Unveiling Anime Preferences: A Data-driven Analysis using MyAnimeList API

Abstract

This research paper presents a data-driven analysis of anime preferences using the MyAnimeList (MAL) API. The objective of this study is to uncover valuable insights into the preferences of anime viewers by analyzing data obtained through MAL API. The paper begins with a comprehensive background that includes a detailed literature survey to understand anime recommendation systems. The methodology employed in this study involves utilizing the MAL API to collect and analyze data related to anime. The API provides access to a wide range of anime related information including ratings, ranks, genres etc. The collected data is then preprocessed to ensure its quality and suitability for analysis, involving data cleaning and transformation. Various data analysis techniques are applied to the preprocessed data. These techniques help identify patterns and trends in different fields such as genres, studios, media type etc. The findings from the data analysis provide valuable insights into the preferences of anime viewers and help to identify features that may be crucial while building the model. Then using cosine similarity model, content-based anime recommendation system is built after feature engineering. The results of this research highlight the importance of leveraging data-driven approaches in understanding and catering to the diverse preferences of anime viewers. By EDA uncovering patterns and trends in anime preferences, anime recommendation systems can be enhanced to provide more relevant recommendations to viewers. The implications of this research extend to the anime industry, where a deeper understanding of viewer preferences can inform content production and distribution strategies. In conclusion, this research paper presents a data-driven analysis and content-based recommendation system for anime using the MAL API and data science. The methodology employed involves data collection, preprocessing, Exploratory Data Analysis(EDA), Feature engineering and Model building. The findings contribute to enhancing anime recommendation systems and understanding the diverse preferences of anime viewers.