Too Many Variables
67 🏗️ Principal Component Analysis

Feature Engineering A-Z

Preface
Introduction
Numeric Features
Categorical Features
Datetime Features
Missing Data
Text Features
Circular Features
Too Many Variables
Correlated Data
- 79 Correlated Overview
- 80 High Correlation Filter
Outliers
Imbalanced Data
Miscellaneous
Spatial
Time-Series Data
Image Data
Ralational Data
Video Data
- 138 Video Overview
- 139 🏗️ Temporary
Sound Data
- 140 Sound Overview
- 141 🏗️ Temporary
142 🏗️ Order of transformations
143 🏗️ What should you do if you have sparse data?
144 🏗️ How Different Models Deal With Input
145 🏗️ Summary
References

Table of contents

67.1 Principal Component Analysis
67.2 Pros and Cons
- 67.2.1 Pros
- 67.2.2 Cons
67.3 R Examples
67.4 Python Examples

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Too Many Variables
67 🏗️ Principal Component Analysis

67 🏗️ Principal Component Analysis

67.1 Principal Component Analysis

WIP

67.2 Pros and Cons

67.2.1 Pros

67.2.2 Cons

67.3 R Examples

67.4 Python Examples

66 Zero Variance Filter

68 🏗️ Principal Component Analysis Variants' class='pagination-link' href='/too-many-pca-variants'> 68 🏗️ Principal Component Analysis Variants

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