Now, let us discuss about the second project, which we have, the second project is for classification
problem.
For this project, again, you will have to be outliers, find the missing values, create new columns,
find relevant columns and try different algorithms.
But now the metrics which you will be using will be different.
And another thing which you will have to keep in north is the classes in this particular date.
This is the date which we have and this particular data we have around.
Thirty two volumes and out of these two columns did not get value is plus, as you can see, the count
of the number of rows is Doleac eighty four thousand eight hundred and eight, while the sum of the
glass value is for nine people, which clearly shows that there are a large number of values as well
and only a few rows of data which have one person in them.
And this dataset is a credit card fraud dataset, which clearly shows that most of the users are genuine.
While only two hundred and eighty four examples are there, which are all fraud data.
I'm here.
I mean that it will be to find out these fraudulent data points correctly.
Hence we will be looking out for the all of these classes and hence the metric which should be used
for this particular problem should be called.
Another thing which should be taken care of is that this data is imbalanced in nature, hence for using
and selecting the class speed it should be selected appropriate for the you can decide if you want to
keep this time the column or not.
I compare all of these columns which are present and select the columns which are important.
You will be applying the same techniques which have been discussed or you can explore different other
techniques.
The main target here is to find out the fraudulent credit card transactions correctly.
That is to predict those four hundred and twenty nine point correct.
So hence you will split the data.
Or if you want to do, you can use cross CV and you can use different mechanisms by the main target
here is to identify these as accurately as possible.
So this is the next project, which is declassification project.
Thank you.