Let's see a more technical feature scaling method, that we can use for scaling our dataset. It is popularly known as "Scaling to Unit Length", as all the features are scaled down using a common value. Unlike previous methods that we have studied so far, used to scale the features based on some value specific to the variable, here all the variables are used to scale the features. Here, the scaling is done row-wise to make the complete vector has a length of 1, i.e. normalisation procedure normalises the feature vector and not the observation vector. Note:- Scikit-learn recommends this scaling procedure for text classification or clustering. Formula Used:- Scaling to Unit Length can be done using 2 different ways:- 1. Using L1 Norm:- L1 Norm or popularly known as Manhattan Distance can be used to scale the datasets. Scaling to Unit Length using Manhattan Distance where l1(x) can be calculated using the below formula. Manhattan Distance Formula 2. Using L2 Norm:- L2