So now that we know the basic concept behind logistic regression, we'll be learning a simple model
in which we will use only a single predictor.
So let us look at this data.
We want to predict the values of this all variable, whether the house will be sold within three months
or not.
If it is if it will be sold, it has value.
And if it will not be sold, it has value, too.
So we will be using only one predictor.
Which is this price.
That is the asking price.
How much is the price?
So that is asking for this property.
So running a logistic model is very easy to create a logistic model.
We need to use a function called DMM so we will make delimit outfit.
This is the variable name so you can keep anybody of a name.
But after that, you need to write any function, which is Dehlin.
So this is a function which stands for generalized linear models.
The first parameter is the response variable, which is sword.
So Wheelwright sold.
So this is the response very well.
And it is to be dependent on one productivity Bill.
So will they do this in whatever way they don't deliver contained or they're delivering?
Is the dependent variable or the response variable?
And whichever variable, Wilmington on the right hand side, that will be predictor variables.
You're right, price.
So these two variables we need to use to create this money.
And we'll also specify the date that we used.
We just do days equal to be if.
The third parameter that we have to specify is family is equal to binomial.
This barometer will tell us that we are running a logistic regression rather than any generalized linear
model.
So this family is equal to binomial is important.
But every day when you are running a logistic model to run this, you can see that a new variable dealing
four discrete did this variable has the information of this logistic model.
Look at the information you can make somebody
in the back roads.
Will IDL and Broadford just be ready, willing and will run it?
So this is all the information that we get when we run the logistic regression model.
But we are going to discuss in the next lecture.
Is this part in which we are getting the relationship of price with this old video, but which is the
response variable?
So we wanted to predict B does you don't beat 11 values.
This estimates column is giving us this.
B does.
You know, we did the intercept and this beta one, which is decommissioned of price variable.
So we have B does it when we do one in the next video, we will understand what is the meaning of all
these other values.