QuickDataScience | Quick & Easy Data Science

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. 

Introduction Machine Learning, Data Science, Data Analytics. etc. etc. are the terms that are on hype in the current world and every individual is drawn toward these fancy fields, not only because there is a high demand for these technologies but also the things we can achieve from them.  Data is the next-generation fuel for industries, has seen a huge surge in its importance in the past few decades because with the data we can avail all the super-intelligence kind of stuff. All the super-intelligence stuff like knowing our customers better in large, predicting future events, building intelligent systems have been made possible with the data.  Thus, as we can harness the power of data, more and more industries are trying to capture as much data as they could to enhance their products/services. Hence. the demand for technologies and jobs dealing with data is on rising. This rising demand is attracting more and more individuals towards itself.  But with rising new ways to capture data, i