Data Science & Machine Learning
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 76 021 subscribers, ranking 2 084 in the Education category and 4 135 in the India region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 76 021 subscribers.
According to the latest data from 28 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 719 over the last 30 days and by 4 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.75%. Within the first 24 hours after publication, content typically collects 1.22% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 092 views. Within the first day, a publication typically gains 930 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 29 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.
from sklearn.decomposition import PCA
import numpy as np
X = np.array([
[1,2],
[3,4],
[5,6]
])
pca = PCA(n_components=1)
X_pca = pca.fit_transform(X)
print(X_pca)
๐น 8. Advantages
โ Faster ML models
โ Reduces noise
โ Better visualization
๐น 9. Disadvantages
โ Hard to interpret transformed features
โ Possible information loss
๐น 10. Real-World Uses
โ Image compression
โ Face recognition
โ Big data preprocessing
๐ฏ Todayโs Goal
โ Understand dimensionality reduction
โ Learn principal components
โ Understand variance concept
๐ PCA = Compressing data intelligently ๐ฅ
๐ฌ Tap โค๏ธ for more!from sklearn.cluster import KMeans
# Sample data
X = [[1], [2], [10], [11]]
model = KMeans(n_clusters=2)
model.fit(X)
print(model.labels_)
๐น 6. Important Terms โญ
โ Cluster โ Group of similar points
โ Centroid โ Center of cluster
โ K โ Number of clusters
๐น 7. Choosing Best K (Elbow Method) โญ
๐ Elbow Method helps find optimal K.
The graph looks like an elbow ๐ป
๐น 8. Advantages
โ Simple and fast
โ Works well for grouped data
โ Easy to implement
๐น 9. Disadvantages
โ Need to choose K manually
โ Sensitive to outliers
โ Not good for irregular shapes
๐น 10. Why K-Means is Important?
โ Used in recommendation systems
โ Customer segmentation
โ Market analysis
๐ฏ Todayโs Goal
โ Understand clustering
โ Learn centroids & clusters
โ Implement K-Means
๐ K-Means = Finding hidden groups in data ๐ฅ
๐ฌ Tap โค๏ธ for more!
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