Email Spam Classification

This repository contains a Streamlit app for classifying email messages as spam or not spam using a machine learning model.

Table of Contents

• Overview
• Installation
• Usage
• Model Training
• Deployment
• Contributing
• License

Overview

The Email Spam Classification app uses a trained machine learning model to predict whether an email message is spam or not. The app is built with Streamlit and deployed on Streamlit Cloud.

Installation

1. Clone the repository:

   git clone https://github.com/navyabijoy/email-spam-classification.git
   cd email-spam-classification

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the dependencies:

   pip install -r requirements.txt

Usage

1. Run the Streamlit app:

   streamlit run main.py

2. Interact with the app:

   Open your browser and go to http://localhost:8501 to interact with the app. Enter an email message in the text area and click the "Predict" button to see if it is classified as spam or not.

Model Training

If you want to train the model yourself, follow these steps:

1. Prepare the dataset:

   Ensure you have a dataset of email messages labeled as spam or not spam.

2. Train the model:

   Use the provided Jupyter notebook or script to train a new model. Save the trained model as model.pkl and the vectorizer as vectorizer.pkl.

Deployment

To deploy the app on Streamlit Cloud:

1. Push your code to GitHub:

   Ensure your repository is up to date on GitHub.

2. Deploy on Streamlit Cloud:

   Follow the Streamlit Cloud documentation to deploy your app.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any changes.

License

This project is licensed under the MIT License.