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LSTM-based Classifier: Utilizes Long Short-Term Memory networks for sequential pattern recognition in email content. SVM Classifier: Implements Support Vector Machines for efficient email spam detection based on distinct feature mapping. Repository includes LSTM and SVM models with documentation for robust email spam classification.

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sarankumar1325/TRS_EMAIL_SPAM_CLASSIFIER

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EMAIL SPAM CLASSIFIER PROJECT DOCUMENTATION

I am pleased to present the culmination of my efforts: the Email Spam Classifier Repository, which is specifically tailored to the goals of our internal project. This repository aims to streamline and optimise email classification for spam detection by providing a set of classifiers with comprehensive documentation.

The repository includes a carefully curated selection of classifiers that have been meticulously developed to distinguish spam email from legitimate email. Each classifier has undergone rigorous testing and refinement to ensure its accuracy and efficiency in effectively categorising email content.

In addition to the classifiers, I have carefully compiled detailed documentation. This documentation serves as a roadmap that explains the features, implementation steps and best practises for using these classifiers in our project framework. It should simplify the integration process and enable us to utilise these tools seamlessly.

I believe that this repository holds immense potential for improving the efficiency of our email classification project. By centralising these classifiers and providing comprehensive documentation, I wanted to create a resource that would streamline our efforts and promote a more robust approach to spam detection.

I invite you to explore the repository, test the classifiers, and read the documentation. Your feedback and insights will be invaluable and will go a long way to further refining and optimising this resource.

Thank you for this opportunity to contribute to our project. I look forward to hearing your thoughts and suggestions on the Email Spam Classifier Repository.

Yours sincerely,

SARAN KUMAR S,

Intern,

The Fusion Apps.

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LSTM-based Classifier: Utilizes Long Short-Term Memory networks for sequential pattern recognition in email content. SVM Classifier: Implements Support Vector Machines for efficient email spam detection based on distinct feature mapping. Repository includes LSTM and SVM models with documentation for robust email spam classification.

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