ANIME DASHBOARD

In this Project, we delve into the vast world of anime preferences, utilizing comprehensive datasets containing user ratings and anime details. Our primary objective is to analyze user ratings to understand the preferences and behaviors of anime enthusiasts. Through data exploration, we seek to uncover patterns, correlations, and factors influencing user ratings. Furthermore, we aim to identify popular genres, top-rated anime titles, and factors contributing to user engagement

CHALLENGES

Top Rated Anime: -

Which anime titles have the highest average ratings? Provide a visualization of the top-rated anime based on user ratings.

Distribution of Ratings:-

What is the distribution of ratings across all anime titles?

Genre Analysis:-

Which genres are the most popular among users? Analyze the distribution of genres and identify the most common genres among anime titles.

Type of Anime:-

What types of anime (e.g., movie, TV series, OVA) are most

prevalent in the dataset?

User Engagement:-

Which anime titles have the highest number of community members? Analyze user engagement by identifying anime titles with the largest community following.

Correlation Analysis:-

Is there a correlation between the number of episodes and the average rating of an anime? Explore the relationship between these variables using scatter plots or correlation analysis.

User Preference Analysis:-

Are there specific genres or types of anime that tend to receive higher ratings from users? Conduct a comparative analysis of user ratings across different genres and types of anime.

Recommendation Engine:-

Can we build a recommendation engine to suggest anime titles to users based on their preferences and ratings? Explore the feasibility of implementing a recommendation system using collaborative filtering or content-based filtering techniques.