# 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](https://pypi.org/project/genderpred-in/1.0.1/)
### 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):**
```bash
pip install genderpred_in
```
**or you can use **github** to install (harder way):**
1. Clone the repository:
```bash
git clone https://github.com/DhrvM/GenderPred-India.git
cd GenderPred-India
```
3. Install the package:
```bash
pip install .
```
5. Verify the installation:
```bash
pip list
```
### Usage
**Import Package**
```python
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:**
```python
# 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:
```bash
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](https://www.tensorflow.org/) - The machine learning framework used
- [Keras](https://keras.io/) - High-level neural networks API
- [NumPy](https://numpy.org/) - Used for numerical computing
- [Pandas](https://pandas.pydata.org/) - Data manipulation and analysis
## Authors
**Dhruv Malpani** - Initial Work
[LinkedIn](https://www.linkedin.com/in/dhruv-malpani/)
[GitHub](https://github.com/DhrvM)
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.
## Acknowledgments
[**Praneeth Vasarla**](https://medium.com/@praneethvaasarla)
Your article helped me create the initial model using Logistic Regression and n-grams. ([article](https://medium.com/@praneethvaasarla/how-i-used-nlp-to-predict-the-gender-for-indian-names-df0ae30c275b))
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"description": "\r\n\r\n# GenderPred-IN\r\n\r\nGenderPred-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%.\r\n\r\n[PyPI](https://pypi.org/project/genderpred-in/1.0.1/)\r\n\r\n\r\n\r\n### Features\r\n\r\n- LSTM Model: Utilizes a Long Short-Term Memory (LSTM) neural network model to understand the sequential patterns in names.\r\n\r\n- Tokenizer and Label Encoder: Processes names through a trained tokenizer and label encoder to convert names into a format suitable for the LSTM model.\r\n\r\n- Pre-trained Model: Comes with a pre-trained model, eliminating the need for extensive training and setup.\r\n\r\n- User-friendly: Simple and easy-to-use functions to get predictions with minimal setup.\r\n\r\n\r\n\r\n\r\n\r\n## Getting Started\r\n\r\n### Installation\r\n\r\n**You can install the package using pip (easy-peasy way):**\r\n\r\n```bash\r\n\r\npip install genderpred_in\r\n\r\n```\r\n\r\n**or you can use **github** to install (harder way):**\r\n\r\n\r\n\r\n 1. Clone the repository:\r\n\r\n ```bash\r\n\r\n git clone https://github.com/DhrvM/GenderPred-India.git\r\n\r\ncd GenderPred-India\r\n\r\n ```\r\n\r\n 3. Install the package:\r\n\r\n ```bash\r\n\r\n pip install .\r\n\r\n```\r\n\r\n 5. Verify the installation:\r\n\r\n ```bash\r\n\r\n pip list\r\n\r\n ```\r\n\r\n\r\n\r\n### Usage\r\n\r\n\r\n\r\n**Import Package**\r\n\r\n```python\r\n\r\nfrom genderpred_in import classify_name, get_name, get_first_name, get_male_probability, get_female_probability, get_gender\r\n\r\n```\r\n\r\n**Here is an example of how to use the package:**\r\n\r\n```python\r\n\r\n# Classify the name \"Rohit\"\r\n\r\nresult = classify_name(\"Rohit\")\r\n\r\n\r\n\r\n# Retrieve and print the results\r\n\r\nfull_name = get_name(result)\r\n\r\nfirst_name = get_first_name(result)\r\n\r\nmale_prob = get_male_probability(result)\r\n\r\nfemale_prob = get_female_probability(result)\r\n\r\ngender = get_gender(result)\r\n\r\n\r\n\r\nprint(f\"Full Name: {full_name}\")\r\n\r\nprint(f\"First Name: {first_name}\")\r\n\r\nprint(f\"Male Probability: {male_prob}\")\r\n\r\nprint(f\"Female Probability: {female_prob}\")\r\n\r\nprint(f\"Gender: {gender}\")\r\n\r\n```\r\n\r\n\r\n\r\n### Example Output:\r\n\r\n\r\n\r\n```bash\r\n\r\nFull Name: Rohit\r\n\r\nFirst Name: ROHIT\r\n\r\nMale Probability: 0.9916077852249146\r\n\r\nFemale Probability: 0.008392222225666046\r\n\r\nGender: male\r\n\r\n```\r\n\r\n\r\n\r\n### Functions\r\n\r\n- `classify_name(full_name)`: Classifies the given full name and returns a dictionary with the name, first name, gender, and probabilities.\r\n\r\n- `get_name(result)`: Retrieves the full name from the classification result.\r\n\r\n- `get_first_name(result)`: Retrieves the first name from the classification result.\r\n\r\n- `get_male_probability(result)`: Retrieves the male probability from the classification result.\r\n\r\n- `get_female_probability(result)`: Retrieves the female probability from the classification result.\r\n\r\n- `get_gender(result)`: Retrieves the predicted gender from the classification result. (Output: *male, female, unknown*)\r\n\r\n\r\n\r\n## Versions\r\n\r\n`Version 1.0.2` Fixed Model loading error for Windows.\\\r\n\r\n`Version 1.0.1` Uses LSTM model with a tokenized First-Name to Generate Predictions of Gender.\r\n\r\n\r\n\r\n\r\n\r\n## Built With\r\n\r\n\r\n\r\n- [TensorFlow](https://www.tensorflow.org/) - The machine learning framework used\r\n\r\n- [Keras](https://keras.io/) - High-level neural networks API\r\n\r\n- [NumPy](https://numpy.org/) - Used for numerical computing\r\n\r\n- [Pandas](https://pandas.pydata.org/) - Data manipulation and analysis\r\n\r\n\r\n\r\n## Authors\r\n\r\n**Dhruv Malpani** - Initial Work\r\n\r\n[LinkedIn](https://www.linkedin.com/in/dhruv-malpani/)\r\n\r\n[GitHub](https://github.com/DhrvM)\r\n\r\n\r\n\r\n## License\r\n\r\n\r\n\r\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.\r\n\r\n\r\n\r\n## Acknowledgments\r\n\r\n[**Praneeth Vasarla**](https://medium.com/@praneethvaasarla)\r\n\r\nYour article helped me create the initial model using Logistic Regression and n-grams. ([article](https://medium.com/@praneethvaasarla/how-i-used-nlp-to-predict-the-gender-for-indian-names-df0ae30c275b))\r\n\r\n",
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