SpamSieve is an Email Classification Tool designed to categorize emails efficiently using Natural Language Processing (NLP) techniques and Long Short-Term Memory (LSTM) networks. The project encompasses a range of NLP preprocessing techniques to enhance email classification accuracy. Furthermore, the classification model is built using LSTM, a type of recurrent neural network (RNN) known for its effectiveness in sequential data processing tasks.
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NLP Techniques:
- Remove Header
- Remove HTML Tags
- Convert to Lowercase
- Remove Hyperlinks
- Remove Whitespace
- Remove Digits
- Remove Underscores
- Remove Stopwords
- Remove Special Characters
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LSTM for Classification
The model is deployed as an API using Flask, offering seamless integration and interaction with other systems and applications. The Flask framework provides a robust environment for hosting the model, enabling efficient and reliable access to the classification functionality.
- nltk
- Bs4
- Flask
- os
- re
- json
- keras
- sklearn
To utilize SpamSieve for email classification:
Ensure you have all Prerequisites installed on your system. Clone the repository to your local machine.
git clone https://github.com/oussama95boussaid/messages-emails-classifier.git
Navigate to the project directory.
cd messages-emails-classifier
Run the Flask application using the command python SpamSieve.py.
cd SpamSieve_deployment
python SpamSieve.py
Once the application is running, you can interact with the classification API using appropriate requests.
Contributions to SpamSieve are welcomed! Whether it's bug fixes, feature enhancements, or documentation improvements, your contributions are highly appreciated. Please feel free to submit pull requests or open issues for any suggestions or problems encountered.
Special thanks to the open-source community for providing invaluable resources and libraries that made this project possible.