All right, so last step, we know a restaurant review classification project is applying some machine
learning classification algorithm.
Now, before that, we have to split up a site and forte as well.
You know, earlier projects are sort of really like a train split.
So from model selection model of this scale.
And we are going to split this into X, train X, this white linen like this.
And I just kept this size 20, wasn't it?
So out of thousand, record twenty was in the job record.
We'll go to testing buckets and remaining 80 percent leading to raining buckets.
Let's just keep a random street detail just to recreate the same result.
The Orango.
So.
All right.
Let me display the shape of this X and this train.
And extends this dog --.
Offsets a small little.
So there will be a 800 records assigned to cleaning buckets and 200 records assigned to testing buckets.
And let's see for Hawaii and the so-called green dot ship came by, underscore this dog ship.
So there will be 800 tacos and 200 tacos next.
We are going to apply this night based algorithm and that will be a part of this.
And be Haskell Library.
So from his Gaylan.
Right now, you base, you are going to import this glassine andI.
Let's create the object of this CNN.
Let me say I need to classify it, and next is we are going to do the training on our training dataset.
So it will be classified not.
That will be a fit matter.
Not a trained matter.
So send us your train.
And I understood three.
And let me have any hopes that we'll be a fit.
All right.
So Michael got God.
Let's have a prediction on our training set.
So I'm just going to assign it to why not put any prediction will be on a.
And the school hopes that will be on the center's good test data.
So, Ryan, this data will be our ground truth, whereas Minder's Cooperative will be of a prediction
result, which will be predicted by this Goslee and Nairobi's classified.
All right, so let's find accuracy for this.
Classification model after applying on a testing because.
So that will be from Haski Lone Dog.
Metrics import, let's say accuracy score.
Hey, I'm just going to apply.
Accuracy is good.
So that will be a plus lightest.
That will be a low ground troop level and wideness could.
Right.
Let's just run it.
And it indicates that almost 73 percent accuracy.
That means out of 200 records, if we just multiplied 200 Pequots by zero point seventy three, which
will be close to around one hundred and forty six.
So in one hundred and forty six, that of a prediction was right.
Whereas another 50 for records of a prediction was wrong.
All right.
So that is the whole story of the restaurant review classification dataset.
And in this particular section, we learn about all those pre processing step leiker stamping stopwork
removal, lowering your every single token removal of all those digits, punctuation mark.
And these are the minimum thing you need to do while applying your data to the machine learning by any
feature engineering algorithm.
Although this particular naglfar model also has an a capability to do all those things.
Whatever we did earlier.
All right.
So that is all about the Restaurant Review Classification Project.