KNN Algorithm In Machine Learning | KNN Algorithm Using Python | K Nearest Neighbor | Simplilearn

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This KNN Algorithm in Machine Learningtutorial will help you understand what is KNN, why do we need KNN, and how KNN algorithm works using Python. You will learn how do we choose the factor 'K', when do we use KNN, with proper hands on demonstration to predict whether a person will have diabetes or not, using the KNN algorithm.

Below topics are explained in this K-Nearest Neighbor Algorithm (KNN Algorithm) tutorial:
00:00 Introduction to KNN(K Nearest Neighbor)
00:57 Why do we need KNN?
02:33 What is KNN?
03:51 How do we choose the factor 'K'?
05:46 When do we use KNN?
06:42 How does the KNN algorithm work?
09:19 Use case - Predict whether a person will have diabetes or not?

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When Do We Use the KNN Algorithm?
The KNN algorithm is used in the following scenarios:
✅Data is labeled
✅Data is noise-free
✅Dataset is small, as KNN is a lazy learner

Pros and Cons of Using KNN
✅Pros: Since the KNN algorithm requires no training before making predictions, new data can be added seamlessly, which will not impact the accuracy of the algorithm.
KNN is very easy to implement. There are only two parameters required to implement KNN—the value of K and the distance function (e.g. Euclidean, Manhattan, etc.)
✅Cons: The KNN algorithm does not work well with large datasets. The cost of calculating the distance between the new point and each existing point is huge, which degrades performance.
Feature scaling (standardization and normalization) is required before applying the KNN algorithm to any dataset. Otherwise, KNN may generate wrong predictions.

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Комментарии
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My professor took 1 hour to clear the basic concepts of KNN but I was unable to understand. Thanks for clearing my concepts in just under 15 minutes. Thanks a lot. Really appreciated. I am going to subscribe your channel. Thanks once again.

xunnygujjar
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I've been trying to understand this for weeks and you've summed it up in the first 2 minutes. Light bulb moment!

KrisYT
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These tutorials are easy to understand and informative compared to other videos😌Thanks:)

sinchanar
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Incredible video. I used it when I first heard of KNN to better understand it, and just used it to create my first model. You all are the best!

samsupertaco
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Great video, up until the point where you skipped the part where you show how to train the algorithm. One could argue thats the most important part...

raefmac
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This is an amazing video! I am trying to help my niece and have never read anything about KNN in my life but the way this video explains it is simply awesome! So thankful to you for creating this video as it would help thousands of students and those family members that want to help them learn properly. Do not understand why some professors can't seem to explain it so simply as you have! God bless you man!

lfreeordye
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Best data science video ever. so detailed and explanatory. very good for beginners. Please keep making detailed videos like this

ernestanonde
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I really really love how you broke down the topic and able to pass across all valuable information in a short while. Thank you

akintomiwaakinyemi
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We hope you enjoyed watching our video. The link for the dataset used in the video is provided in the description. Thanks!

SimplilearnOfficial
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Thank you for sharing this video it was very clear in detailed manner with example.
But I have a doubt in whether I to should take squareroot of n (n = Sum of Output of Distance Function) for calculating K.
5:11 (Choosing K)
8:55 (Choosing K=3)

shambhavisadhashivam
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Many thanks team, this leaa than 30mins clip saved me a couple of days to learn similar thing from some books and articles. Fantastic job :)

ghafoury
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My uni teacher made knn sucha scary thing to me... thanks God i found this vid...i m in love with knn now

Ajazahm
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20:32 Correction - standard scaler does not restrict the range of data between -1 to +1 . It converts the data to a mean of 0 and standard deviation of 1 . So if u take the mean of a standardised column ul find it equals 0. It basically skews the data to a smaller range and makes it comparable with other variables with different magnitudes which otherwise would not have been comparable. Min-max scaler (normalisation) restricts the data between 0 to +1.

amithnambiar
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Thanks alot that's very helpful, but when trying to use StandardScaler an error occurs
"ValueError: could not convert string to float"
i can't solve it, ahat shall i do ?
thanks in advance.

shoroukelsebaie
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Thank you very much for this tutorial, it has been very clear, it has helped me a lot for my first Machine Learning model. Awesome!!

west
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I'm totally new to any machine learning or programming stuff, and tbh I was super scared about learning -'K-NN algorithm' because the word 'algorithm' already sounded scary enough. However, your analogy of "Cats or dogs" and "claws and ears" REALLY MADE A LOT OF SENSE! If I have to lecture my own class about machine learning someday for other beginners, I think your "cat and dog" analogy of explanation cannot be more simpler. Thanks for the great video!

laguna
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Thanks for a *fantastic* video!!! may I ask - when determining K, why do you do the sqrt of y_test, rather than y_train (or x_train, which is the same length). In the video, it looks like you intended to do that, then for some reason - changed it...

gideonyuval
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Finally ! easiest video i found😅.Thanks simplilearn!!!

reaper
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One of the best explanations I have come across, thank you so much!

ganeshsubramanian
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This is the best explanation I have seen so far. Thank you.

andriibilych