While working on various dataset to train a Machine Learning model. What is it, that you look for? What is the most important part of the EDA phase? There are certain things which, if they are not done in the EDA phase, can affect further statistical / Machine Learning modelling. One of the answers is to, find “Outliers”.

One more thing which i missed in the article, that presence of outliers affects the distribution of the data.

Let's examine what can happen to a data set with outliers.

For the sample data set:

1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4

We find the following: mean, median, mode, and standard deviation

Mean = 2.58

Median = 2.5

Mode = 2

Standard Deviation = 1.08

If we add an outlier to the data set:

1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4, 400

The new values of our statistics are:

Mean = 35.38

Median = 2.5

Mode = 2

Standard Deviation = 114.74

It can be seen that having outliers often has a significant effect on the mean and standard deviation and hence affecting the distribution.

We must take steps to remove outliers from our data sets. One of the easiest way to handle this is log transformation.

Hope this information will help you.