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Data Science & Machine Learning
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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
Show more๐ Analytical overview of Telegram channel Data Science & Machine Learning
Channel Data Science & Machine Learning (@datasciencefun) in the English language segment is an active participant. Currently, the community unites 75 822 subscribers, ranking 2 109 in the Education category and 4 254 in the India region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 75 822 subscribers.
According to the latest data from 20 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 833 over the last 30 days and by 1 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.15%. Within the first 24 hours after publication, content typically collects 1.15% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 391 views. Within the first day, a publication typically gains 875 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as learning, accuracy, distribution, panda, dataset.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โJoin this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_dataโ
Thanks to the high frequency of updates (latest data received on 21 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 Education category.
75 822
Subscribers
+124 hours
+1047 days
+83330 days
Posts Archive
1. What is the primary difference between R square and adjusted R square?
In linear regression, you use both these values for model validation. However, there is a clear distinction between the two. R square accounts for the variation of all independent variables on the dependent variable. In other words, it considers each independent variable for explaining the variation. In the case of Adjusted R square, it accounts for the significant variables alone for indicating the percentage of variation in the model. By significant, we refer to the P values less than 0.05.
2. What is the curse of dimensionality?
Curse of Dimensionality refers to a set of problems that arise when working with high-dimensional data. The dimension of a dataset corresponds to the number of attributes/features that exist in a dataset. A dataset with a large number of attributes, generally of the order of a hundred or more, is referred to as high dimensional data. Some of the difficulties that come with high dimensional data manifest during analyzing or visualizing the data to identify patterns, and some manifest while training machine learning models. The difficulties related to training machine learning models due to high dimensional data are referred to as the โCurse of Dimensionalityโ.
3. What are some Stopping Criteria for k-Means Clustering?
a. Convergence. No further changes, points stay in the same cluster.
b. The maximum number of iterations. When the maximum number of iterations has been reached, the algorithm will be stopped. This is done to limit the runtime of the algorithm.
c. Variance did not improve by at least x * initial variance
4. What are hard margin and soft Margin SVMs?
Hard margin SVMs work only if the data is linearly separable and these types of SVMs are quite sensitive to the outliers. But our main objective is to find a good balance between keeping the margins as large as possible and limiting the margin violation i.e. instances that end up in the middle of margin or even on the wrong side, and this method is called soft margin SVM.
Sharing 20+ Diverse Datasets๐ for Data Science and Analytics practice!
1. How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
2. Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
3. Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
4. Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
5. Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
6. Iris Dataset: https://archive.ics.uci.edu/ml/datasets/iris
7. Titanic Dataset: https://www.kaggle.com/c/titanic
8. Wine Quality Dataset: https://archive.ics.uci.edu/ml/datasets/Wine+Quality
9. Heart Disease Dataset: https://archive.ics.uci.edu/ml/datasets/Heart+Disease
10. Bengaluru House Price Dataset: https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data
11. Breast Cancer Dataset: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29
12. Credit Card Fraud Detection: https://www.kaggle.com/mlg-ulb/creditcardfraud
13. Netflix Movies and TV Shows: https://www.kaggle.com/shivamb/netflix-shows
14. Trending YouTube Video Statistics: https://www.kaggle.com/datasnaek/youtube-new
15. Walmart Store Sales Forecasting: https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting
16. FIFA 19 Complete Player Dataset: https://www.kaggle.com/karangadiya/fifa19
17. World Happiness Report: https://www.kaggle.com/unsdsn/world-happiness
18. TMDB 5000 Movie Dataset: https://www.kaggle.com/tmdb/tmdb-movie-metadata
19. Students Performance in Exams: https://www.kaggle.com/spscientist/students-performance-in-exams
20. Twitter Sentiment Analysis Dataset: https://www.kaggle.com/kazanova/sentiment140
21. Digit Recognizer: https://www.kaggle.com/c/digit-recognizer
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๐ป ๐๐ฒ๐ฟ๐ฒ ๐๐ผ๐ ๐ฐ๐ฎ๐ป ๐น๐ฒ๐ฎ๐ฟ๐ป :- ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด, ๐๐ฎ๐ฐ๐ธ๐ถ๐ป๐ด, ๐๐ฟ๐ฎ๐ฐ๐ธ๐ถ๐ป๐ด, ๐ช๐ฒ๐ฏ ๐ฑ๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐, ๐๐ฝ๐ฝ ๐ฑ๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐, ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด, ๐๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ถ๐ฎ๐น ๐๐ป๐๐ฒ๐น๐น๐ถ๐ด๐ฒ๐ป๐ฐ๐ฒ, ๐๐ฒ๐ฒ๐ฝ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด, ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ, ๐๐ถ๐ด๐ถ๐๐ฎ๐น ๐ ๐ฎ๐ฟ๐ธ๐ฒ๐๐ถ๐ป๐ด, ๐๐ฟ๐ฎ๐ฝ๐ต๐ถ๐ฐ ๐ฑ๐ฒ๐๐ถ๐ด๐ป, ๐๐ป๐ถ๐บ๐ฎ๐๐ถ๐ผ๐ป, ๐ฉ๐ถ๐ฑ๐ฒ๐ผ ๐ฒ๐ฑ๐ถ๐๐ถ๐ป๐ด, ๐ฃ๐ต๐ผ๐๐ผ๐ด๐ฟ๐ฎ๐ฝ๐ต๐, ๐ฃ๐ต๐ผ๐๐ผ๐ ๐ฒ๐ฑ๐ถ๐๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐บ๐ฎ๐ป๐ ๐บ๐ผ๐ฟ๐ฒ ๐น๐ผ๐๐ ๐ผ๐ณ ๐๐ต๐ถ๐ป๐ด ๐ถ๐ป ๐ณ๐ฟ๐ฒ๐ฒ ๐๐
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๐ ๐ฐ๐น๐ฒ๐ฎ๐ป ๐น๐ถ๐ฏ๐ฟ๐ฎ๐ฟ๐ ๐ณ๐ผ๐ฟ ๐ด๐ฒ๐ฒ๐ธ๐.
๐๐ฒ๐ ๐๐๐ด ๐๐ผ๐๐ป๐๐, ๐ก๐ฒ๐๐๐ผ๐ฟ๐ธ๐ถ๐ป๐ด, ๐๐๐ต๐ถ๐ฐ๐ฎ๐น ๐๐ฎ๐ฐ๐ธ๐ถ๐ป๐ด, ๐๐๐ฏ๐ฒ๐ฟ๐๐ฒ๐ฐ๐๐ฟ๐ถ๐๐, ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด & ๐น๐ผ๐ ๐บ๐ผ๐ฟ๐ฒ ๐น๐ฎ๐๐ฒ๐๐ ๐๐ฒ๐ฐ๐ต๐ป๐ผ๐น๐ผ๐ด๐ ๐ฏ๐ฎ๐๐ฒ๐ฑ ๐ฒ๐๐ผ๐ผ๐ธ๐.
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