This session is about variable transmission.
We are trying to derive maximum value out of what our data got.
Incredible transformation.
You're not having any data like you're only trying to make the data more useful and different techniques
are available for variable transmission.
That is transformation of continuous variable, and that is transformation of categorical, like both
we will see in this session in the case of continuous variable, which is numerically what's right.
You can transform the data by converting the data from a numeric to a laboratory or square root on human
right through this, we're essentially changing the nature of relationship on nature, of distribution,
of dataset.
That's what we are doing through this exercise.
Beny is transforming a new data into a category like we already saw the example of beaning and logarithmic
in treating outlier data.
So that's something we Soldini.
In the case of categorical data, the transformation to numeric value happens through the process of
what is known as encoding, OK?
In fact, the transformation is more needed in categorical variables than numeric variables.
This is because categorical variables as such cannot be used directly in most machine learning models.
Hence, it becomes a necessity for us to convert the categorical variables into numeric variables.
Right.
So if you see the insurance example, the ones that are mentioned in green right there on non numeric
categorical, the ones that are mentioned in red, they are continuous numeric variables.
So all the ones that are that are shown in red, the numeric variables, I can translate them if needed
into longer to make a square root or Kubelik.
Right.
Whereas the ones in green non numeric I have to mandatorily convert them, transform them into numeric
data, only then I can use them for machine learning models.
Right.
So in the case of non numeric or categorical, it's more than necessary for.
Right.
So how do you convert Python comes with preloaded library?
As you can see the example here, I have six smoker region, right?
These are categorical variables using the encoding process.
I input pre processing and then I use pre dot, label encoder and then I transform and you can see that
the categorical data is now converted into numeric data and this data can now be used for generating
machine learning.
Models get very simple, right?
So variable transformation is a very important process.
More important in the case of categorical variables, because I cannot use categorical variables directly
in machine learning models like they must be first converted into numeric value.
OK, so that completes the session on variable transmission, right?
In fact, with that session we completed and are exploratory data analysis online program.
So just to summarize, we started with understanding the data, basic understanding of the data.
Then we did univariate analysis, then Vivarium analysis to see the relationship between two variables.
We also saw the concept of Collini are clearly something we don't want in our dataset.
Then we also saw missing values, treatment, how to handle missing values, how to handle outliers,
how to transform variables like andas we saw as part of exploratory data analysis.
Right.
So if you really see the last three, what I would call a future Regini because I'm trying to get maximum
value out of whatever data and I want to make the data more useful for my purposes.
Right.
So that completes the session on exploratory data analysis.
Thank you.
And I wish you the best of luck.