updating requirements

Author: tkarim45

README.md

@@ -1 +1,84 @@
-# Email Spam Detection
+<h1 align="center">Email Spam Detection</h1>
+
+## About the Project
+
+I developed an email spam detection system using logistic regression, achieving an impressive accuracy of 98%. The model was trained on a comprehensive dataset of labeled emails, allowing it to effectively distinguish between spam and non-spam messages. The project is version-controlled using GitHub, facilitating collaboration and continuous integration. For deployment, I containerized the application using Docker, ensuring consistent performance across different environments. This streamlined approach not only enhances the model's reliability but also makes it scalable and easy to maintain.
+
+## Technologies Used
+
+![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)
+![Flask](https://img.shields.io/badge/flask-%23000.svg?style=for-the-badge&logo=flask&logoColor=white)
+![GitHub Actions](https://img.shields.io/badge/github%20actions-%232671E5.svg?style=for-the-badge&logo=githubactions&logoColor=white)
+![Docker](https://img.shields.io/badge/docker-%230db7ed.svg?style=for-the-badge&logo=docker&logoColor=white)
+
+## Getting Started
+
+To get a local copy up and running follow these simple steps.
+
+### Installation
+
+1. Clone the repo
+   ```sh
+   git clone https://github.com/tkarim45/Email-Spam-Detection-End-to-End-Deployment.git
+    ```
+2. Install Python packages
+    ```sh
+    pip install requirements.txt
+    ```
+3. Run the app
+    ```sh
+    python app.py
+    ```
+4. Access the app in your browser
+    ```sh
+    http://localhost:8080
+    ```
+</details>
+
+## Deployment on Docker
+
+To deploy the app using Docker, follow these steps:
+
+1. Build the Docker image
+    ```sh
+    docker build -t email-spam-detection .
+    ```
+2. Run the Docker container
+    ```sh
+    docker run -p 8080:8080 email-spam-detection
+    ```
+
+<> # Path: README.md
+<summary>Expand</summary>
+
+## Usage
+
+The app provides a simple interface for users to input an email and receive a prediction on whether it is spam or not. The model is highly accurate and can be used to filter out unwanted emails effectively.
+
+## Roadmap
+
+The project is currently in the final stages of development. Future updates will focus on improving the model's performance and adding more features to the app. I plan to integrate additional machine learning algorithms to enhance the accuracy of the spam detection system further. I also aim to deploy the app on a cloud platform to make it accessible to a wider audience.
+
+## Contributing
+
+Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.
+
+1. Fork the Project
+
+2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
+
+3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
+
+4. Push to the Branch (`git push origin feature/AmazingFeature`)
+
+5. Open a Pull Request
+
+## License
+
+Distributed under the MIT License. See `LICENSE` for more information.
+
+## Contact
+
+Your Name -
+
+Project Link: [