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Voice Gender Identification is a project aimed at building a machine learning model that can classify voice recordings as male or female based on acoustic properties. Using a dataset of 3,168 voice samples with 20 different acoustic features like frequency, pitch, and energy, the project applies various classification algorithms such as Logistic Regression, KNN, Random Forest, Decision Tree, SVM, and Gradient Boosting to predict gender. The project involves data preprocessing, feature engineering, model creation, and parameter tuning to achieve optimal results.