neelnsoni13/genderverification

📸 Application Screenshot

🛠 How It Works (End-to-End)

1. Data Preparation

• The dataset gender.xlsx contains names and their corresponding genders (Male/Female).
• The Gender column is mapped to numerical values:
  • Male (M) is mapped to 1
  • Female (F) is mapped to 0

2. Feature Extraction (TF-IDF Vectorization)

• The names are converted to TF-IDF vectors using character n-grams (1 to 3 characters).
• This helps the model learn important patterns in names.

3. Model Training

• A Neural Network is built using Keras Sequential API:
  • Dense layers with ReLU activation
  • Batch Normalization and Dropout layers to prevent overfitting
  • Output layer with Sigmoid activation for binary classification
• The model is trained with callbacks like early stopping and learning rate reduction.

4. Saving the Model and Vectorizer

• The trained model is saved as gender_prediction_model_Improve.h5
• The TF-IDF vectorizer is saved as tfidf_vectorizer_Improve.joblib

5. Streamlit Application

• Loads the pre-trained model and vectorizer.
• Accepts user input (name) and predicts gender.
• Displays the predicted gender in a clean UI.

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📝 Project File Structure

.
├── TrainImprove.py          # Training script for the model
├── ml-st1.py                 # Streamlit app for gender prediction
├── gender.xlsx              # Dataset with names and gender
├── gender_prediction_model_Improve.h5  # Saved Keras model
├── tfidf_vectorizer_Improve.joblib     # Saved TF-IDF vectorizer
└── screenshot.png           # Screenshot of the app UI

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🚀 How to Run the Project

1. Clone the Repository

$ git clone <repository-url>
$ cd <repository-folder>

2. Install Dependencies

$ pip install -r requirements.txt

3. Train the Model (Optional)

If you want to retrain the model, run the training script:

$ python TrainImprove.py

4. Run the Streamlit Application

$ python -m streamlit run ml-st.py 

5. Access the App

Open your browser and go to: http://localhost:8501

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💡 How the Code Works

Training (TrainImprove.py)

1. Data Loading: Reads the dataset from gender.xlsx.
2. Preprocessing: Converts names to TF-IDF vectors.
3. Model Building: Defines a neural network with regularization.
4. Model Training: Trains the model with early stopping.
5. Saving Artifacts: Stores the trained model (.h5) and vectorizer (.joblib).

Application (final.py)

1. Load Model and Vectorizer: Loads the pre-trained model and TF-IDF vectorizer.
2. User Input: Accepts a name input from the user.
3. Prediction: Transforms the name using TF-IDF and makes a prediction.
4. Output: Displays the predicted gender (Male/Female) in the app.

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📦 Dependencies

• Python 3.x
• TensorFlow
• Scikit-learn
• Pandas
• Streamlit

Install them using:

$ pip install tensorflow scikit-learn pandas streamlit joblib

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🎨 Future Enhancements

• Improve the UI design.
• Include more diverse datasets for better generalization.
• Add confidence scores for predictions.
• Deploy the app online for public access.

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🤝 Contributing

Feel free to fork the project and submit a pull request for improvements.

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📜 License

This project is licensed under the MIT License.