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Gender Classification with Feature Engineering by Darsh Patel is a document available to read on EtoBox.

What is Gender Classification with Feature Engineering about?

The document details a machine learning lab exercise focused on gender classification using a dataset of 5001 entries with various facial features. It covers data analysis, feature engineering, and model evaluation using Naive Bayes classifiers for both categorical and continuous features, achieving high accuracy rates. Key findings include the creation of new features that enhance model performance and the successful conversion of the target variable into a numerical format for modeling.

Author

Darsh Patel

Language

EN