Hylan, I hope you have learned a lot from these supervised learning and unsupervised listening sessions
the Supervised Learning Project continues to produce.
The first project is a regression project and the second one is a classification project.
This particular review will give you an outline of how you should work on the project in case of supervised
learning.
As we already know, we have two types of variables.
The first one is the value which we want to predict, and the second one are the different values using
which we will learn certain ideas about the values and we will predict those.
So what is the data which we have here?
Here we have this entire dataset, which is about the whole state.
The target here is the price column, and here you can see we have around twenty one thousand rows of
data in this twenty one thousand rows of data.
We have various details about the prices, which is the value which you need to predict.
As you can see, this is a continuous value.
So this should be a regression from.
Now, there are a lot of columns which are present in this particular dataset.
The values being I.B., the bedrooms, number of bathrooms, the adult, the house, the waterfront
condition, if it is a waterfront, what is the condition level of the house?
What is the grade of the house?
And when it was built, when it was renovated, the zip code and so on.
Using all of these details, you need to create.
Features and then use them, as you can see, that there are no categorical columns pressing, you need
not convert anything into the tables, but you can consider these your renovation.
Zip code, as I've got to go to columns, I'm convert to renovation and find some relationship between
the renovation.
Similarly, you can find out some details about this date, what this date signifies.
So this is what you can do.
And similarly, here are some ideas now based on your learning, you would better know what you need
to do with this idea.
So now once you have this entire dataset, you will follow the process, which has been clearly defined
and described to you.
So you will find out different outliers and missing data, and in case there are certain outliers or
missing data presented, then you will try to impute the values to it.
Now, what values you should be imputing, you would have learned during the preparation.
Similarly, you can create new columns and using these techniques, which we have discussed.
You can remove the columns and do feature selection.
Then you will be applying different algorithms and comparing the performances.
We have very rewarded performances, not that you don't need to improve the model a lot.
The model improvement is a dome, which you need to know that even.
If a model you see at one point of time, is it the person using a linear model?
I'm using a random for this.
Let us see in case the eye could see changes to eighty eight point one, then it is not recommended
to use a random forest because we are not gaining a lot in terms of accuracy, but we are increasing
the complexity of the.
So always consider how much you're increasing the complexity of the model in terms of how much you are
gaining in terms of accuracy.
Same thing goes for stacking.
If you are random forest or you most orny, this model is giving you an IQ of see eighty eight point
six percent and a stacking model gives you be eight point seven percent.
Then go ahead and use the old model, the range of what is a used or whatever model you have used instead
of going for the stacking model, because again, the stacking model will be a highly complex model,
which you don't want to use.
OK, so these are a few things which you need to keep in mind and also remember, there is no great
or good model.
A model should be having a good accuracy.
And your target here is to learn how to implement the different algorithms.
So do not skip to any specifical automatically.
What I would recommend to you is to figure out the dataset and implement all the algorithms which you
have learned and improve each and every algorithm.
By this, you will learn how to fine tune different models and you will also learn how you can compare
different models.
During this entire course, I have used a lot of data sets, and this gives you an additional opportunity
to use those data sets for you for the learning.
So as a project, you can implement several algorithms on this particular dataset.
And after you are done with the project as a future learning, you can pick out any datasets which I
have shared and then start exploring them, stop walking on them and implementing different algorithms
on top of those datasets.
That is the reason why I have so many data sets so that at the end of the course you will have enough
data to practice on.
So I hope you will work hard on this project and improve the accuracy of the models and create great
models and have a bright future using this morning.
Thank you.