Introduction to kNN: k Nearest Neighbors Classification and Regression in Python Using scikit-learn

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Introduction to kNN: k Nearest Neighbors Classification and Regression in Python using sklearn with 10 fold cross validation

Hi there! I'm a Machine Learning PhD student based in Ireland. I am studying case-based reasoning and recommender systems techniques in sports science and marathon running. I mentioned how I actually use kNN a lot for implementing CBR since we can use the nearest neighbors as the cases retrieved from our case-base and use them to find a solution for our new case. This video provides a very basic introduction to k Nearest Neighbors which may not be too useful for people who are very familiar with machine learning. But a lot of people who watch my channel are not. I give examples of kNN classification and regression using sklearn, and I explain and use cross-validation. Next time I will be explaining how feature extraction and rigorous evaluation can be achieved for these types of algorithms. If you are interested, be sure to subscribe! I am posting 1-2 videos per week about ML/RecSys and the rest of my content is more general PhD.

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One of the best thing to do on this planet is to share knowledge in easier way.
Thanks! :)

revanthtv
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It will be highly appreciated if you add timestamps in your future videos.

youssefabdallah
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I logged in from my other acc just to like this vid once more. I especially liked the "You don't need to reinvent the wheel", thus vid being practical and apt.

kaustubhkeny
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why didnt you train or test the data set?

AR-hpjl
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Quick question by the way, my R2 value turned out to be negative. Any idea why? I'm using a heart disease dataset to practice if that matters.

TheVerbalAxiom
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This was the best explanation of using Knn for regression that I've found. Thank you for explaining it in away that is so easy to understand (both the concept and the code)!

kristinferraraccio
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Nice video, but I have one question you are using Mean Square Error for Classification but isn't it used for Regression? For Classification, we have the Accuracy and Confusion Matrix, right?

mohammedzia
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I LOVE how she explains literally every single bit of it. I've been searching for this kind of tutorial for a while, especially since I just started in ML. Thank you SO much. I hope you make new tutorials too! I just subscribed. Keep it up!

TheVerbalAxiom
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I'm working on a Localization project, and there's an application of weighted/normal KNN in it. This vid helped me with basics. Thanks a lot!

stlo
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Nice explanation, thanks alot for sharing

HasanNaser
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Great and very helpful video. Thank you!!!!

compasssolutions
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amazing, i hope you publish more, good luck

hussainsalih
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Hi thanks for the well-articulated teaching videos ! So much to learn from you in 25 mins. I have a little question here, regarding the R2 value. When you put the (r2_score(y, y_pred)), what does it compute actually? Does it compute the correlation between y & y_pred? or does it compute the correlation between your feature & your target? I am slightly confused...

berthaamelia
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Thanks a lot. It was really helpful. I appreciate you if you make more videos about Machine Learning algorithms especially neural networks.

yasamannazemi
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Hi i am from Pakistan and started my career towards Data Science. you have amazing content .

samdanisatti
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Thank u for your video! It was very helpful! Best regards from Argentina!

melinaluque
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can you do the program without using any existing implementation in python library. Like I have got assignment on this topis, but i am not allowed to use any other library other than math. I have been told to do from scratch. Any solutions n it????

rheajose
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This video was very helpful, concept was explained very well...

manitmaini
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Hey thanks for the help! Why did you use the entire dataset for the cross validation?

jeff_kola
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Thank you very much. A great video. Very clear and educative. I found it helpful. Keep it up.

stephenngumbikiilu