/actresses
country	Production country
date_added	Date when added to platform
release_year	Original release year
rating	Content rating (PG, R, etc.)
duration	Runtime information
listed_in	Genre categories
description	Content summary
day_added	Specific day when content was added
year_added	Year when content was added
month_added	Month when content was added
Genre Categories	Individual genre columns (Teen_TV_Shows, Horror_Movies, etc.)
User_Rating	User-generated ratings

3. disney_hotstar.csv

Disney+ Hotstar content database with family-friendly focus

Column	Description
title	Name of the content
description	Content summary
genre	Content category
year	Release year
age_rating	Age-appropriate rating
running_time	Duration in minutes
type	Content type (Movie/TV Show)

4. apple_tv.csv

Apple TV+ premium content collection with quality metrics

Column	Description
id	Unique content identifier
title	Name of the content
type	Content type (Movie/TV Show)
description	Content summary
release_year	Original release year
age_certification	Age rating classification
runtime	Duration in minutes
genres	Content categories
production_countries	Countries where produced
seasons	Number of seasons (for TV shows)
imdb_id	IMDB database identifier
imdb_score	IMDB user rating
imdb_votes	Number of IMDB votes
tmdb_popularity	The Movie Database popularity score
tmdb_score	The Movie Database user rating

5. hollywood_movies.csv

Hollywood box office and movie database with financial data

Column	Description
Title	Movie name
Date	Release date
Genre	Movie category
orig_lang	Original language
Revenue($)	Box office earnings in USD
Budget($)	Production cost in USD
country	Production country
score	Critical/User rating

6. bollywood_movies.csv

Bollywood film industry dataset with regional insights

Column	Description
title	Movie name
date	Release date
genre	Movie category
language	Primary language
revenue	Box office earnings
budget	Production cost
country	Production country
score	Critical/User rating

📊 Sample Insights You Can Discover

• Drama is the most popular genre across all platforms
• Significant content production increase post-2015
• ...

The dataset is meant for performing visualization activities. You are expected to explore data and plot various aspects of it to present relevant and meaningful information

| Header | Definition| |Do you typically check a daily weather report? | Yes or No | |How do you typically check the weather? | "The Weather Channel", "Local TV News", "Radio weather", "Internet search", "The default weather app on your phone", "Newsletter", "Newspaper", "A specific website or app (please provide the answer) A specific website or app (please provide the answer) If they responded this value for the second question, they were asked to write-in the app or website they used.| |If you had a smartwatch (like the soon to be released Apple Watch), how likely or unlikely would you be to check the weather on that device?| "Very Likely", "Somewhat Likely", "Somewhat unlikely", "Very unlikely"| |Age | 18-29, 30-44, 45-59, 60+| |What is your gender?| Female, Male| |How much total combined money did all members of your HOUSEHOLD earn last year?| $0 to $9,999, $10,000 to $24,999, $25,000 to $49,999, $50,000 to $74,999, $75,000 to $99,999, $100,000 to $124,000, $125,000 to $149,999, $150,000 to $174,999, $175,000 to $199,999, $200,000+, Prefer not to answer.| |US Region| New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain, Pacific. |

This dataset contains the data behind the story Where People Go To Check The Weather.. The data is copyright of Disney as mentioned on this site.