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Disney Movies and Box Office Success

Explore Disney movie data, then build a linear regression model to predict box office success.

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  • 10 tasks
  • 405 participants
  • 1,500 XP

Project Description

Since the 1930s, Walt Disney Studios has released more than 600 films covering a wide range of genres. While some movies are indeed directed towards kids, many are intended for a broad audience. In this project, you will analyze data to see how Disney movies have changed in popularity since its first movie release. You will also perform hypothesis testing to see what aspects of a movie contribute to its success.

This project assumes that you can manipulate data using pandas and can make basic plots using Seaborn. You should also be familiar with statistical inference and be able to perform two-sample bootstrap hypothesis tests for difference of means. The prerequisites for this project are Introduction to Linear Modeling in Python and Introduction to Seaborn.

The dataset used in this project is a modified version of the Disney Character Success dataset from Kelly Garrett.

Project Tasks

  • 1The dataset
  • 2Top ten movies at the box office
  • 3Movie genre trend
  • 4Visualize the genre popularity trend
  • 5Data transformation
  • 6The genre effect
  • 7Confidence intervals for regression parameters (i)
  • 8Confidence intervals for regression parameters (ii)
  • 9Confidence intervals for regression parameters (iii)
  • 10Should Disney make more action and adventure movies?
Instructor Avatar
Sirinda Palahan

Assistant Professor at University of the Thai Chamber of Commerce

Sirinda is an assistant professor in the School of Science and School of Business. She also is the head of a bachelor’s degree program in Big Data Management for the School of Business. She has a Ph.D. degree in Computer Science and Engineering from Pennsylvania State University. Her main interests are data analysis and big data for business.

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Technology

  • Python LogoPython
  • Topics

    Data ManipulationData VisualizationProbability & StatisticsImporting & Cleaning Data