* feat: implement representative sampling by province (~500k records), extract surnames from the first token of name, build letter transition matrices (frequency and probability), add heatmap visualization for transitions, and integrate a Markov chain–based name generator.
* Implemented letter frequency analysis with histograms, computed bigram and trigram frequencies, and displayed the top results in tabular format. Rebuilt the transition probability matrix, and developed a name generator capable of producing realistic outputs based on surname data.
Amaury Cansa
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