https://github.com/ksdkamesh99/ling-gender
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.
https://github.com/ksdkamesh99/ling-gender
deep-learning gender-classification gender-detection gender-from-name ling-gender lstm-neural-networks nlp-machine-learning recurrent-nueral-network
Last synced: 3 months ago
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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.
- Host: GitHub
- URL: https://github.com/ksdkamesh99/ling-gender
- Owner: ksdkamesh99
- License: mit
- Created: 2020-05-29T18:54:14.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T10:04:09.000Z (almost 3 years ago)
- Last Synced: 2025-05-07T08:44:31.350Z (5 months ago)
- Topics: deep-learning, gender-classification, gender-detection, gender-from-name, ling-gender, lstm-neural-networks, nlp-machine-learning, recurrent-nueral-network
- Language: Jupyter Notebook
- Homepage:
- Size: 23.2 MB
- Stars: 29
- Watchers: 2
- Forks: 13
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 👨Ling-Gender👩
## 📌 Introduction:-
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.
## 🏃♂️ Local Installation
1. Drop a ⭐ on the Github Repository.
2. Clone the Repo by going to your local Git Client and pushing in the command:
```sh
https://github.com/ksdkamesh99/Ling.git
```
3. Install the Packages:
```sh
pip install -r requirements.txt
```
4. At last, Go to 3.7.7 Python interpreter(Make Sure to create virtual env).
```sh
#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
```
5. Screenshots will be updated below.
## 📧Contact:-
For any kind of suggesstions/ help in package regarding improving accuracy of model. Please mail me at ksdkamesh99@gmail.com.
## 📜 LICENSE
[MIT](https://github.com/ksdkamesh99/Ling/blob/master/LICENSE)