This article attempts to make PCA crystal clear to anyone who wishes to understand it thoroughly, step-by-step, in both high and low-level concepts. Continue reading Principal Component Analysis fully explained
A dummy variable is a variable (or feature, predictor, column) whose values can be either 0 or 1. Continue reading When to add a dummy variable?
In Machine Learning, while some predictive models allow categorical variables in the data, most require all predictor variables to be continuous Continue reading How to convert Categorical Variables to Numerical Variables
Many people have a tendency to always do feature centering, scaling or normalizing right before applying predictive models to the data… Continue reading When to do feature centering, scaling and normalization?
Feature selection is hard but very important. Continue reading Feature selection with sklearn
This blog post attempts to address why NaNs are bad and how we can fix them. Continue reading How to deal with missing values (NaNs)