Let's see this important concept, outliers.
Outliers are basically extreme points in our data set, and they impact the accuracy of our models quite
adversely.
This graph you will remember from the central tendency and dispersion session, I use this to explain
the concept of standard deviation.
I'm now going to use the same graph to explain outliers.
So what are outliers in this example?
If you really see the outliers at this point, sixty nine, seventy one and 96.
So the question is, can we have outliers in our dataset?
You can have.
Provided the number of such outliers are negligible.
And a better way to handle outliers is to remove them, remove outliers from your dataset.
Why do you want to have outliers?
Outliers can lead to low accuracy.
Unwanted inferences can result if outliers are present in our dataset.
OK, we're going to use more explored to understand outliers.
Your box plot will look like this.
The outliers will shown as a daughter star mark.
OK, and the limits for box plot are.
Q three plus one point five times you three minus one, are you one minus one point five times three
minus Q on this one point five times three minus one is also known as important range.
OK, unwater Q1 and Q3, they are percentiles.
I'm sure you will remember percent from the previous session.
I'm going to use Excel to demonstrate the concept of outliers and box plus.
You can use Python or any other programming language to do the same.
OK, now let's see.
Box plot in action.
I have the invoice amount for this.
I'm going to create a box plot.
OK, how many data points are there?
There are 156 data points in our lives to let them come to insert.
Click recommended charts.
Come to all charts, click Moxham Whiskered, OK, so this is your box plot.
So as you can see here, the outliers are clearly shown as dots, right?
It is also telling what are those values?
So we can remove them.
Removed outliers from the box plot.
OK, and as I mentioned earlier, you can use.
Python or any of the programming language to create a box plot and to remove the outliers also.
OK, you can do that programmatically.
Right.
So, again, a very powerful concept, as I said, outliers come in the way of our accuracy.
So let's remove them to the extent possible.
OK.