Duration: 34:10

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After looking at the theory of this algorithm last week, this week we're implementing the DBSCAN algorithm from scratch using just the numpy library :)

This is another algorithm from the area of Machine Learning, where we learn to group elements of data together based on the features they have. This could be useful for example for customer segmentation and then you can give each group of your customers a more fitting recommendation or offer :)


Sound-wise this video features typing on my mechanical keyboard (I have a Razer with orange switches, which are similar to Cherry Brown switches), and soft spoken explanations of everything going on :)


Sorry for the whirring background noise, don't know what happened to my mic settings in this one and I wasn't able to completely remove it in editing :( I will try and have better audio again in the next one.


If you have specific things you would like to see in Python that would fit into a video, let me know in the comments!

Hope you enjoy some relaxing "Code with me"-content :)

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Me:
Instagram: https://www.instagram.com/chromacodeasmr/
GitHub: https://github.com/ChromaCodeASMR/

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Links:

iPad Theory video about DBSCAN: https://youtu.be/ljFXhk5n994

My Coding video playlist: https://www.youtube.com/playlist?list=PLr559pN7sNFRb4j_56WjfHSD2ie5aLteR

The Iris Dataset: https://archive.ics.uci.edu/ml/datasets/Iris/

You can also use it directly through the sklearn package like I did here: https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html