Welcome back in this lecture we will be learning about regression.
So regression is a very important algorithm.
When we work in data analysis or machine learning part.
So what the regression really does is it is a major role for the nation between a mean value of one
variable and the corresponding value of other variable.
So suppose there are two variables are dependent and independent variable in the x and y axis.
So what it really does is a regression technique is used to estimate the relationship between these
two particular variables.
So when it predicts the relationship between them it predicts that out do you think that the average
has been trained to suppose there is independent variable in the x axis and there's a dependent variable
in the y axis.
So as the variable in the x axis is increasing or decreasing it looks into this.
And based on that it estimates the relationship between the both very much of an X and Y.
So our regression data is regression line which shows that trend of our end of the relationship how
regression is going going.
So this is what regression is all about to predict the relationship between the two variables.
So let's see how regression can be done here in power.
So again for regression as well we will take our scatter charts charactered is really amazing here and
probably a form many kind of analysis is.
So let's take something here for details.
We will dig our brought up code for legend.
We will dig our customer names and for the success in the X-axis we will take the independent variable.
Suppose we just quantity ordered and by existing We did our scenes.
So what we'll be doing here is so before going further let me show you something.
So this is a scattered chart that aggression analysis we have to find a trend between these two particular
variables quantity order and say so we cannot really make much out of it when the data is not that much
scared as you can see there are certain outliers here these two which are really not helping us to visualize
or they develop so far that we apply certain filters.
So first of all in the quantity ordered we will make it less than 60.
So as you can see here see here is between 50 to 100 between 200.
There are so many datapoints available up here and all after hundred there are not much good ones are
available.
So I will just take a random Velu between 50 to under that 60 and I'll apply filter.
There you go.
Now for the y axis which is my SES again I will apply sort and felt that as you can see not much point
is available before 20 here.
OK.
And also look you can see you are in that band after 10000 not much of any available are there to see
a particular good trend.
I have to make it much more scattered here too far that I am doing all this filtering.
So what I have done here is I have four cells I have taken at less than 10000 and greater than 20.
I apply the filter.
There you go.
And I think all of one mistake that I've made here is that quantity order should be more good than 20.
So I just simply make sense less than 10000 and the quantity order is should be greater than runty that
it will not look good.
So now you can see here that the top ones are pretty much scattered to in a regression chart.
We also need to grow in line.
So I would go to the analytic spin up here and I will add a trend line just simply clicking on the trend
line and clicking on ad once done you can see this board that last line you can make a dash or son or
daughter as the need to this is our brand name on it right
now as you can see what it shows is as quantity or dirty or is increasing the size is also increasing.
This can be shown up here using this particular regression line which is going in the upward direction.
So it is showing that trend and the relationship between the SES and the quantity ordered how they are
dependent on each other when the quantity order is increasing the number of say is increasing.
So you can do this in many other cases as well.
This particular series there is one of those use cases just to show you how regression actually works.
So this is pretty simple in power by using this particular scatter chart.
So I hope you have understood what really regression here is and you'll see the regression one.
What we have to do is if we have to make it much more scattered so far that I ever use certain filters
up here in sales and the quantity order to make it much more scattered in our side.
And after that we have applied regression line trend line up here.
So that was all about the regression.
I hope you understood this.
We will be continuing our work in the next lecture and that's all for now.
We'll be in the next lecture.
Thank you.