Since the 1930s, Walt Disney Studios has been captivating audiences with a diverse array of films spanning various genres. In this project, we will delve into Disney movie data to uncover how the popularity of these films has evolved over time. By conducting comprehensive analyses and hypothesis testing, we aim to identify the factors that contribute to a movie's box office success.
This project assumes a familiarity with data manipulation using pandas, basic visualization using Seaborn, and a working knowledge of statistical inference. We will employ two-sample bootstrap hypothesis tests for differences in means to make our conclusions.
We will begin by loading and exploring the Disney Character Success dataset. This dataset provides information about various Disney movies and their attributes.
Identifying the top ten highest-grossing Disney movies will offer insight into the kind of movies that tend to succeed financially.
Analyze the trend in movie genres over the years to understand how the audience's preferences have evolved.
Using Seaborn, we'll create visualizations to present the genre popularity trend over time.
To prepare our data for linear regression analysis, we'll transform and clean the dataset as needed.
We will conduct hypothesis testing to determine whether certain genres have a significant impact on a movie's box office performance.
Calculate confidence intervals for the regression parameters to quantify the relationships between movie attributes and box office success.
Continuing from the previous task, we'll compute additional confidence intervals for regression parameters to refine our understanding.
In the final step of calculating confidence intervals, we'll solidify our findings and conclusions about the factors influencing box office success.
Based on our analyses, we will provide recommendations regarding whether Disney should produce more action and adventure movies.
- Python
- Pandas (for data manipulation)
- Seaborn (for data visualization)
- Statistical inference concepts
- Basic knowledge of linear modeling in Python
- Familiarity with data visualization using Seaborn
By working through this project, you will gain valuable insights into the trends and factors that contribute to the box office success of Disney movies. Through data analysis, hypothesis testing, and visualization, you will develop a deeper understanding of how movie attributes relate to audience reception and financial performance.