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Description:

genderpredin 1.0.3

GenderPred-IN
GenderPred-IN is a Python package designed to predict the gender of a person based on their name. It is specifically tailored for Indian names, leveraging advanced machine learning techniques to provide prediction with accuracy ~96%.
PyPI
Features

LSTM Model: Utilizes a Long Short-Term Memory (LSTM) neural network model to understand the sequential patterns in names.

Tokenizer and Label Encoder: Processes names through a trained tokenizer and label encoder to convert names into a format suitable for the LSTM model.

Pre-trained Model: Comes with a pre-trained model, eliminating the need for extensive training and setup.

User-friendly: Simple and easy-to-use functions to get predictions with minimal setup.

Getting Started
Installation
You can install the package using pip (easy-peasy way):
pip install genderpred_in

or you can use github to install (harder way):

Clone the repository:

git clone https://github.com/DhrvM/GenderPred-India.git

cd GenderPred-India

Install the package:

pip install .

Verify the installation:

pip list

Usage
Import Package
from genderpred_in import classify_name, get_name, get_first_name, get_male_probability, get_female_probability, get_gender

Here is an example of how to use the package:
# Classify the name "Rohit"

result = classify_name("Rohit")

# Retrieve and print the results

full_name = get_name(result)

first_name = get_first_name(result)

male_prob = get_male_probability(result)

female_prob = get_female_probability(result)

gender = get_gender(result)

print(f"Full Name: {full_name}")

print(f"First Name: {first_name}")

print(f"Male Probability: {male_prob}")

print(f"Female Probability: {female_prob}")

print(f"Gender: {gender}")

Example Output:
Full Name: Rohit

First Name: ROHIT

Male Probability: 0.9916077852249146

Female Probability: 0.008392222225666046

Gender: male

Functions

classify_name(full_name): Classifies the given full name and returns a dictionary with the name, first name, gender, and probabilities.

get_name(result): Retrieves the full name from the classification result.

get_first_name(result): Retrieves the first name from the classification result.

get_male_probability(result): Retrieves the male probability from the classification result.

get_female_probability(result): Retrieves the female probability from the classification result.

get_gender(result): Retrieves the predicted gender from the classification result. (Output: male, female, unknown)

Versions
Version 1.0.2 Fixed Model loading error for Windows.\
Version 1.0.1 Uses LSTM model with a tokenized First-Name to Generate Predictions of Gender.
Built With

TensorFlow - The machine learning framework used

Keras - High-level neural networks API

NumPy - Used for numerical computing

Pandas - Data manipulation and analysis

Authors
Dhruv Malpani - Initial Work
LinkedIn
GitHub
License
This project is licensed under the MIT License - see the LICENSE.md file for details.
Acknowledgments
Praneeth Vasarla
Your article helped me create the initial model using Logistic Regression and n-grams. (article)