GenderPred-Character-Level-Name-Classification-Using-Deep-Learning

This project uses deep learning and machine learning techniques to classify the gender of Brazilian names based on their character sequences. The models are trained on a large dataset of names and can predict whether a given name is typically male or female with high accuracy. The project demonstrates the power of character-level processing in natural language tasks.

Key Features

• Character-Level Input: The model processes names as sequences of characters rather than treating them as whole words, allowing it to capture subtle patterns in name structures.
• Multiple Deep Learning Models: The project includes several models—CNN, MLP, RNN, BiLSTM, and GRU—to compare performance and robustness across different neural network architectures.
• Open Dataset: The dataset used is publicly available, allowing for easy reproduction of results and further experimentation.
• Pre-trained Models: Links to pre-trained models are provided for quick deployment and testing without needing to train from scratch.

Dataset

The dataset consists of Brazilian names labeled with gender. It can be downloaded directly using the following Python code:

import requests

url = "https://data.brasil.io/dataset/genero-nomes/nomes.csv.gz"
filename = url.split("/")[-1]
with open(filename, "wb") as f:
    r = requests.get(url)
    f.write(r.content)






## References
This repository builds upon significant academic research in the field of name-based gender classification:

Rego, R. C., Silva, V. M. & Fernandes, V. M. (2021). Predicting Gender by First Name Using Character-level Machine Learning. arXiv preprint arXiv:2106.10156 v2.
Rego, R. C., & Silva, V. M. (2021). Predicting gender of Brazilian names using deep learning. arXiv preprint arXiv:2106.10156 v1.

R. C. B. Rego, G. d. S. Nascimento, D. E. d. L. Rodrigues, S. M. Nascimento, & V. M. L. Silva (2023). "Brazilian scientific productivity from a gender perspective during the Covid-19 pandemic: classification and analysis via machine learning," IEEE Latin America Transactions, vol. 21, no. 2, pp. 302-309, doi: 10.1109/TLA.2023.10015223.


## Conclusion
This repository offers a comprehensive toolkit for predicting gender based on first names using advanced machine learning techniques. Whether you are interested in exploring the intricacies of character-level models or need a robust solution for name-based gender prediction, this repository provides everything you need to get started.