Duration: 37:57

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Let's code together an implementation of the k-means clustering algorithm with Python on the Iris example dataset.


This is an 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 :)


TIMESTAMPS:

0:00 Intro
1:11 Project Setup
3:37 Data Import
8:16 Splitting the data
10:00 PCA and Plot
15:30 Random Center Starts
25:24 Assign Points to Centers
29:28 Compute new Centers
34:46 Putting it together



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 :)


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:

This code on GitHub: https://github.com/ChromaCodeASMR/K-means

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

The Iris Dataset: [https://archive.ics.uci.edu/ml/datasets/Iris/](https://archive.ics.uci.edu/ml/datasets/Iris/)

You can also use it directly through the sklearn package: [https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html)

The IDE/editor I use: https://code.visualstudio.com/