Enhancing email security and user productivity with an intelligent email spam classifier.
- About the Project
- Technologies Used
- Features
- Getting Started
- Usage
- ML Algorithm
- Screenshots
- Live Project Link
- Contributing
- License
- Author
Spam Detective is an intelligent email spam classifier built to enhance email security and user productivity. Leveraging machine learning techniques, Spam Detective efficiently identifies and filters out spam emails, providing users with a cleaner and safer email environment.
- Python
- Scikit-learn
- Streamlit
- Data Cleaning
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Developed an email spam classifier using Python's Scikit-learn library.
- Implemented natural language processing (NLP) techniques for text analysis.
- Created a machine learning pipeline for model building and evaluation.
- Built a user-friendly website interface using Streamlit.
- Deployed the application on the Render platform for seamless access.
To get started with Spam Detective, follow these steps:
Before running the project, ensure you have the following prerequisites installed on your system:
- Python
- pip (Python package manager)
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Clone the repository: Clone the Spam Detective repository to your local machine.
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Navigate to the project directory: After cloning the repository, navigate to the project directory.
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Install Dependencies: Install the required dependencies by running
pip install -r requirements.txt. -
Run the Streamlit App: Start the Streamlit app by running
streamlit run app.py. -
Access the Application: Open your web browser and navigate to the address shown in the terminal. You should now be able to access Spam Detective and begin exploring its features.
Once the Streamlit app is up and running, you can easily interact with Spam Detective's user-friendly interface. Input an email message, and the application will classify it as spam or not spam based on its content.
The machine learning algorithm used in Spam Detective is the Naive Bayes algorithm, specifically the Multinomial Naive Bayes classifier. This algorithm is particularly well-suited for text classification tasks like email spam detection.
Include screenshots of the application here to showcase its user interface and functionality.
Spam Detective - Trustworthy Spam Classifier
Contributions are welcome! If you have suggestions or improvements for the Spam Detective project, feel free to open an issue or create a pull request with necessary changes.
Distributed under the MIT License. See LICENSE for more information.
- Ayushmman Saini - Data Scientist - Your GitHub Profile