Hey, everyone, welcome back.
So the new project on which we are going to work up on that Ashton review classification and mainly
throughout this whole project.
We are going to use this and indicate lively.
All right.
So let me first input the data set and we see our business problem that what we are going to achieve.
And what is the classification?
We are going to do so from the file.
Just click on upload.
And let me go to the.
And before that stop.
Right.
I kept this restaurant and the could leave you start ESV Fine.
OK.
So you can see fine uploaded.
And I hope you did this whole project to four different videos.
So in this video, we'll be focusing on our business problem and importing all those later.
Next lecture is very much important because where we are going to do all those cleaning step, whatever
we learn in this NLB basic section, and then we are going to apply one very simple feature engineering
model, which is nothing but a bag of or model.
And then we'll be applying one classification algorithm, which is based on a base theorem, a nine
Navys algorithm.
All right.
So let me put all those necessary and required libraries, just as these were like Nubby Mike, Rutley
and Pangas Library.
And let me just add undescended.
All right.
So let's just import our restaurant review.
So we got.
And this could CSC and let just import a restaurant reviews, not VSP, fine.
So here the separate that will be again, Celesta.
And let me just run it.
All right.
So we are able to successfully lead this fight, but one more argument.
I want to push it.
Be.
Voting so that we just keep it.
So why that is because sometimes you may observe that, you know, place of such a kind of textual data,
you may encounter some level quotes.
So those double code, it needs to be ignored while reading this CSP or kind of PSV.
Fine.
Let me put all the data in to the data frame object.
Let's observe first few records.
And you will see we have every loser available and each and every reviews has been assigned to label
either zero or one.
So if you win here indicates that it is a positive review.
It is like by to use it.
And Yellow indicates he had the negative review.
User does not like it.
If you want to see data from the taling will get idea how many.
Go to Lacasse when a relevant.
All right, so you can see the total from zero to 999 Lachman's thousand recordset are.
Let's try to find out how many of them started one and how many of them are zero.
So later, like and we are going to play this.
Will you find this good KONE's?
So we hope Total find out the code side belongs to Class Zero and find it, the codes belongs to class
one.
That means it's a quiet balance, let's say.
And what object to force is to clean this all decks.
And in the next video, we will see how to clean all those texts and build the model, which eventually
going to put any weather review like this one.
Are not genetically the reviews positive or negative based on this review tax.
All right.
See you in the next video.