Hi, this particular project is related to automobile domain.
So this one is a used car price prediction program.
So again, we will be dealing with a regression problem.
This particular dataset contains around
Orlac, twenty three thousand rows of data, and these data contains twenty five features of various
used cars and included devices.
Your task is to predict the price of the used cars in the future.
So let us go ahead here.
You can see that this particular price column is the target that you need to predict.
Next is the idea column, which again is not very much of use for you.
And then we have this Eurail, which contains the links for these cars.
You might not really be interested in these.
You are less.
Well, then we have various regions.
Region you are is whether we have the year in which the car was born, then the manufacture of the car,
the model, the condition, the number of cylinders, the fuel odometer and different details about
that.
Here we have different details which are present.
Then the image you all again, might not be much of use, but there is a lot of information presented
in this description column.
So what you can do is you can try, including this particular information by applying a little bit of
MLV on top of it, including various features using this particular description which is given here,
and then you can use it to predict the prices again.
You already know the drill.
The task here is to find out the prices.
So the first thing which you will be doing is you will get rid of the unwanted columns, find out the
important columns, create features from categorical columns to convert them into numerical columns.
Then you will have to convert this particular text again.
So you might think about converting this text into, again, different count vectors or dividing vectors
and then use that for a solution has also been given for this particular problem.
But I would suggest to work on this and then go to the project in case you need that.
So thank you.
And I would let you know about the next project once you're done with it.