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https://github.com/pjparties/farzee


https://github.com/pjparties/farzee

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README 

        
# Farzee - Spam Detection using NLP



![Farzee Logo](farzee_logo.png)



## Overview



Farzee is a powerful spam detection tool built using Natural Language Processing (NLP) techniques. It helps users identify and filter out spam messages and emails, ensuring that important communications remain unaffected.



## Table of Contents



- [Introduction](#introduction)

- [Features](#features)

- [How it Works](#how-it-works)

- [Usage](#usage)

- [Contributing](#contributing)

- [License](#license)



## Introduction



In a world filled with information, distinguishing between genuine and spammy messages and emails can be challenging. Farzee comes to the rescue by employing advanced NLP algorithms to analyze the content of messages and emails, assigning a spam probability score to each message. Users can then set thresholds to automatically filter out spam.



## Features



- **NLP-Based Spam Detection**: Farzee utilizes state-of-the-art NLP techniques to identify spam messages and emails.

- **Customizable Thresholds**: Users can adjust the spam probability threshold to fine-tune spam filtering according to their preferences.

- **Integration-Friendly**: Farzee can be integrated into various messaging and email platforms for seamless spam detection.

- **User-Friendly Interface**: The user interface is intuitive, making it easy for users to manage their spam filters.

- **Detailed Reporting**: Farzee provides detailed reports on detected spam messages and emails.

- **Real-Time Detection**: Messages and emails are scanned in real-time for instant spam identification.



## How it Works



Farzee employs a combination of text preprocessing techniques, feature extraction, and machine learning models to determine the likelihood that a message or email is spam. The core steps include:



1. **Text Preprocessing**: The content is cleaned and transformed into a suitable format for analysis.



2. **Feature Extraction**: Relevant features are extracted from the text, including keywords, n-grams, and other linguistic attributes.



3. **NLP Analysis**: The NLP model evaluates the extracted features and assigns a spam probability score.



4. **Threshold Comparison**: The score is compared to a user-configurable threshold to determine if the message or email is spam.



5. **Filtering**: Messages and emails exceeding the threshold are filtered out or flagged as spam.



## Usage



Integrate Farzee into your messaging or email platform following the provided documentation. Users can configure threshold settings and monitor spam reports through the user-friendly interface.



## Contributing



Contributions to Farzee are encouraged! If you have ideas for improvements, bug reports, or want to contribute new features, please check out the [contribution guidelines](CONTRIBUTING.md).



## License



Farzee is open-source software licensed under the [MIT License](LICENSE). You are free to use, modify, and distribute it according to the terms of this license.



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**Disclaimer**: Farzee is designed to assist in spam detection but may not be perfect. Exercise caution and verify important messages and emails when using this tool.