Hello.
So before going ahead in this session, let's have a walkthrough on all our previous session from data
importing by doing law to the analysis, by doing lots of data processing on our data after we have
performed lots of speech and coding as well as our detection separating our independent features, feature
selection, lots of ping we have done in this project.
So in this session, what we have to do in the previous session, whatever automation we have done with
respect to this automation, we have to play with our multiple algorithms.
This is exactly the assignment for the station.
And the very first statement is you have to dump your model.
So what exactly is a meeting of this company and why?
There is a need, let's say, if you will notice over here, I have simply write a function over here
so that I don't have to do this task for each and every model so that I can automate my own stuff in
a similar way.
If you don't want to create your model again and again and don't want to do all this stuff, just do
dumping and save your model for somewhere else.
So whenever there's a need of that model just loaded using some fancy models of Python, that is exactly
what Bikal modules to just load that model and reuse it again.
So what exactly is inside in this model.
So model exactly contains a mathematical equation model.
Exactly.
Contains all the coefficient with respect to what our algorithm we are going to use.
So let's say if you really want to dump good model.
So here I'm going to say very first, you have to open some file for this.
I'm going to say I'm going to use some while handling techniques already.
So let's say you have to dump your model in some file.
So for this, I have to say I have to open some file in Rightmove.
So very first lesson, I have to open my file, let's say, over here.
So I'm just going to copy the spot from here and here.
Basically, I'm going to pasted.
So after it, we have to mention what can be done.
My model M, so my model is nothing but model dot l so dot pixel is exactly your extension because the
module that will come into picture whenever you have to load your model, whenever you have to dump
your model is exactly your typical model.
So I'm just going to import this pixel model over here.
After what we have to do after doing all these things, what do we have to do and say?
I'm going to say this is exactly my let's say this is my this is exactly my file.
And after this, what I'm going to do, I'm just going to dump using my Bikal model.
And you will see it has a function which is exactly the dump.
And using this function, what I have to do, I have to dump my model in this file.
That's what is the meaning of these parameters that I have.
Bussau, dear.
Let's say I had to mention some condition over here and let's say I don't want to dump model in each
and every case.
So for this, I am just going to add some simple conditionality or just mention it is equally close
to land.
Just dump all these stuff.
Similarly, over here, you have to pass a parameter which is exactly your dump.
And after what we have to do next, we have to play with that.
So we have to play with this for a signature.
Let's it yeah, I have to dump my models.
In such case, I can pass one or that's it.
So if again I'm going to execute it now, you'll see all of this stuff gets executed.
And if you will visit your part, you will see a filename which is exactly what Beaky l file having
will be an extension.
So which had that side and this is exactly the model here.
So this is exactly the model, whatever you have done or whatever you have done already.
So this is exactly the model that you need.
So let's move ahead with our next Pohlman statement in which I have to play with multiple algorithms.
Let's say you can play with this isn't rescan and linear regression and multiple regression algorithm.
That's all approved.
Whatever algorithm you just passed, that algorithm name in your function, that's literally all of
you.
So let's say I'm just going to play with X to the very first.
I'm going to say I have to import some algorithms or let's say I'm going to say the very first algorithm
that I'm going to consider is exactly my, let's say, linear regression.
And after it, I'm going to consider let's say I'm going to play with.
And so I'm going to say from as I could learn, I have to import these neighbors and here I have a class
which is my key neighbors, because it is a regression, you suggest, and after it, I am also going
to play with dissidents.
So I am going to say from this cycle on, I'm going to say, gee, I have to import my decision, tree
regulation.
So just like you ordered and in this Graddick function, you have to just pass this linear regression
and you have to initialize it as well.
So just execute it.
You will see or hear all of this stuff gets executed.
And this is exactly the situation in case of linear regression.
And if you are thinking why I have zero because I don't have to dump my model.
Similarly, you can play with the word like say I'm going to try and cry with my left decision.
I have to just call this function here.
I have to pass my decision tree.
Let's see.
I have to import my decision tree redressal.
So here I'm going to say this integer agressor here, I have to simply initialize my decision tree requested.
So you have to execute it as well.
And here you are just going to initialize this regression.
And I don't have my model.
It's all up to you.
So just execute it.
It will take a long time and you will see it had that much accuracy in case of decision tree.
It has that much autist, good gurpreet and all these different kind of matics in case of decision.
And all these are my predictions with respect to why this isn't the algorithm.
And this is exactly a beautiful distribution in case of decision.
And this is the distribution in case of linear regression that has somewhere around sixty three percent
accuracy.
So it's still you will notice my random forest because it has approx 80 percent accuracy.
Similarly, you can play with multiple algorithm.
That's all up to you.
Let's say I'm going to play in the station.
So nearest neighbor.
So I'm going to say Kinabalu regression.
I don't have to dump my model just by zero.
Just if you order, it will take up a lot of time and you will see it has somewhere around sixty six
percent accuracy.
So whenever your data set is huge, never go with Kinen because again, it is not advisable to get a
scientist whenever they are working on large data sets.
Similarly, you can play with your SVR and multiple regression algorithms that are available there.
So that's all about this session.
Hopefully will have the session.
Very much so.
Thank you.
Have a nice day.
Keep learning.
Keep going.
Keep practicing.