A Natural Language Processing model trained with over 1,00,000 (1 Lakh) names is used to predict a gender of a person based on the first name of the person.This model is created using Long Short Term Memory(LSTM) a variant of Recurrent Nueral Network which has training accuracy of 99.35% and tested over 11,000 samples with a test accuracy of 89.08% which is quite high in nlp for out of sample test cases.
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Drop a ⭐ on the Github Repository.
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Clone the Repo by going to your local Git Client and pushing in the command:
https://github.com/ksdkamesh99/Ling.git- Install the Packages:
pip install -r requirements.txt- At last, Go to 3.7.7 Python interpreter(Make Sure to create virtual env).
#Import Ling as l in any python file/Interpreter(note it is present in the same directory)
import Ling as l
print(l.gender("kamesh"))
# Output will be 1 which means male
print(l.gender("sudha"))
#Output will be 0 which means female- Screenshots will be updated below.
For any kind of suggesstions/ help in package regarding improving accuracy of model. Please mail me at [email protected].