GithubHelp home page GithubHelp logo

email-spam-detector's Introduction

Email-Spam-Detector

Description:

My Email Spam Detector is a robust solution designed to effectively identify and filter out spam emails, ensuring a safer and clutter-free inbox experience. Leveraging powerful machine learning algorithms and advanced natural language processing techniques, this detector accurately distinguishes between legitimate emails and spam, providing users with peace of mind and enhanced productivity.

Key Features:

Data-driven Approach: My detector utilizes a dataset of emails labeled as spam or non-spam (ham) to train a machine learning model, ensuring high accuracy and reliability in classifying incoming emails.

Text Representation: Emails are processed and converted into numerical vectors using TF-IDF (Term Frequency-Inverse Document Frequency) vectorization, capturing important features and patterns in the email content.

Logistic Regression Model: The core of our detector is a Logistic Regression model, trained on the TF-IDF vectors of emails to learn patterns indicative of spam or non-spam content.

Evaluation Metrics: We employ standard evaluation metrics such as accuracy to assess the performance of our model, ensuring its effectiveness in accurately classifying emails.

How it Works:

Data Preprocessing: Email data is preprocessed to remove irrelevant information such as HTML tags, punctuation, and stop words, ensuring a clean and standardized dataset for training.

Feature Extraction: The text content of emails is converted into numerical feature vectors using TF-IDF vectorization, preserving the semantic meaning of words while capturing their importance in each email.

Model Training: A Logistic Regression model is trained on the TF-IDF vectors of emails, learning to distinguish between spam and non-spam content based on the patterns observed in the training data.

Prediction: Upon receiving a new email, the detector applies the trained model to predict whether the email is spam or non-spam, providing users with real-time classification.

Benefits:

Enhanced Security: Protects users from phishing attempts, malware, and other malicious activities commonly associated with spam emails, reducing the risk of security breaches and identity theft.

Improved Productivity: By filtering out spam emails, users can focus on important tasks without being inundated with irrelevant or potentially harmful content, leading to increased productivity and efficiency.

Customizable Thresholds: Users have the flexibility to adjust the sensitivity of the detector based on their preferences, allowing them to strike a balance between reducing false positives and minimizing the risk of missing legitimate emails.

Conclusion:

My Email Spam Detector offers a comprehensive solution for effectively identifying and filtering out spam emails, providing users with a safer and more streamlined email experience. With its advanced machine learning algorithms and intuitive interface, users can enjoy a clutter-free inbox while minimizing the risk of falling victim to email-based threats.

email-spam-detector's People

Contributors

garvanand avatar

Stargazers

 avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.