Machine Learning Tutorial Python - 13: K Means Clustering Algorithm

K Means clustering algorithm is unsupervised machine learning technique used to cluster data points. In this tutorial we will go over some theory behind how k means works and then solve income group clustering problem using sklearn, kmeans and python. Elbow method is a technique used to determine optimal number of k, we will review that method as well.
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Code: github.com/codebasics/py/blob...
data link: github.com/codebasics/py/tree...
Exercise solution: github.com/codebasics/py/blob...
Topics that are covered in this Video:
0:00 introduction
0:08 Theory - Explanation of Supervised vs Unsupervised learning and how kmeans clustering works. kmeans is unsupervised learning
5:00 Elbow method
7:33 Coding (start) (Cluster people income based on age)
9:38 sklearn.cluster KMeans model creation and training
14:56 Use MinMaxScaler from sklearn
24:07 Exercise (Cluster iris flowers using their petal width and length)
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why do we not need to use centroids for the iris dataset?

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[@hshrestha2811]

Summarizing the algorithm for K Means clustering based on this video: 1. Start with k centroids by putting them at random points here k =2 2. Compute distance of every point from centroid and cluster them accordingly 3. Adjust centroid so they become center of gravity of given cluster 4. Again recluster every point based on distance with adjusted centroid 5. Reiterate until data points stop changing cluster 6. Again adjust centroids

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[@ganeshn9464]

Please correct me if I am wrong - 2. Compute distance of every point from centroid and cluster them accordingly - This entire process is "built" inside the KMeans.fit_predict(). Correct ?

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[@nilupulperera]

What a beautiful explanation. The beauty of Data Science is shown in this video in a remarkable way. The exercise is really beautiful! Thank you very much, Sir.

[@codebasics]

Dear Nilpul, Thanks for the comment. Keep learning. 😂

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I looked 5 min at start, and your teaching style for ML is spot on, better than the IIT professors. I am enjoying ML algo now. Thanks.

[@qas4273]

It's a blessing to be able to finally say that I can learn ML, thanks to you :). I have used 'HUE' from seaborn instead of writing plt.scatter for every group of the cluster. sns.scatterplot(df['Age'], df['Income($)'], hue = df['Cluster'])

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[@codebasics]

Yes, with seaborn you can do it in one line. Thanks for posting this comment 🙏🏼🙏🏼

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Fantastic explanation. I like the way you showed us what happens if you don’t scale your features. You also waited for the perfect opportunity to show why we need to use the elbow method.

[@codebasics]

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This is a great quick refresher for those with the basic knowledge of ML clustering algorithms

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[@codebasics]

glad you liked it :)

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[@codebasics]

Glad it helped you 😊

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This was awesome! I can't believe I learned how to do K-means clustering in just a few hours. Your explanations are clear and concise. Thank you so much!

[@codebasics]

Glad it was helpful!

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This is the best-explained K-means on the internet - period. Thank you!

[@codebasics]

Glad it was helpful!

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It was good, simple, informative, no errors. I guess these are enough clusters to define the quality of this video.

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Sir, you made Machine Learners life easy....amazing explanation that ever seen before and by Elbow technique we got K=3 for iris dateset.

[@codebasics]

Thanks for you kind words and I am happy you liked it 😊

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[@codebasics]

Glad you enjoyed it!

[@flyingsalmon]

@@codebasics Absolutely. I do have a follow-up question. I got lost after scaling the values...let me explain. I have a dataset of tips($) and total restaurant bill($) per day per group of customers. I got 3 clusters and they look good after rescaling the bill and tips via MinMax (my x-axis is the bill for food, and y-axis is tips $). But after clustering, I can't tell how to map the x-axis and y-axis rescaled values (which are 0..1 floats) to actual dollar values that are in the dataset. I need to know how the clusters map to real dataset. Is there a way to do that logically? Would really appreciate your input. TY!

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[@codebasics]

Suigeneris44, I appreciate your comment buddy. I am glad you found this to be useful :)

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How will I forget to give a thumbs up?🤣😂.The video was sooo useful and very simple but effective.I understood k-means clustering and Elbow methods.Thank you for posting such a useful video.

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Optimal value for iris data set got 3. Thank you very much for simple & great explanation.

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[@ericwr4965]

This was brilliant and I appreciate the explanation of the code. Question, once you get the clusters identified as you took age and income, what would you explain? Would you need the table as well to discuss the pattern as a supplement as otherwise we would just say we have three clusters, but what do they mean?

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[@codebasics]

Panda, thanks for your appreciation. I am going through some health issues and that's the reason not able to upload the content on regular basis. I will start uploading once I recover... 🙂

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[@codebasics]

Thanks Pooja for your kind words. This means a lot to me and gives me a fuel to continue my work. If you like my videos would you share this series and deep learning series on your facebook page, linked in or watsapp? That way maximum people can benefit from it.

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[@LamNguyen-nm1id]

both StandardScaler and MinMaxScaler worked for petal perfectly. The elbow had me at 2 and 3 but 3 seems to have a better variance than 2 upon plotting it in a scatter graph

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Thanks for your appreciation, sure when I am in India I will plan for a Meetup

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🤓👍

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Excellent quick and short explanation of K-means. Appreciate it

[@codebasics]

Glad it was helpful!

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👍👍😇

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Hi Dhaval ji - excellent video on KMC. Very precise in presenting, Particularly liked the cluster_centers_ and inertia_ concepts. The final elbow plot with for loop being the starting point was unparalleled in clarity. Thanks a lot

[@codebasics]

Thanks a ton

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Excellent tutorial sir. Thank you soo much.

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Supperrrrrrbbbb....These tutorials just made my life easy. It would be great if you can create different tutorials for the mathematical calculations for each of these models that we have learnt in this playlist. Keep Growing and keep teaching us!!

[@codebasics]

Sure I am planning to extend this series with more algorithms and ML techniques. Stay tuned.

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you are very strong .thank you so much for making this class easy

[@AlonAvramson]

Thank you! really enjoyed this session. I tried both Petal and Sepal and it went very well.

[@codebasics]

Glad you enjoyed it!

[@spacetimevideostudio109]

Thank you so much Dhaval for this video, with my elbow technique I got k=3. Thanks

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Really clearly explained video thanks!

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All your videos are clear and good. Congrats for that. Can you please make a video for recommender systems and NLP

[@codebasics]

Thanks thirugnanam, point noted!

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Thank you so much, it is very helpful, simple and clearly!

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Wow. You are a great man. You've made it soo simple to understand. Thank you Sir. 🔥🔥🔥

[@codebasics]

Glad you enjoyed it

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Thanks a lot for the video. You taught the K-Means Clustering in 10 mins!

[@codebasics]

👍☺️

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[@codebasics]

👍😊🙏

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Very clear. Thanks!

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Excellent material!

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its very helpful. Thank you so much!

[@zerostudy7508]

Cool other one to my list... i love cluster Mr. @codebasics in Exercise well i also found that we can (km.predict([[7,3]])), for which cluster category label of petal length =7 and petal width= 3 but labels of clusters gave is not constant, some time they give clusters as unique labels [0 1 2] or [2 0 1] but still the same clustering though only different Labels name but same result.

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great session! thank you.

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Thanks a lot, great as all the other videos

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The way explained, it really understandable... Keep uploading more n more videos on ML .. with Case Study .. Thanks in Advance

[@codebasics]

Thank you, I will