So here is the data that we are going to use in this course, the problem statement here is that you
are a manager in a real estate company.
You want to find out these selling potential of a property that is basically data of past property transactions.
You want to predict whether the property will be sold within three months or not.
So the sold variable here, which is the last variable of this day, will really be dependent variable
in our analysis.
And other factors which impact this old variable, which we identified, busiest primary and secondary
search, are these independent variables.
This dataset has values for all these variables.
Whenever we collect data.
Different parts of data come from different sources.
And we need to collate this data into a single tabular format.
As this.
And this table, the header row is just the variable names.
While keeping the video names, trying not to use space, instead using underscore.
For example, an air quality variable we have used underscored this is because some software do not
accept spaces.
Secondly, try to keep names so that you can recognize the actual variable that is a whiteboarding,
putting names like X1, x2, X3.
Since post analysis, the results from software will also be showing these variable names.
And then it will become difficult to make sense of the design.
I will show you in a couple of minutes what each variable stands for.
But even by just looking at the names, you'll get some idea of what this day is.
So this data is about how transactions and other related variables to each house.
We will look at these variables in detail in some time after this top head row.
Each row contains values pertaining to one single observation.
And we have a total of five hundred six observations.
The total number of columns we have is 19.
We may have received different parts of this data from different sources, probably one source gave
us data of also address and its price.
One source gave data of air quality in different neighborhoods into the case.
We need to use a unique identifier which will help us match these two parts.
So doing all these different parts of data and get the data into one single data set with the rows and
columns, the columns contain each variable and rules contain each observation.
Now, your data suggest 80, but whenever you are going to share your data, all your analysis with
anyone, the data should be accompanied by data dignity.
A data dictionary contains information about the variables.
Indeed, do does it?
So comprehensive data dictionary includes the following.
It has definitions of all the variables of your d2c.
Then it should tell about the unique identifier for each observation.
This unique identifier is called the primary key to match the data from another table.
If there is a magic key used, that should also be highlighted.
If you want to learn more about primary, Antec and Ricki's you can look at our video here.
We have shared the link in this presentation.
This video is part of another course on database management using Escuela.
Lastly, if there is a categorical variable in the data, all its values should also be explained.
Now, let's define the variables of our host bracingly, does it?
On the left is the variable limb, as indeed I say, on the right is a brief description of what that
variable is.
I'm going to read this one by one.
No.
So the price tells us the asking price of the property by the owner.
That is not how much the owner is willing to sell the property.
That's a NATO stance.
What proportion of residential area in the town?
It call Stanford index of quality of it in that neighborhood.
Room them is the average number of rooms in the houses of that locality.
It is how all is that house construction is that is all many years ago.
It was constructed.
This one, two, three, four are four distances of that hose from the nearest employment hub.
Because we hypothesised that the ease of selling the house will depend on the employment opportunity
available needed.
Teachers stand for the number of teachers, but Ulgen population in that town.
Then we have food probe.
That is the proportion of world population in the town.
Airport stands for.
Is there any airport in the city or not?
So this is a categorical variable.
It has to Aleuts.
Yes, I know.
These are self explanatory.
Yes.
Means that there is an airport in the city.
No means there is no airport in the city.
And horse bed is giving us the number of hospital beds.
But Teligent population in the down.
And hard rooms, guilty number of hotel rooms, but out in public and indeed on.
Waterboarding is another categorical variable.
It has values like Lake River boat or none.
Which is giving us whether there is a natural freshwater source within the city or not.
Rainfall is giving us the average rainfall in centimeters.
Buster is a categorical variable with this telling us whether there is a bus terminal in the city.
Yes means that there is no means there is no bus terminal in the city.
And Park's variable is giving us the proportion of land assigned as green land in that town.
And lastly, we have the sole variable, which is our dependent variable, which is telling us whether
that particular property was sold within three months of getting listed on or.
By default, it will be having value zero and one one meaning that the house was sold within three months
and zero means that the house was not sold within three months.
So that does.
Yes, wi fi and the PDA of PBT, a dash in the resource section of this lecture.
You have the new the data we'll be using for analysis.
No doubt this data is not real leader, and it's similar to Boston housing data set commonly used for
analysis.
But do not take the result of our analysis too seriously.
This is not actually does it?
In the next video, we will learn how to import this data, say, in our software package.