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One-hot encoding — Machine Learning Zoomcamp Module 3

One-Hot Encoding allows encoding categorical variables in numerical ones. This method represents each category of a variable as one column, and a 1 is assigned if the value belongs to the category or 0 otherwise.

Machine Learning Zoomcamp › Module 3: Machine Learning for Classification › One-hot encoding

Notes

One-Hot Encoding allows encoding categorical variables in numerical ones. This method represents each category of a variable as one column, and a 1 is assigned if the value belongs to the category or 0 otherwise.

Classes, functions, and methods:

• df[x].to_dict(orient='records') - convert x series to dictionaries, oriented by rows.
• DictVectorizer().fit_transform(x) - Scikit-Learn class for one-hot encoding by converting x dictionaries into a sparse matrix. It does not affect the numerical variables.
• DictVectorizer().get_feature_names() - return the names of the columns in the sparse matrix.

The entire code of this project is available in this jupyter notebook.

• Notes from Peter Ernicke

Key concepts

• scikit-learn
• Jupyter Notebooks

Related notes

• mlz-m01-introduction-to-numpy
• mlz-m01-introduction-to-pandas
• mlz-m01-linear-algebra-refresher
• mlz-m01-setting-up-the-environment
• mlz-m01-summary
• mlz-m02-baseline-model-for-car-price-prediction-project
• mlz-m02-categorical-variables
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• mlz-m02-data-preparation
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• mlz-m03-summary
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• mlz-m03-using-the-model
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Sources

• Video
• Lesson file