So this is the data that we have.
This is a dependent variable that is valuation of business, we want to calculate the value generated
by our store.
So using these independent variables, we want to predict this dependent variable, we have around seven
independent variables.
The first one is tape of the store, the second one is tape of the location, the next place to tape,
then we have the investment, then we have the dollar value of investment.
Then we have a number of competitors, then the estimated population in the vicinity of our store,
and then we have the average household income in the vicinity of that is store.
So this is the past data of our stores, so we have the data of our own thirteen hundred and thirty
stores.
Now, let's look at this independent variable, the first one is the type of the store.
Now, let's apply a filter to our data, so select all the data points by holding control shift and
then the Iraqis.
And then.
Go to this DataLab and click on this filter icon, or else you can also use old HP shortcut.
So once you have applied the filters, you will be able to see this dropdown buttons and inside this
drop down menu you will see what all the values or variables are taking.
So let's first look at the type of a store.
This is the size of this store.
So whether we have opened a large store or a medium store or a small store.
So if you can see there are only three categories in this variable.
The second variable is the type of the location.
So when we are opening our stores, whether we are opening in a commercial space or a residential space,
so you can see there are only two values, commercial and residential.
And this value is telling us what is the type of location of the store.
Then we have the city type city type means whether the city in which we have opened a store is a metro
city or a non metro city.
So there are only two allus micro and macro.
And this variable is signifying the city type of our iStore.
So these three are the only categorical variables that we have, the next four variables are continuous
very well.
The first variable is investment.
This is the amount of money we invested in, say, building the furniture or the infrastructure for
overstored.
And as you can see, this is a continuous variable, that's why we are getting multiple values in our
filter option, then we have the number of competitors, the number of competitors located in the vicinity
of our store.
You can see that there are four values.
So maximum, we have 40 stores.
Then we have the estimated population in the vicinity.
So this is the number of people that are living near that store.
Then we have the average household income near the store, so for all these people, what is the average
household income of families?
And at last, we have the dependent variable that is the valuation of the business.
Now, using our linear regression model, we want to use these independent variables to predict the
valuation of business.
After building such model, we will be able to identify valuation of the store from this independent
variable.
So in a way, before investing in a property, we will be able to identify its value and if that iStore
valuation is higher than the investment needed, then we can go ahead and open that store.
So this is our data, we have three categorical independent variable.
We have four continuous independent variable and then we have one dependent variable.
In the next lecture we will apply preprocessing and this data.
There are no missing values or outliers in this data, we just have to convert this categorical variables.
We have already processed this data for outliers and missing value treatment since we have already discussed
those concepts in our previous case study.
One more important thing to note here is that this is a fictitious data, we have created this data
only for practice, and you should not use this data for any professional work and you should also not
use the result of this data in your professional life.