🏆 Data Feeling | AIeron
IT предприниматель и препод 🧑🏫 ex-ML лидер в Dodo Brands 🦤🍕 Прокачиваю людей в Data Science 🚀 Победитель Stepik Awards 🏆 Kaggle Expert 🤹♀️ Создатель @Speakadora_bot @big_llm_course РКН https://clik.now/datafeeling Алерон @Ale_v2
Show more📈 Analytical overview of Telegram channel 🏆 Data Feeling | AIeron
Channel 🏆 Data Feeling | AIeron (@datafeeling) in the Russian language segment is an active participant. Currently, the community unites 14 701 subscribers, ranking 718 in the Marketing & PR category and 45 401 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 14 701 subscribers.
According to the latest data from 18 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -78 over the last 30 days and by 6 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 18.76%. Within the first 24 hours after publication, content typically collects 7.37% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 759 views. Within the first day, a publication typically gains 1 084 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 30.
- Thematic interests: Content is focused on key topics such as лот, n8n, бразилия, пет, санкция.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“IT предприниматель и препод 🧑🏫
ex-ML лидер в Dodo Brands 🦤🍕
Прокачиваю людей в Data Science 🚀
Победитель Stepik Awards 🏆
Kaggle Expert 🤹♀️
Создатель @Speakadora_bot @big_llm_course
РКН https://clik.now/datafeeling
Алерон @Ale_v2”
Thanks to the high frequency of updates (latest data received on 19 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Marketing & PR category.
features = list(set(features))Вроде бы красивый трюк, как удалить дубли в одну строчку. Однако, если запустить этот код потом снова, последовательность будет уже иная. Как следствие - разные результат модели. Последствия оказываются очень болезненными, если вы проверяете разные гипотезы и результат модели должен четко отражать наличие/удаление признака, а не более удачную последовательность. В общем, будьте осторожнее. Вместо set'а используйте лучше Numpy =)
features = np.unique(features)
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