Miscellaneous
100
🏗️ Zip Codes
Feature Engineering A-Z
Preface
Introduction
Numeric Features
1
Numeric Overview
2
Logarithms
3
Square Root
4
Box-Cox
5
Yeo-Johnson
6
Percentile Scaling
7
Normalization
8
Range Scaling
9
Max Abs Scaling
10
Robust Scaling
11
Binning
12
Splines
13
Polynomial Expansion
14
Arithmetic
Categorical Features
15
Categorical Overview
16
Cleaning
17
Unseen Levels
18
Dummy Encoding
19
Label Encoding
20
Ordinal Encoding
21
Binary Encoding
22
Frequency Encoding
23
Target Encoding
24
Hashing Encoding
25
Leave One Out Encoding
26
Leaf Encoding
27
GLMM Encoding
28
Catboost Encoding
29
Weight of Evidence Encoding
30
James-Stein Encoding
31
M-Estimator Encoding
32
Thermometer Encoding
33
Quantile Encoding
34
Summary Encoding
35
Collapsing Categories
36
Categorical Combination
37
Multi-Dummy Encoding
Datetime Features
38
Datetime Overview
39
Value Extraction
40
Advanced Features
41
Periodic Features
Missing Data
42
Missing Overview
43
Simple Imputation
44
Model Based Imputation
45
Missing Values Indicators
46
Remove Missing Values
Text Features
47
Text Overview
48
Manual Text Features
49
Text Cleaning
50
Tokenization
51
Stemming
52
N-grams
53
Stop words
54
Token Filter
55
Term Frequency
56
TF-IDF
57
Token Hashing
58
Sequence Encoding
59
LDA
60
word2vec
61
BERT
Periodic Features
62
Periodic Overview
63
Trigonometric
64
Periodic Splines
65
Periodic Indicators
Too Many Variables
66
Too Many Overview
67
Zero Variance Filter
68
Principal Component Analysis
69
Principal Component Analysis Variants
70
Independent Component Analysis
71
Non-Negative Matrix Factorization
72
Partial Least Squares
73
Linear Discriminant Analysis
74
LDA Variants
75
Autoencoders
76
Uniform Manifold Approximation and Projection
77
ISOMAP
78
Filter based feature selection
Correlated Data
79
Correlated Overview
80
High Correlation Filter
Outliers
81
Outliers Overview
82
Identify
83
Outlier Removal
84
Imputation
85
Indicate
Imbalanced Data
86
Imbalanced Overview
87
Up-Sampling
88
SMOTE
89
SMOTE Variants
90
Down-Sampling
91
Near-Miss
92
Tomek Link Removal
93
Condensed Nearest Neighbor
94
Edited Nearest Neighbor
95
Instance Hardness Threshold
96
🏗️ One Sided Selection
Miscellaneous
97
Miscellaneous Overview
98
IDs
99
Colors
100
🏗️ Zip Codes
101
🏗️ Emails
Spatial
102
Spatial Overview
103
🏗️ Spatial Distance
104
🏗️ Spatial Nearest
105
🏗️ Spatial Count
106
🏗️ Spatial Query
107
🏗️ Spatial Embedding
108
🏗️ Spatial Characteristics
Time-Series Data
109
Time-series Overview
110
🏗️ Smoothing
111
🏗️ Sliding
112
🏗️ Log Interval
113
🏗️ Time series Missing values
114
🏗️ Time Series outliers
115
🏗️ Differences
116
🏗️ Lagging Features
117
🏗️ Rolling Window
118
🏗️ Expanding Window
119
🏗️ Fourier Features
120
🏗️ Wavelet
Image Data
121
Image Overview
122
🏗️ Edge and corner detection
123
🏗️ Texture Analysis
124
🏗️ Greyscale conversion
125
🏗️ Color Modifications
126
🏗️ Noise Reduction
127
🏗️ Value Normalization
128
🏗️ Resizing
129
🏗️ Changing Brightness
130
🏗️ Shifting, Flipping, and Rotation
131
🏗️ Cropping and Scaling
132
🏗️ Image embeddings
Ralational Data
133
Relational Overview
134
🏗️ Manual
135
🏗️ Automatic
Video Data
136
Video Overview
137
🏗️ Temporary
Sound Data
138
Sound Overview
139
🏗️ Temporary
140
🏗️ Order of transformations
141
🏗️ What should you do if you have sparse data?
142
🏗️ How Different Models Deal With Input
143
🏗️ Summary
References
Table of contents
100.1
Zip Codes
100.2
Pros and Cons
100.2.1
Pros
100.2.2
Cons
100.3
R Examples
100.4
Python Examples
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Miscellaneous
100
🏗️ Zip Codes
100
🏗️ Zip Codes
100.1
Zip Codes
WIP
100.2
Pros and Cons
100.2.1
Pros
100.2.2
Cons
100.3
R Examples
100.4
Python Examples
99
Colors
101
🏗️ Emails