Hi, I hope you have worked on this first project, which is the house price prediction problem, the
next project which we would work under supervised learning, is, again, a regression project which
is based on the property hazard and the school prediction.
So in this particular project, we will be using supervised learning and regression.
And this is an industry level project.
So the data center would be a little more typical in nature and a little complex in nature in comparison
to the previous one.
So here we would be given a mass dataset which contains around forty thousand rows of properties and
thirty for various dots.
That is thirty for various columns of data.
So this particular dataset deals with a problem which is property hazard problem.
So it says that there are different properties in a particular location and one particular organization
is trying to do a domain Damiens of these properties.
They are trying to restore the properties.
Now, when this organization takes up the task of restoring this property, so there are certain hazards
which are associated with the property which is being restored, there could be a case where the property
has zero risk and it is completely fine to restore it.
Well, there could be a few properties where it would be very difficult to restore the property and
there could be different hazards which could be possible.
Or maybe the company might lose a lot of money by investing in that property.
And still, it would not be good enough for selling to someone else.
So that is a different risk level which will be associated with these properties.
So here we have around forty thousand eight hundred properties and could be four different task authority
for different risk factors associated with different things to which they are creating a generalized
single risk score.
So this risk score is actually depending on these thirty four different dust right now.
These are for taking up in different heavy equipment maintenance by the manual.
Also, the manual crew is actually doing these performing these days.
So each of these days have been given a hazard score, which is eventually used to decide the level
of safety checks and caution while planning for the maintenance process of these properties.
So your task here is to build a predictive model for predicting the hazards, given other information
related to the main building, that school may have given these 30 foot columns which have different
maintenance task and did different properties.
And out of that, you need to make sure that you create a model which is able to find out accurate risk.
Now, one thing to note here is that the risk score is not a continuous value.
So what we have been dealing with till now are continuous values.
So now where does this go?
Should this be taken up as a classification problem or regression problem?
But because these are actual numbers and a lot of numbers, so it's not fit to be a classification problem.
That is why you will be using a regressive it.
So, for example, what you can do is you can use the objective function, which is counterpoise.
So counterpoised is actually will be helpful in finding out in applying regression back for the numbers.
That is something which will not have the floating point values.
So you will be dealing with something which is in a whole number or integer form.
Right.
So we can use that.
Apart from that, this project for this project, you will be working with me.
Absolutely.
So what you can do is you can find out the mean absolute error for the modules which you have created
and you can find out the score as one minus the mean absolute error divided by a five point four.
You don't need to really think much about how this coding is happening.
You just need to make sure that the score, which you get after this particular calculation comes out
to be more than zero point five.
And that would be a good model, which you would have generated.
So, again, there is no specific rule or no specific threshold value that your model has to be top
notch or should have an accuracy of this or.
I told this, I mean, absolutely nothing like that.
This is the minimal point that the school has to be at least 2.5.
That is greater than zero point five.
And apart from that, there is no limit.
You can keep improving your model, implement different models, compare different models, stick them
together, do whatever you feel like and build your borders.
And again, my model, which I would be sharing as the code, is just one of these solutions.
There is no absolute perfect solution.
You can try at your own to create your own good solution for that.
OK, so thanks.
Please work on this particular model, building this particular problem and I will be sharing the solution
in the next video for this one.
Thank.