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Birth certificate dataset

As explained in Natural Language Processing in Action, Second Edition

A birth certificate dataset is a collection of birth-record entries containing names, recorded sex, and associated counts or demographic fields.

A birth certificate dataset collects name records with recorded sex, birth year, region, counts, and frequencies. Here, it supplies examples for predicting recorded sex from name patterns. section 5.2.4

Aggregating names and recorded sex

Grouping by name and sex sums counts across records: Timothy appears with 5 recorded female births and 3,538 recorded male births. The resulting 4,238-row table can support training and testing.

A modeling workflow uses the dataset to:

  • Keep names from the test set out of training and retain the resulting feature matrix as sparse data. section 5.2.4

How Natural Language Processing in Action, Second Edition covers it

How deep it goes
Works through aggregation, splitting, vectorization, and sparse-matrix handling, but does not show completed neural-network training.
Examples
Demonstrates regional and yearly records, Timothy counts, aggregated name-sex rows, and character n-gram vectors.
What it assumes
Uses Python, pandas, NumPy, and basic grouping and indexing operations before introducing feature vectorization.
What you can do after
Apply it via pandas and scikit-learn to prepare labeled name data and character-feature matrices for classification.
How it connects
Connects birth records with names, recorded sex, TF-IDF, logistic regression, neural networks, and train-test splitting.
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