  Predicting gender by the first name is not a simple task. In many
applications, especially in the natural language processing (NLP) field, this
task may be necessary, mainly when considering foreign names. In this paper, we
examined and implemented several machine learning algorithms, such as extra
trees, KNN, Naive Bayes, SVM, random forest, gradient boosting, light GBM,
logistic regression, ridge classifier, and deep neural network models, such as
MLP, RNN, GRU, CNN, and BiLSTM, to classify gender through the first name. A
dataset of Brazilian names is used to train and evaluate the models. We
analyzed the accuracy, recall, precision, f1 score, and confusion matrix to
measure the models' performances. The results indicate that the gender
prediction can be performed from the feature extraction strategy looking at the
names as a set of strings. Some models accurately predict gender in more than
95% of the cases. The recurrent models overcome the feedforward models in this
binary classification problem.
