Data preparation involves transforming raw data in to a form that can be modeled using machine learning algorithms.

Cut through the equations, Greek letters, and confusion, and discover the specialized data preparation techniques that you need to know to get the most out of your data on your next project.

Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively prepare your data for predictive modeling with machine learning.

Table of Contents

I Introduction

II Foundation

Data Preparation in a Machine Learning Project

Why Data Preparation is So Important

Tour of Data Preparation Techniques

Data Preparation Without Data Leakage

Ill Data Cleaning

Basic Data Cleaning

Outlier Identification and Removal

How to Mark and Remove Missing Data

How to Use Statistical Imputation

How to Use KNN Imputation

How to Use Iterative Imputation

IV Feature Selection

What is Feature Selection

How to Select Categorical Input Features

How to Select Numerical Input Features

How to Select Features for Numerical Output

How to Use RF E for Feature Selection

How to Use Feature Importance

V Data Transforms

How to Scale Numerical Data

How to Scale Data with Outliers

How to Encode Categorical Data

How to Make Distributions More Gaussian

How to Change Numerical Data Distributions

How to Transform Numerical to Categorical Data

How to Derive New Input Variables

VI Advanced Transforms

How to Transform Numerical and Categorical Data

How to Transform the Target in Regression

How to Save and Load Data Transforms

VII Dimensionality Reduction

What is Dimensionality Reduction

How to Perform LOA Dimensionality Reduction

How to Perform PCA Dimensionality Reduction

How to Perform SVD Dimensionality Reduction

VIII Appendix

Getting Help

How to Setup Python on Your Workstation

IX Conclusions

How Far You Have Come