Hello.
So before going into session, let's have a quick recap of what we have done in our capital now.
So this is exactly a basic banking and what not to do with it and when to use it and how to compute
value of basically using this cross-validation.
Then we have this use case that we have already solved using over Euclidean distance.
Then it is exactly the thing which is my probability.
Then we have this one distance and Manhattan distance.
You will see this as well, how Google Maps and this all.
I would like to know how, how, how exactly the after it what we have, we have this mathematical intuition
and happenstance after what we have.
We have also learned why it is an algorithm, why exactly the skin in this algorithm.
Then we have this case in which how you compute the distance between your categorical data.
Then we have told you this regarding this.
So in this session, we have to learn at what type of use and what type of real life use cases.
You can go ahead.
You can go ahead with this key and so can it.
It is highly used.
It is highly used in your recommender systems.
Whenever, say, whenever you have to recommend Lexi, Lexi, how Ms.
Yeah, let's let's talk about Amazon.
Let's talk about it.
So let's see how how this Amazon recommends a product.
Well, let's say whenever you are going to Browse on Amazon, how exactly Amazon recommends some product.
So they basically use this in their backyard.
Yeah.
So Violencia.
Yeah, definitely.
So approx, 35 percent of revenue, approx, 35 percent of revenue of Amazon is due to this recommender
system.
So just Amazon, just amazing how Amazon uses Gion and extensively just Amazon there.
So let's talk about our second real life example.
Let's say I have some documents.
Actually, I have some lecci, I have some number of documents and multiple documents, a letter that
has some news.
Let's say these are all the documents on the Internet to determine all the documents on the Internet.
And if if on this document, I have to find I have to find what are a document that contains similar
topics, that contain similar topics.
At the same time, I have to use this canon at that time.
I have to use this document extensively.
Lexis, let's say I'm going to search on Google, Executive Search and Google.
Yeah.
What is your or what is data science?
Let's say you will see you have some.
You have some.
You have some resources.
You have some.
You have some top and enquiry's.
Here, here, here, they use this they uses this cannon to showcase this result.
That's it, similarly, similarly in, let's say, in image recognition.
In image recognition, similarly in video recognition, highly used, highly used algorithm in this
type of use cases.
So let's talk about some advantages and disadvantages of you begin using again against it.
Yeah.
So let's let's let's talk about very first.
What are the pros of using Kinect?
Yeah, definitely.
It is used for both classification.
It is most used for classification and it is used for regression as well.
So we all have learned how how it works in case of classification.
So let's talk about let's talk about how how exactly it works in case of regression support or support.
I have this use.
Suppose I have this use case.
And here let's say this is my neighbors scattered over here.
Let's say this is my class, too.
This is my class.
Let's say this is my new data.
And I have to find to I have to find let's say let's this this is a use case of predicting the price,
predicting the price or predicting the price of flights.
Predicting the price of sweets, so what it will do, let's say I have some new data.
By the way, let's say I have some new data.
So what it will do, let's see if my value is three or five.
So what if my value of three of selected three?
So let's say it will consider three three Naess neighbors.
And basically it will it will mean of all these data points, it will basically take me off all these
data points.
So whenever you have regression, you give.
Whenever you have some regression, you just consider me and whenever you have some classification,
you go with mode or you can also consider your majority or your probability that's all about you.
So basically, one of the pros is it is used in both classification and recreation.
And one of the products that I really feel is it's mathematics behind the mathematics behind this algorithm.
Is that it?
Is it it's definitely it's too easy in comparison to all the other algorithms of machine learning.
And definitely one of the uses it is it is very easy.
It is very easy to implement as well, because you have a class you have a class in your in your in
your neighbors module, in your neighbors module of cyclone, in you best model of cyclone, you have
to just imported and you have to give your data and then you have to say what, what, what exactly
the value of it and you will get it.
Output.
That's it.
That's just like a piece of cake.
So it is definitely very easy to be very easy, very easy to implement.
So what are the pros?
Well, the boss definitely it will take not that much time, that much time in training phase.
It will take not that much time in training this.
So I can say definitely that as it is a lazy learning algorithm that we all have learned.
So, yeah, training wise, it will now take that much time, as has my all of the algorithms will be.
So definitely it is one of the advantages of Ken.
So let's talk about some of the counts.
From the Council of Canada so far, though, definitely one of the concepts here, it is not recommended
whenever you have some huge data from the council and yeah, it is not good for high dimensional data.
Yeah.
Whenever you have some high dimensional data, let's say you have some pandemic data.
So definitely you have to compute distance.
Imagine just imagine if your relatives.
Just imagine you have to come to the new one with each and every data point.
Let's see if this is pandemic.
Just imagine how much combat it and it will be.
That's why that's why it is not good for high dimensional data.
And yeah, it is.
It is it is definitely expensive in in testing fees or I can see it is definitely expensive in my prediction
purpose.
Definitely very, very expensive because it takes lots of resources and they are part of the this to
find out the value of care.
Yeah, so these are all my pros and cons of Gannon and how it is used exactly in case of regression
classification, so we have gold can add up to a greater extent.
So that's all about this algorithm as well.
Hope you love this session and this algorithm as well.
Thank you.
How nice to keep learning.
Keep growing.
Keep practicing.