/Detection_of_spam_email

This is an ML project where linear regression has been used to detect emails whether they are spam or ham.

Primary LanguageJupyter Notebook


Detection of Spam Mail

Table Of Contents

About The Project

Spam emails are unsolicited messages sent in bulk, often with malicious intent. They can be a significant nuisance, leading to wasted time, compromised security, and decreased productivity. This project focuses on building a spam mail detection system that can automatically identify and filter out spam emails.

The system utilizes machine learning algorithms to analyze various email features, such as subject line, sender information, and email content, to make predictions about whether an email is spam or legitimate. By training a model on labeled data, the system can learn patterns and characteristics associated with spam emails and make accurate predictions on unseen data.

Built With

This project aims to develop a spam mail detection system using machine learning algorithms. The system analyzes incoming emails and classifies them as either spam or legitimate based on their content and other features. The goal is to minimize the number of spam emails that reach the users' inbox, improving productivity and reducing the risk of falling victim to phishing attacks or other malicious activities.

Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Installation

To set up the spam mail detection system, follow these steps:

  1. Install the necessary dependencies specified in the requirements.txt file.
  2. Configure the email client to forward incoming emails to the spam mail detection system.
  3. Train the machine learning model using a labeled dataset of spam and legitimate emails.
  4. Fine-tune the model parameters and select the best-performing algorithm for your specific use case.
  5. Configure the blacklist and whitelist based on known spam email addresses, domains, or keywords.
  6. Integrate the spam mail detection system with the email client to enable real-time scanning and filtering of incoming emails.
  7. Monitor the system's performance metrics and user feedback to continually improve the accuracy and effectiveness of the spam detection model.

Usage

  1. Email Content Analysis: The system examines the content of each incoming email, including the subject line, body, and any attachments, to extract relevant features for spam detection.
  2. Machine Learning Algorithms: Various machine learning algorithms, such as Naive Bayes, Support Vector Machines (SVM), or Random Forest, are employed to build a robust classification model. These algorithms are trained using a labeled dataset of known spam and legitimate emails.
  3. Feature Extraction: The system performs feature extraction techniques, such as Bag of Words or Term Frequency-Inverse Document Frequency (TF-IDF), to convert the textual data into numerical representations that can be used by the machine learning algorithms.
  4. Blacklist and Whitelist: The system maintains a blacklist of known spam email addresses, domains, or keywords that are commonly associated with spam messages. Additionally, a whitelist can be implemented to ensure important emails from trusted senders are not mistakenly classified as spam.
  5. Real-Time Scanning: The system operates in real-time, scanning incoming emails as they arrive in the mailbox. This allows for immediate detection and filtering of spam messages.
  6. User Feedback and Training: Users have the ability to provide feedback on misclassified emails, marking them as spam or legitimate. This feedback is used to continuously improve the accuracy of the spam detection system through retraining of the machine learning model.
  7. Integration with Email Clients: The spam mail detection system can be integrated with popular email clients, such as Microsoft Outlook or Gmail, to provide seamless protection against spam messages.
  8. Performance Metrics: The system tracks performance metrics, including accuracy, precision, recall, and F1 score, to evaluate the effectiveness of the spam detection model and monitor its performance over time.

Contributing

Contributions to the spam mail detection system are welcome! If you encounter any issues or have suggestions for improvements, please submit a pull request or open an issue in the project repository.

When contributing, please follow the existing code style and ensure any new code is properly tested. Also, update the documentation as necessary to reflect the changes.

Creating A Pull Request

  1. Fork the Project
  2. Create your Feature Branch (https://github.com/Abhi- 187/Detection_of_spam_email.git)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch
  5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Authors

  • Abhi Patel - CSE Student at SRM University
  • Harsh Kelawala - CSE Student at SRM University
  • Zeal Shah - CSE Student at SRM University -

Acknowledgements

  • Dr. Vijayalakshmi V for Guidance and support