K Nearest Neighbor classification with Intuition and practical solution

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In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification.

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This explaination is one of the most precise explanation that I have seen on Internet.

programmingpurpose

This is in fact well explained, defining every term, and assuming no previous knowledge. Thanks so much!

nelsondelarosa

Feature scaling (StandardScalar) should be applied after train test split. As it will not lead to information leak.

vaibhavchaudhary

Loved Ur videos and Ur taste in music..kadhal vanthale in the bookmark 😂❤️🔥

kiruthigan

Cool. Also finished my practice in Jupyter notebook. Thanks

sandipansarkar

No words about u r explanation sir, simple lucid way explanation

sivareddynagireddy

Thank you, you asked a question I had in my head, looking forward to applying the suggested solution, about imbalanced dataset...

Tapsthequant

Sir you are great inspiration to me. Thanks a lot for making every complex problem simpler.

TechnoSparkBigData

This is awesome! Thank you so much. I am working on a project at work for lead segmentation to help us find our "ideal lead" for our sales reps with a lot of very messy data. This is a great starting point. Quick question (might be a loaded question ha) - after we find these clusters, how do we go about seeing the "cluster profiles"? Or what all data points make up these clusters (in categorical form)

codyphillippi

congratulations krish on 1million subscribers🥳

padduchennamsetti

really its good... but you mentioned K=150, as per my knowledge we are not supposed to take even number. there might be chance of equal number of classes got selected nearest neighbor... algorithm may not be able to estimate the class for new record...

vishnu

All the best Superb Explanation you are a superb resource u will reach great heights continue ur good work

shyam

Thank you so much Krish for this great playlist. You are gem

shubhamsongire

Very well explained again. Thank you so much.

ijeffking

Very nice sir ur explanation and coding technique is very nice....
I am new learner of data science please keep uploading such video and new techniques of different kinds of algorithms which help us make easy to understand to deal with different kinds of datasets.

MaanVihaan

Thank You
This is a very helpful video

sazeebulbashar

Minkowski distance = (Manhattan) and (Euclidean)

joeljoseph

Sooper Explanation Krish. I have a doubt here.. When do we need to use MinMaxScaler() and when do we use StandardScaler()? Is there any difference? or we have to try using both and see which gives better results? Please clarify

vignesh

At 18:52, you said larger value of K will lead to overfitting, which is not true. Smaller value of K leads to overfitting. I think, if there are 2 K-values giving same error, we choose the one that is bigger because it is less impacted by outliers.

scifimoviesinparts

Thank you so much for sharing this information. I'd just one doubt sir if we will scale before train_test_split wont it be lead to data leakage? as during scaling process during fit when it consider average of all the data points it also take the value of test data set so my model will already have some hint regarding it??

ManashreeKorgaonkar

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