So here is the data that we are going to use.
And this goes.
The problem statement here is that you are a manager in a real estate company and you want to find out
the true value of a property.
That is basically pricing of past property transactions.
You want to get the price of the property you want to sell.
So price of a property will be the dependent variable in our analysis and other factors which impact
the price which we identified Mrs.
Primary and secondary research are the independent variables.
This dataset has values for all these variables.
Whenever we collect data.
Different parts of data come from different sources.
And you need to collate this data into a single tabular format such as this.
And this table, the hetero tells us the variable names.
While keeping variable names, try not to use space.
Instead, use an underscore, for example, in crime rate.
We have used crime underscore rate.
This is because some softwares do not accept spaces and variable names.
Secondly, try to keep names such that you can recognize the actual variable.
That is a way to put names like X1, x2, x3.
Since post analysis, the results from softwares 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 get some idea of what this data is.
So this data is about price of houses and other related variables to each house.
We will look at these variables in detail and some time after this top header row, each row contains
values pertaining to one observation.
And we have our total of 506 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 wholesale address and its price.
One source gave us the tough crime rates in different neighborhoods in such a case.
We need to use a unique identifier which will help us match these two parts.
So join all these different parts of data and get it into one single dataset with rows and columns that
columns contain each variable and rules contain each of deletion.
Now, your dataset is at 80, but whenever you are going to share your data or your analysis with anyone,
the data should always be accompanied by data.
Dignity.
A data dictionary contains information about the variables in the dataset.
So comprehensive data dictionary includes the following.
It has definitions of all the variables of your dataset.
Then it should tell about the unique identifier for each observation.
This unique identifier is called the primary key.
To match data from another table.
If there is a matching key used, that should also be highlighted.
If you want to learn more about primary and secondary keys, you can look at our video here.
We have shared Dillinger.
This video is part of our another course on database management using a skewed.
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 pricing.
Does it?
On the left is the variable name, as indeed he does it, on the right is the brief description of what
that variable is.
I'm going to read this one by one.
No.
So Price tells us the value of the house crime rate tells us the crime rate in that neighborhood rest
area stands for the proportion of a residential area in the town.
Air quality gives us the index of quality of air in that neighborhood.
Room number is the average number of rooms in houses of that locality.
Age is how old is that house destruction in years?
How many years ago it was constructed?
This one, this two, this three, just four are four distances of that house from the nearest employment
hub.
Because we hypothesize that.
The price of homes will depend on the employment opportunity available Nereid features stands for the
number of teachers but thousand population in that town.
Then we have poor prop.
This is the proportion of world population in the town.
Airport stands for.
Is that any airport in the city or not?
So this is a categorical very well it does.
Do I lose?
Yes and no.
These are self-explanatory.
That, yes.
Means that there is any airport in the city.
No means there is no airport in the city.
And horse hospitals is giving us number of hospital beds.
But thousand population in the town and heart rooms is giving us number of hotel rooms.
But out in public and in the town waterboardings and other 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.
We're just telling us whether there is a bus terminal in the city.
Yes.
Means there is no means there is no bus terminal in the city.
And Boks variable is giving us the proportion of land assigned as green land in that town.
So with the CSB file and the PDAF of Peabody, Adarsh in the resource section of this lecture.
You have the due data.
We will be using for our analysis.
Note that this data is not real data and is similar to the Boston housing data set commonly used word
analysis.
But do not take the result of our analysis too seriously.
This is not actual data.