This project involves cleaning, transforming, and analyzing an anime dataset sourced from Kaggle. The goal is to extract meaningful insights into trends, ratings, user engagement, and genre popularity in the anime industry. The analysis leverages R for data cleaning and visualization and provides a comprehensive understanding of patterns in the dataset.
The anime industry has grown significantly over the years, with a diverse range of genres, formats, and audience preferences. By analyzing this dataset, we aim to uncover:
- What genres are most popular?
- How do ratings and user engagement change over time?
- Are there significant differences between TV shows and movies in terms of ratings and popularity?
The dataset was sourced from Kaggle and includes:
- Anime Metadata: Titles, genres, episodes, ratings, and more.
- Viewer Data: Ratings and user engagement metrics.
Note: Due to licensing restrictions, the raw dataset is not included in this repository. You can download it directly from Kaggle: Anime Dataset.
- Data Cleaning: Scripts for cleaning and preparing the dataset for analysis.
- Exploratory Data Analysis (EDA): Visualization and analysis to uncover key trends and patterns.
- Visualizations: Graphs and charts created to summarize findings.
- R: For data cleaning, manipulation, and visualization.
- Kaggle: Dataset source.
- Git and GitHub: For version control and project sharing.
This donut chart illustrates the distribution of different genres within the dataset, providing a clear visual representation of the most to least common genres in anime. This helps in understanding the genre diversity and prevalent themes.
The line chart tracks user engagement over time, showing the number of active users per month. This visualization is crucial for identifying trends in viewer activity, such as spikes or declines, which could correlate with seasonal releases or particular events.
This graph provides an overview of the average ratings for anime, segmented by various criteria established in Part 1 of our analysis. It sets the stage for deeper exploratory data analysis and hypothesis testing regarding what factors influence anime ratings.
This visualization shows the popularity of different anime episodes, highlighting the episodes with the highest viewership. This insight can be useful for networks and creators to understand which episodes captivated the audience most.
This chart compares the popularity and viewership trends between TV shows and movies, providing insights into audience preferences between these two formats.
This plot reveals the trends in anime production over the years, showing the number of anime titles produced each year. This helps understand the growth or decline in anime production and potential market saturation.
This graph displays the average TV ratings for anime by year, allowing us to observe changes in quality or popularity over time and identify any potential patterns in audience reception.
