Introduction The grass is not as green as it can be seen from outside when it comes to Machine Learning or Data Science. The end result of designing a perfect hypothesised Model is rarely possible not because ML is not powerful, but there is a long tedious and repetitive work of cleaning, analysing and polishing the dataset is involved. One thing that we need to take care of in this Cleaning and improving process is "The Outliers". This is a mere term with a simplistic meaning but is troublesome to handle/manage the data when introduced in it. Still unaware of Outliers, How they are introduced? and How to identify them? Read it Here > Mystery of Outliers < Let's begin with the first technique to Handle outliers.