Using Robust Scaler to scale features | Machine Learning

In this tutorial, we'll look at Robust Scaler, a type of feature scaling technique for linear Machine Learning models. In the tutorial, we'll be going through all the nitty-gritties of Robust Scaler, when to use them, when NOT to use them, how is it helpful, how is it NOT so helpful etc etc. Feature scaling is so important that your model performance could shoot up by many a percentage points if you use the correct feature scaling techniques. In a nutshell, Robust Scaler works by subtracting the median, and dividing by the Inter Quantile Range, or the difference between the 75th quantile and the 25th quantile for each observation in a particular feature so as to try and minimize the effects of marginal outliers as much as possible. I've uploaded all the relevant code and datasets used here (and all other tutorials for that matter) on my github page which is accessible here: Link: https://github.com/rachittoshniwal/machineLearning If you like my content, please do not forget to upvote this video and subscribe to my channel! If you have any qualms regarding any of the content here, please feel free to comment below and I'll be happy to assist you in whatever capacity possible. Thank you!