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K-means using ‘seed-randint-numpy’

K-means classification

K-means, a simple Machine learning algorithm used for classification purposes. Machine learning is an awesome field, full of interesting topics and scenarios to study. While starting in this track one of the basics we study is classifiers, these things can be complex as hell and also easy as watching Tom chasing Jerry on television. I have implemented the K-means with a simple NumPy library of python 3. It’s a good start for someone who is started to learn or teaching this classifier. No Oop used, just some function call which intern makes some more function calls.


View the full code in my GitHub repository follow this link K-means notebook
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  1. Tanika Co Valda13 August 2026 at 08:25

    nice

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  2. Tanika Co Valda13 August 2026 at 08:31

    The article presents K-means as a simple machine learning classification approach and demonstrates how it can be implemented using Python 3 and NumPy. Keeping the implementation function-based without OOP makes it easier for beginners to follow the algorithm and understand how the different operations work together. A Numpy Course is therefore a good fit for building familiarity with the numerical tools used in this example.

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  3. Tanika Co Valda13 August 2026 at 08:31

    The practical notebook implementation also provides a useful starting point for experimenting with machine learning concepts through Python. Working with K-means helps learners understand how data can be grouped and analyzed programmatically, while also providing hands-on experience with data-processing workflows. These concepts can be further explored through a Data Analysis Course.

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  4. Tanika Co Valda13 August 2026 at 08:31

    The focus on K-means makes the example especially relevant for understanding clustering techniques and applying them to practical datasets. Starting with a simple two-dimensional implementation can make the core clustering process easier to visualize before moving toward more complex applications. Similar ideas can be extended through Clustering Projects.

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