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Explanatory data analysis of anime ratings dataset 🕵🏻‍♀️

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What's this repository for? 🕵🏻‍♀️

What's in there? 🤔

It contains different tops:

  • Rating by happiness per minute
  • Rating using Kinopoisk TOP 250 formula (assuming that all users in dataset gave their rate in the last year)
  • Top of the most contradictory anime titles (includes Neon Genesis Evangelion and Sword Art Online)
  • Top of the most unambiguous titles (includes Kimi no Nawa and Fullmetal Alchemist: Brotherhood that is really logical in my opinion)
  • Top of the most popular genres
  • Top of genres with the highest rating (Comedy, School, Slice of Life)

Results 🏅

Top titles by happiness rate 🥰

English name Russian Name Rating Happiness per minute
Mitsudomoe Zouryouchuu! Отвязная троица 7.82 0.049
Arslan Senki (TV): Fuujin Ranbu Сказание об Арслане 7.70 0.048
Haikyuu!!: Karasuno Koukou VS Shiratorizawa Волейбол!!! Старшая школа Карасуно против академии Сираторидзава 9.15 0.046
Mushishi Zoku Shou 2nd Season Мастер муси 2 8.88 0.044
Mushishi Zoku Shou Мастер муси 8.80 0.044

Top titles based on Kinopoisk formula 🎞

English name Russian Name Rating New rating
Death Note Тетрадь смерти 8.71 7.79
Sword Art Online Мастера меча онлайн 7.83 7.78
Shingeki no Kyojin Атака титанов 8.54 7.78
Code Geass: Hangyaku no Lelouch Код Гиас: Восставший Лелуш 8.83 7.78
Angel Beats! Ангельские ритмы! 8.39 7.78

The most popular genres 📊

  1. Comedy (комедия) - 1️⃣ 1️⃣ %
  2. Action (экшен) - 9️⃣ %
  3. Drama (драма) - 8️⃣ %
  4. Shounen (сёнен) - 7️⃣ %
  5. Romance (романтика) - 5️⃣ %
  6. Supernatural (сверхъестественное) - 5️⃣ %

Future :shipit:

I'm looking forward to building a recommendation system using this dataset. Also, making a clusterization is in my plans. 🎯

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Explanatory data analysis of anime ratings dataset 🕵🏻‍♀️

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