Gender Classification

Predicting gender from a name is deceptively tricky:

• Dictionaries don't generalize. A lookup table fails on rare, novel, or compound names; the signal really lives in how a name is spelled.

• Sub-word patterns matter. Endings and letter combinations carry most of the gender signal, which a character-level model can learn directly.

• Order counts. The same letters in different orders mean different things, motivating a sequence model (CNN for local patterns + LSTM for order) over a bag-of-characters.

• Short, variable input. Names are short and vary in length, so fixed-length padding and a compact network were appropriate.

• Needs to be callable. The classifier had to run as a service, not just a notebook.

• Character preprocessing. Lowercase the name and split it into a list of characters.

• Sequence encoding. A fitted character tokenizer maps characters to integers; sequences are padded to a fixed max length (40).

• Character embedding. A learned Embedding layer turns character ids into dense vectors.

• CNN + LSTM model. A Conv1D layer extracts local letter-pattern features, an LSTM models character order, and Dense → sigmoid produces the binary gender probability; an LSTM-stacked variant was also trained for comparison.

• Flask serving. A small Flask app exposes a /predict_gender endpoint that runs the full preprocess→encode→predict pipeline on an incoming name.

• Dictionary-free prediction that generalizes to unseen names from spelling alone.

• Character-level modeling that learns the sub-word patterns carrying the gender signal.

• Sequence-aware (CNN+LSTM) rather than treating a name as an unordered bag of letters.

• Reusable as a service via a simple HTTP endpoint.

• Reproducible workflow — model training and prediction captured as notebooks with saved artifacts.