https://github.com/solegalli/feature-engineering-for-machine-learning

Code repository for the online course Feature Engineering for Machine Learning
https://github.com/solegalli/feature-engineering-for-machine-learning

data-science feature-engineering feature-extraction machine-learning python

Last synced: 13 days ago
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Code repository for the online course Feature Engineering for Machine Learning

• Host: GitHub
• URL: https://github.com/solegalli/feature-engineering-for-machine-learning
• Owner: solegalli
• License: other
• Created: 2019-05-01T08:05:42.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2023-12-05T12:07:10.000Z (11 months ago)
• Last Synced: 2024-10-22T19:20:01.024Z (21 days ago)
• Topics: data-science, feature-engineering, feature-extraction, machine-learning, python
• Language: Jupyter Notebook
• Homepage: https://www.trainindata.com/p/feature-engineering-for-machine-learning
• Size: 24.7 MB
• Stars: 368
• Watchers: 6
• Forks: 397
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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## Feature Engineering for Machine Learning - Code Repository

[](https://www.trainindata.com/p/feature-engineering-for-machine-learning)

Code repository for the online course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)

**Launched**: November, 2017

**Actively maintained**.

[](https://www.trainindata.com/p/feature-engineering-for-machine-learning)

## Table of Contents

1. **Introduction: Variable Types**

	1. Numerical Variables: Discrete and continuous

	2. Categorical Variables: Nominal and Ordinal

	3. Datetime variables

	4. Mixed variables: strings and numbers

2. **Variable Characteristics**

	1. Missing Data 

	2. Cardinality

	3. Category Frequency

	4. Distributions

	5. Outliers

	6. Magnitude

3. **Missing Data Imputation**

	1. Mean and Median Imputation

	2. Arbitrary value imputation

	3. End of Tail Imputation

	4. Frequent category imputation

	5. Adding string missing

	6. Random Sample Imputation

	7. Adding a missing indicator

	8. Imputation with Scikit-learn

	9. Imputation with Feature-engine

4. **Multivariate Imputation**

	1. MICE

	2. KNN imputation

5. **Categorical Variable Encoding**

	1. One hot encoding: simple and of frequent categories

	2. Ordinal encoding: arbitrary and ordered

	3. Target mean encoding

	4. Weight of evidence

	6. Rare Label encoding

	7. Encoding with Scikit-learn

	8. Encoding with Feature-engine

	9. Encoding with category encoders

6. **Variable Transformation**

	1. Log, power and reciprocal

	2. Box-Cox

	3. yeo-Johnson

	4. Transformation with Scikit-learn

	5. Transformation with Feature-engine

7. **Discretisation**

	1. Arbitrary

	2. Equal-frequency discretisation

	3. Equal-width discretisation

	4. K-means discretisation

	5. Discretisation with trees

	6. Discretisation with Scikit-learn

	7. Discretisation with Feature-engine

8. **Outliers**

	1. Capping

	2. Trimming

9. **Datetime**

	1. Extracting day, month, week, etc

	2. Extracting hr, min, sec, etc

	3. Capturing elapsed time

	4. Working with timezones

	

10. **Mixed variables**

	1. Creating new variables from strings and numbers

11. **Feature creation**

	1. Sum, prod, count, mean, std, etc

	2. Div, sub

	3. Polynomial expansion

	4. Splines

	

12. **Feature Scaling**

	1. Standardisation

	2. MinMaxScaling

	3. MaxAbsoluteScaling

	4. RobustScaling

13. **Pipelines**

	1. Classification Pipeline

	2. Regression Pipeline

	3. Pipeline with cross-validation

## Links

- [Online Course](https://www.trainindata.com/p/feature-engineering-for-machine-learning)