Anime DataDive - A Data Driven Explosion of Anime
Inspired by my deep passion for anime and data analytics, I undertook an anime data analysis project. This initiative aimed to unearth viewer preferences, industry trends, and success factors in anime, bridging the gap between entertainment and data-driven insights .
Objective: Utilized Python libraries for data cleaning, SQL for data storage, and Tableau for visualization. The dataset was sourced from Kaggle and meticulously cleaned.
Performance Analysis: Investigated the relationship between 'Score' and anime 'Duration' to identify patterns.
Audience Engagement and Popularity: Examined top-rated anime titles by 'Score' and explored correlations between 'Ranking,' 'Score,' and 'Members.'
Content Analysis: Determined the most common 'Type' of anime and identified prevalent 'Genres' within the dataset.
Key Outcomes: Uncovered trends and correlations involving 'Score,' 'Members,' and various attributes, offering valuable insights into audience preferences and engagement. Detailed the impact of 'Genres,' 'Themes,' and 'Producers' on anime popularity, providing crucial insights for content selection and marketing strategies.
This project exemplifies my proficiency in data analytics, data cleaning, and data visualization, showcasing my ability to draw meaningful insights from complex datasets in the context of my passion for anime.
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