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Category: Feature Engineering

Data Mining, Feature EngineeringLeave a comment

Principal Component Analysis fully explained

March 13, 2020December 30, 2020 Tung.M.Phung

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

Data Mining, Feature EngineeringLeave a comment

When to add a dummy variable?

December 19, 2019July 31, 2020 Tung.M.Phung

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?

Data Mining, Feature EngineeringLeave a comment

How to convert Categorical Variables to Numerical Variables

December 18, 2019July 31, 2020 Tung.M.Phung

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

Data Mining, Feature EngineeringLeave a comment

When to do feature centering, scaling and normalization?

December 5, 2019July 31, 2020 Tung.M.Phung

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?

Data Mining, Feature EngineeringLeave a comment

Feature selection with sklearn

September 4, 2019July 31, 2020 Tung.M.Phung

Feature selection is hard but very important. Continue reading Feature selection with sklearn

Data Mining, Feature EngineeringLeave a comment

How to deal with missing values (NaNs)

August 31, 2019July 31, 2020 Tung.M.Phung

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)