Using Max Abs Scaler for feature scaling | Machine Learning

In this tutorial, we'll look at Max Abs 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 Max Abs 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, Max Abs Scaler works by dividing each observation by the maximum value in that feature, irrespective of the sign of the observation, i.e. basically by the MAXimum ABSolute (hence the name Max Abs Scaler) value of each feature in a particular feature. 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!