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---
license: mit
---
## 📸 **Application Screenshot**
## <a href="https://ibb.co/JjhspDnr"><img src="https://i.ibb.co/ZzZMgqdc/Screenshot-2025-02-11-231313.png" alt="Screenshot-2025-02-11-231313" border="0" /></a>
## 🛠 **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.
---
## 📝 **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
```
---
## 🚀 **How to Run the Project**
### 1. **Clone the Repository**
```bash
$ git clone <repository-url>
$ cd <repository-folder>
```
### 2. **Install Dependencies**
```bash
$ pip install -r requirements.txt
```
### 3. **Train the Model (Optional)**
If you want to retrain the model, run the training script:
```bash
$ python TrainImprove.py
```
### 4. **Run the Streamlit Application**
```bash
$ python -m streamlit run ml-st.py
```
### 5. **Access the App**
Open your browser and go to: [http://localhost:8501](http://localhost:8501)
---
## 💡 **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.
---
## 📦 **Dependencies**
- Python 3.x
- TensorFlow
- Scikit-learn
- Pandas
- Streamlit
Install them using:
```bash
$ pip install tensorflow scikit-learn pandas streamlit joblib
```
---
## 🎨 **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.
---
## 🤝 **Contributing**
Feel free to fork the project and submit a pull request for improvements.
---
## 📜 **License**
This project is licensed under the MIT License.
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