This project explores the dataset containing information about Walt Disney movies, focusing on factors such as genre, MPAA rating, and total gross revenue. The dataset used in this analysis is sourced from Kaggle.
This Python 3 environment is equipped with several helpful analytics libraries such as NumPy, Pandas, Seaborn, Matplotlib, and Missingno. These libraries facilitate data manipulation, visualization, and analysis.
The dataset is read from the provided CSV file (disney_movies_total_gross.csv). The initial inspection of the data includes viewing the first few rows and assessing missing values using visualizations and statistics.
The dataset contains the following columns:
- movie_title: Title of the movie.
- release_date: Release date of the movie.
- genre: Genre of the movie.
- MPAA_rating: MPAA rating of the movie.
- total_gross: Total gross revenue generated by the movie.
- inflation_adjusted_gross: Total gross revenue adjusted for inflation.
Missing values are observed in the genre and MPAA_rating columns. These null values are handled appropriately for further analysis.
- Genre Distribution: The distribution of movies across different genres is analyzed. The most prevalent genres in Disney movies are Comedy, Adventure, and Drama.
- Total Gross by Genre: A bar plot is generated to visualize the total gross revenue for each genre, providing insights into which genres are more commercially successful.
This README provides an overview of the analysis conducted on the Walt Disney movies dataset. Further exploration and analysis can be performed based on the insights gained from this initial investigation.