Should we split first or scale first?? | Data Preprocessing | EDA | Machine Learning.

I do Free Lancing Projects and Final Year Engineering Projects
I even take classes - Machine Learning/Data Science/Fundaments of Python

Contact me on Whatsapp : 9403027341

You first need to split the data into training and test set (validation set could be useful too).

Don't forget that testing data points represent real-world data. Feature normalization (or data standardization) of the explanatory (or predictor) variables is a technique used to center and normalise the data by subtracting the mean and dividing by the variance. If you take the mean and variance of the whole dataset you'll be introducing future information into the training explanatory variables (i.e. the mean and variance).

Therefore, you should perform feature normalisation over the training data. Then perform normalisation on testing instances as well, but this time using the mean and variance of training explanatory variables. In this way, we can test and evaluate whether our model can generalize well to new, unseen data points.

Видео Should we split first or scale first?? | Data Preprocessing | EDA | Machine Learning. канала Stats_With_Sakhala_ji