neelnsoni13
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genderverification

Joblib

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neelnsoni13 commited on Feb 11

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@@ -1,50 +1,54 @@
----
-license: mit
----
 <html>
 # Gender Prediction from Names using Neural Network
 ### 🚀 **Project Overview**
 This project uses a **Neural Network** model trained on **TF-IDF vectors** of names to predict the gender (Male/Female). The model is deployed using **Streamlit**, making it easy to interact and predict the gender from a user-inputted name.
 ---
 ## 📸 **Application Screenshot**
-<a href="https://ibb.co/7JMfmKPp"><img src="https://i.ibb.co/Y7nCshmd/Screenshot-2025-02-11-222451.png" alt="Screenshot-2025-02-11-222451" 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
@@ -60,28 +64,34 @@ This project uses a **Neural Network** model trained on **TF-IDF vectors** of na
 ## 🚀 **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
 $ streamlit run final.py
 ```
 ### 5. **Access the App**
 Open your browser and go to: [http://localhost:8501](http://localhost:8501)
 ---
@@ -89,6 +99,7 @@ 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.
@@ -96,6 +107,7 @@ Open your browser and go to: [http://localhost:8501](http://localhost:8501)
 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.
@@ -104,6 +116,7 @@ Open your browser and go to: [http://localhost:8501](http://localhost:8501)
 ---
 ## 📦 **Dependencies**
 - Python 3.x
 - TensorFlow
 - Scikit-learn
@@ -111,6 +124,7 @@ Open your browser and go to: [http://localhost:8501](http://localhost:8501)
 - Streamlit
 Install them using:
 ```bash
 $ pip install tensorflow scikit-learn pandas streamlit joblib
 ```
@@ -118,6 +132,7 @@ $ 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.
@@ -126,14 +141,13 @@ $ pip install tensorflow scikit-learn pandas streamlit joblib
 ---
 ## 🤝 **Contributing**
 Feel free to fork the project and submit a pull request for improvements.
 ---
 ## 📜 **License**
-This project is licensed under the MIT License.
 </html>

 <html>
 # Gender Prediction from Names using Neural Network
 ### 🚀 **Project Overview**
 This project uses a **Neural Network** model trained on **TF-IDF vectors** of names to predict the gender (Male/Female). The model is deployed using **Streamlit**, making it easy to interact and predict the gender from a user-inputted name.
 ---
 ## 📸 **Application Screenshot**
+## <a href="https://ibb.co/JjhspDnr"><img src="https://ibb.co/JjhspDnr" alt="Screenshot-2025-02-11-222451" 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
 ## 🚀 **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
 $ streamlit run final.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.
 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.
 ---
 ## 📦 **Dependencies**
 - Python 3.x
 - TensorFlow
 - Scikit-learn
 - 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.
 ---
 ## 🤝 **Contributing**
 Feel free to fork the project and submit a pull request for improvements.
 ---
 ## 📜 **License**
+This project is licensed under the MIT License.
 </html>