RwandaNameGenderModel
RwandaNameGenderModel is a machine learning model that predicts gender based on Rwandan names — whether a first name, surname, or both in any order. It uses a character-level n-gram approach with a logistic regression classifier to provide fast, interpretable, and highly accurate predictions — achieving 96%+ accuracy on both validation and test sets.
🧠 Model Overview
- Type: Classic ML (Logistic Regression)
- Input: Rwandan name (flexible: single or full name)
- Vectorization: Character-level n-grams (2–3 chars)
- Framework: scikit-learn
- Training Set: 66,735 names (out of 83,419)
- Validation/Test Accuracy: ~96.6%
📁 Project Structure
RwandaNameGenderModel/
├── dataset/
│ └── rwandan_names.csv
├── model/
│ ├── logistic_model.joblib
│ └── vectorizer.joblib
├── logs/
│ └── metrics_log.txt
├── train.py
├── inference.py
├── README.md
└── requirements.txt
🚀 Quickstart
1. Install requirements
pip install -r requirements.txt
2. Train the model
python train.py
3. Predict gender from a name using script
Run interactive inference with:
python inference.py
4. Predict gender from a name using Python code
from joblib import load
model = load("model/logistic_model.joblib")
vectorizer = load("model/vectorizer.joblib")
def predict_gender(name):
X = vectorizer.transform([name])
return model.predict(X)[0]
# Flexible input: first name, surname, or both (any order)
predict_gender("Gabriel") # Output: "male"
predict_gender("Baziramwabo") # Output: "male"
predict_gender("Baziramwabo Gabriel") # Output: "male"
predict_gender("Gabriel Baziramwabo") # Output: "male"
📈 Performance
| Dataset | Accuracy | Precision | Recall | F1-Score |
|---|---|---|---|---|
| Validation | 96.72% | 96.90% | 96.53% | 96.72% |
| Test | 96.64% | 96.94% | 96.34% | 96.64% |
Metrics are logged in both logs/metrics_log.txt and TensorBoard format.
🌍 Use Cases
- Demographic analysis
- Smart form processing
- Voice assistant personalization
- NLP preprocessing for Rwandan corpora
🛡️ Ethical Note
This model predicts binary gender based on patterns in names and may not reflect self-identified gender. It should not be used in sensitive contexts without consent.
📄 License
This project is maintained by Gabriel Baziramwabo and is open for research and educational use. For commercial use, please contact the author.
🤝 Contributing
We welcome improvements and multilingual extensions. Fork this repo, improve, and submit a PR!