K-means Clustering From Scratch In Python [Machine Learning Tutorial]

In this project, we'll build a k-means clustering algorithm from scratch. Clustering is an unsupervised machine learning technique that can find patterns in your data. K-means is one of the most popular forms of clustering.

We'll create our algorithm using python and pandas. We'll then compare it to the reference implementation from scikit-learn.

Project Steps
- Write out pseudocode for the algorithm
- Code the k-means algorithm
- Plot the clusters from the algorithm
- Compare performance to the scikit-learn algorithm

Chapters

00:00 Intro
00:37 k-means overview
02:51 Loading in and cleaning FIFA data
06:11 Scaling the data
10:31 Initialize random centroids
14:20 Finding cluster labels for each data point
19:29 Update centroid values
23:30 Plotting k-means iterations
28:24 Pulling the algorithm together
35:25 Comparing our implementation to scikit-learn
37:56 Conclusion and next steps

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[MarianneHMiettinen]

Outstanding! Thank you, man! This really helped me do my masters thesis. I really appreciate that you explained every small step, and used as much visuals as possible, and focused on us being able to learn!
- In case others run into the same problem: With Scikit K-means, when using the fit(data) function, I got an "split" error message. (attributeerror: 'nonetype' object has no attribute 'split'). I checked my BLAS, and updated through conda all libraries, then shut everything down and opened again, and this resolved the problem, but it took a long time.

(I asked chatgpt for help)

[maleck]

Thank you, sir. This is how tutorials should be conducted: with in-depth explanations, step-by-step implementation, and the release of all code and datasheets to enable everyone to practice and advance their own personal projects. Congrats!

[animal]

This was amazing. Brilliantly explained, demonstrated and presented clearly. Helped me so much with my current bootcamp task. Thank you.

[stevenlomon]

From the bottom of my heart; thank you. This was so clear and easily understandable, fantastic video!

[TimHerrin]

Terrific implementation! I also really liked the way you used PCA for iteritive visualization... Nicely done

[Risewithvishwas]

Your explanation is absolutely clear. You have best knowledge. Keep posting new topics and encourage us ❤

[allaguimaouia]

it's very great job, the only one in youtube that explain every place of code 👍👍

[mo_l]

One of the best tutorials on the internet, thank you.

[elu]

This is a nice and powerful way to learn. Thanks for teaching.

[Silverwing_]

Absolutely fantastic
Would love a similar video on PAM clustering for mixed integer and categorical variables

[photoish]

I have never thought that we can visualize K means by using Dimension Reduction (PCA)!! Awesome Tutorial Sir

[jessemunson]

Awesome stuff, Vik. Thanks for sharing.

[tejasvinnarayan]

Amazingly clear! Thank you so much, Dataquest!

[sashagalanova]

very helpful and clear explanations - thank you!

[amandamorrow]

This THE best tutorial online. I am so grateful for this! Thank you

[krlwshu]

Great video. Really helpful looking at implementing it manually. Thank you so much

[TidianeDiallo-ss]

I can't thank you enough. Thank you for this content.

[hounddog]

Such good and clearly delivered material. Thanks a lot!

[oskeeg]

Thank you, thank you, thank you!!! Being able to perform and explain what runs under the hood is really important- I agree. Please keep these videos coming 🙌🏼❤️ The “From Scratch” series :)

[ytustatistics]

you might be a hero... thansk a lot for the contents...