Un supervised learning -Clustering

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Unsupervised learning:

Unsupervised machine learning refers to the category of machine learning techniques where models are trained on datasets without labels. Unsupervised learning generally use to discover the patterns in data and reduce high-dimensional data to fewer dimensions. 

Here, I did work on some of the clustering algorithm using scikit-learn namely, 
  • KMeans, DBScan, Hierrarchial clustering.
  • Dimentionality reduction and manifold learning



Learning the Algorithm:

I Personally feel that data cleaning and Preprocessing are challanging than training the model. Once you finished those 80% of your work is done. Then you can play around with different type of machine learning algorithm. Each algorithms are effective on its own ways. I learned the clustering algorithms through the "iris" dataset in seaborn. 

Lets see some of my learning phase of unsupervised learning through Visualization:

KMeans:



DBScan:



 Hierrarchial clustering:


Dimentionality reduction:

Manifold learning:

Note: If you want to know more about the unsupervised learning check here. You may notice that plotly diagrams are not shown in the github. I prefer you guys to download and run the code you got output needed.

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