I am (name) from LearnVern,
So today we will see in continuation of our previous session of Machine Learning.
So, let's see.
Today we will see about Multi linear Regression.
So, what happens in Multi linear Regression is that here our dependent variable is just one, but to affect that variable, such independent variables are many over here.
So, this is what happens in Multi linear Regression
Now, we will understand this in detail as to How it works?
So, this works based on a formula,
Which is y is equal to B not plus B1 into x1 plus B2 into X2, plus till some point BN into XN plus E.
So, this is its formula.
Here y is our output meaning dependent or predicted variable.
Bnot is basically y intercept, which is basically the value of y when both x1 and x2 are zero.
Now, we will understand about B1 and B2. What are they?
These are regression coefficients, so they basically represent a change in one unit, so they inform if there is any change in y.
Now, BN is the slope of the coefficient .
And last E is the random error.
So, here you can see from a simple linear model, there is a change into a more variable model that you can see.
So, because of many variables we have taken as B1, B2 uptil n numbers.
So, this is the difference that we have made.
So, this is the formula for Multi linear Regression,
And now, let's move ahead and see about its application.
The application are evaluate trend,
Setup price for a house apartment.
So, these are certain examples of Multiple linear regression.
So, friends let's conclude here for today,
We will stop today's session here only.
And it's further parts we will see in our next session.
So keep learning and remain motivated.
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