Names Classifier App: predicting gender by name

Overview

Machine learning-based web-application predicting gender of a person by the first (given) name.
Despite the more or less accurate classification of the existent names, the app defines the gender and probability of being "more male" / "more female" for any newly invented name.

ML-Model

Data used

ML Model is trained on approximately 60k popular international names. I used several official datasets of most popular given names from EU bureaus of statistic (Netherlands, France, Sweden); plus a large corporate dataset of an internationally operated company, having a good representation of the employees names. (all original datasets are as .csv in classifier_modeling/data). Names origins representation:

• The dataset has good coverage of European, American (based on roman languages) names;
• potentially good representation of Eastern-European and slavic names (written in latin transliteration);
• some coverage of Middle-Eastern, Middle-Asian most common names (written in latin transliteration);
• almost no coverage of Far-Eastern and South-Asian names.

Model principles

The key component is an approach of transforming a name into features vector. The program takes prefix and suffix of a name and hashing it into d-dimensional vector. Number of symbols in the suffix and prefix as well as number of dimensions substantially impact the performance of the model.

Empirically, the best performing classifier was chosen with next parameters:

• number of symbols for suffix / prefix: 3;
• dimensions: 320;
• algorithm: random forest classifier with 50 trees and unlimited leaves. (50 trees is a good tradeoff between quality and model weight)

Details of the model are in the jupyter notebook: classifier_modelling/NamesClassifier_modeling.ipynb

Credits

The name classification method was suggested by Cornell University in their ML Certification Course with the reference to an original idea of Nick Montfort).

How to run locally

1. Install all required packages from requirements.txt.
   $ pip install -r requirements.txt
   
   
2. Run the run.py
$ python3 run.py
   
   
3. Application will be run on the browser at the local host http://127.0.0.1:5000

Architecture

classifier_modeling/                    # directory for initial dataset and model creation
├── data
└── NamesClassifier_modeling.ipynb      # process of the model selection and training

application/                            # Flask application directory
├── static/
│   └── trained_model_rf2.joblib        # trained model preserved with joblib
├── temlates/
│   └── index.html                      # single HTML-page 
├── classifier.py                       # module defining NameClassifier class with name hashing and prediction functionality
├── forms.py                            # module defining web-form (wtform) class
├── views.py                            # routing of the Flask app
├── config.py                           # configuration file
└── __init__.py                         # app initiation
        
.env                                    # vars of the environment
Procfile                                # startup for Heroku deployment
requirements.txt                        # dependencies
run.py                                  # entry point of the app

Dependencies

python 3

Packages:

• Flask~=2.2.2
• numpy==1.23.4
• scikit-learn==1.1.3
• scipy==1.9.3
• joblib~=1.2.0
• python-dotenv~=0.21.0
• WTForms~=3.0.1
• numpy~=1.23.4
• Unidecode~=1.3.6