Welcome to the Email Spam Classification Project repository! This project focuses on developing an advanced system for automatically distinguishing between spam (unsolicited, often malicious) and ham (legitimate) emails using machine learning techniques. The goal is to provide users with a reliable tool to filter their email inboxes effectively.
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Machine Learning Model: Utilizes state-of-the-art supervised learning algorithms, such as Support Vector Machines (SVM) and Random Forests, fine-tuned for binary classification of emails.
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High Accuracy: Achieves an accuracy of 97.87% in classifying emails, ensuring minimal false positives and negatives.
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Precision: Demonstrates a precision score of 93.28%, highlighting its ability to accurately identify spam without wrongly flagging legitimate emails.
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Scalability and Efficiency: Designed to handle large volumes of emails efficiently, suitable for both real-time email systems and standalone applications.
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Model Architecture: Built on a robust machine learning framework integrated with advanced Natural Language Processing (NLP) techniques for content and metadata analysis.
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Feature Engineering: Extracts relevant features from email content (e.g., bag-of-words, TF-IDF) and metadata (sender address, timestamp) to enhance classification accuracy.
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Deployment: The model is deployed through a user-friendly interface providing real-time feedback on email classification results.
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Continual model refinement through ongoing data collection and retraining to adapt to evolving spam tactics.
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Integration of more sophisticated NLP techniques and deep learning models for further improving accuracy and robustness.
To get a copy of the project up and running on your local machine for development and testing purposes, follow these steps:
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Clone the repository:
git clone https://github.com/your_username/email-spam-classification.git
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Install dependencies:
pip install -r requirements.txt
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Run the application:
python app.py
This will start the email classification system.
Contributions are welcome! If you have any ideas for improvements, please submit a pull request. For major changes, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License - see the LICENSE file for details.
- Hat tip to OpenAI for their GPT-3 model used in generating parts of this README.