So let's get started with the exploratory data analysis.
The first step is understanding the data.
I want to understand what is really there in my mind, and the first step for that is determining what
is my independent variable and what is my independent.
Let me explain this concept by taking a very simple example, a restaurant that provides pizza and home
delivers pizza.
That restaurant's time taken to deliver pizza is dependent on the order volume right now, many, many
orders are there.
The time taken to deliver the pizza.
The time taken to prepare the pizza.
Is cooking time and the oven temperature.
So all the time taken to deliver is dependent on these four factors.
So this is my dependent variable and these are the independent variables.
Right.
This is the first thing we acidy if I were to develop a machine learning model for this example, it
would be.
What will be the time taken to deliver pizza based on all our cooking time, oven temperature and delivery?
Right.
So you get an idea of what is an independent variable and dependent variable.
Another example would be.
The member of Marks is caught in an exam, he's dependent.
On the number of this study.
Right, so the moxa pain is the dependent variable and the number of hours I study is the independent.
Right.
So let's see the scenario that we will be taking in our exploratory data analysis.
Will be taking an insurance he studied, the insurance company has to determine the insurance charges
for an individual who has taken the whole home.
The insurance company will evaluate leverne parameters and determine the insurance charges for the individual,
as you can see, the parameters of gender married, whether the individual is married or not, a member
of the police education level, whether the individual is self-employed, what is applicant's income?
What is the applicant's income?
What is the loan amount?
What is the training or what is a credit history?
If the individual has taken loans in the past, what is the property?
So all these things are ascertained by the.
Insurance companies, as you can see, that I own my summers and American summers normally, right?
And if you see here, the insurance charges is dependent on these factors, health insurance charges
is the dependent variable and all the extras that you see here are independent votes.
Correct.
So this is the fastest way we are going to be using this example through the exploratory data analysis.
After this, you want to understand what is actually there in the data, right?
For that, you will use data not ahead of time, it tells you the top three records that is there in
my data can got an idea what is there?
You can see that is also a case of.
Probably a junk or missing one.
How are you going to treat that?
That is one of the things that we are going to cover in these subsequent sections.
Remember, if you don't address a lot of junk values, your forecast accuracy will take a hit and probably
you may even get an error like.
Point out we'll be using Google Cola and Python, we will collaborate the development, environment
and Python as the programming language.
So the fight that I will be using for analysis file containing the past data is actually stored in the
Google brain.
I first need to establish the connection between CoLab and Google.
Right.
And after that.
I import five pounds and then I remember this is my prime location.
The Firestone Google.
Yeah.
Damn, I want to understand what type of data is there in my dataset.
Like, you see, these are the variables that you saw.
Right.
If I use data, not info, I get, whether it is a flawed or an integer or an object.
I get all this type of information.
Right.
So this is part of understanding that it is data mining dependent versus independent variables.
Understanding what variables are, what variables anonymously play into the float, was stress getting
an idea about the type of data, the kind of data that we have by looking at the top three records?
So that's what you do in understanding it, right?
So the next step is doing univariate analysis.
OK.