How that continue with our cost and in this video, we would always some explanation plus in the library
and data, so.
And you can say that we need to predict the market trends, depending the betting on the changes that
are occurring.
So to do this will run on neural network regression for what the data said.
So the median values of owner occupied home are predicted for the test data.
I said this right up there, no more properties of houses.
But I suppose any concern with modelling the price of houses in endorsable in house turn up dollars
and shot this year regression, predictive modelling.
So in both actually include features such a crameri the proportion of non retail business asheesh chemical
concentration and more.
So I did draw on the explanation in here, so because we are free, because you only have two hours,
so that's why instead of watching me typing everything for you so easy a.
Lieshout showed all the variables with the smallest disruption here, so there's a number of instances
is around five and six number of attributes, 14 continue at will, including the class attribute and
one binary value attribute.
So each of the detail attributes are listed in here.
The first one is Green, which he copied from Right by Tao.
The second one is that.
And what is the proportion of residential and John for loss over twenty five thousand square feet.
The next one in the US is a proportion of non retail business asheesh.
But how the next one is just a Charles River dummy variable, which is why I struck by the river zero,
which is otherwise.
The next one is a ox which needs to be oxidised concentration.
I think this what I did wrong, and we should be over.
Ten million.
So it is not over pa, pa.
Ten, and so this one is Arrium, which is the average number of rooms, but welding, which a proportion
of owner occupied units will be for 1940.
This is a way to distances to find some employment centers.
The next one is not indict of accessibility to the radio highways.
But Utah is a pupil teacher ratio about how Blackwood is one thousand with a, b, k.
Equals zero Victory Square, where Becae is the proportion of blacks, but how?
House set is a portion of blower's that placed on a population map is a median value of owner who buy
homes in one thousand dollar.
So on this map.
Is there is response variable wired to 13 variables are possible predictor.
So the goal of this.
Project is to fit a operation model that explain the variation in map.
So is there a relationship between the first 13 column and the map?
Variable variables?
Can we predict the manpower value based on the tutted in both columns that we set before?
The objective of this project is to predict the median value of owner occupied homes.
So the data is available in the data by name housing, not data from the UCI that as I said so.
To import the data we need to do dot.
We need to you read underscores yes module upon this library.
So these are DataDot.
Rolling on the radar.
And for us, we have to import a bandos library.
So let.
Import pandas as speedy.
So in this project where you will go, we will collapse.
This will be easier and from now on, Turei for any function containing a bandos library widjojo the
string beedi, as we have Jane bandos library name and the asset class can be added to do that.
So the bandos the library is an open source.
And it's very easy to you it operate a structure and operation to manipulate.
Numerical so.
The available data are not contained.
So is it necessary to retrieve the names of the variable is not contained in an audio file?
So let make the head of.
But we had to run the sale.
And then we're happy, Hesh.
Names equa.
Graham.
That and.
In us.
Just.
Norks.
I am.
I.
This.
Right.
Tocks.
B Trottier.
How's that?
Sorry, Mr..
And then the last one is a.
And this right now is important.
You are straight away so that one Azal.
We don't need any mistakes or be they show data that do data equal, we don't create and underscore
CISPA.
So you can buy straight.
You are.
The limb lies by a quarter to.
And then names equal the names so that one does hail and we do make any mistake, so it is how you imported
it.
So we zero it underscores that grey module of the bandos library.
So in this function, instead of the fine and we can also enter the complete stream.
You are provide content in the website respiratory.
So.
We have set to complete your hiring here and then this variable will bask in the sunshine and you say
that data, you will reward us by straight in the reflection and we have to order bar that had been
boss as well.
So which is the name and names.
So the first person, if or not, why would be use.
The second one is.
So this one.
Is he tied up with you, the wife's away or not?
The second one is specify a list of name to use and now we just playing the role of the frame to say
that we impose a carefully because there might be some mistake.
So just plain.
DataDot had 20 soldier.
And we did successfully bring the results are separate or.
In here, we got out, so I mean, we did impose essentially bought the library, and so that is the
end of this video.
I hope you enjoy it.
And I will see you in the next video.