  An outlier is an observation or a data point that is far from rest of the
data points in a given dataset or we can be said that an outlier is away from
the center of mass of observations. Presence of outliers can skew statistical
measures and data distributions which can lead to misleading representation of
the underlying data and relationships. It is seen that the removal of outliers
from the training dataset before modeling can give better predictions. With the
advancement of machine learning, the outlier detection models are also
advancing at a good pace. The goal of this work is to highlight and compare
some of the existing outlier detection techniques for the data scientists to
use that information for outlier algorithm selection while building a machine
learning model.
