✨ MailGuard AI: Email Spam Filter Demonstration

💌 Welcome to MailGuard AI, an interactive and educational demonstration of email spam filtering powered by Machine Learning. This project showcases how a Naive Bayes classifier can be used to detect spam emails in real time using a user-friendly Streamlit interface.

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🎯 Objective

The aim of this project is to demonstrate the power of Machine Learning in action — specifically how a trained algorithm can be used to classify emails as spam or ham (legitimate). With an easy-to-use web interface, users can input email messages and get immediate, intelligent results.

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🚀 Features

• 🔤 Interactive UI: Powered by Streamlit — type or paste an email to get instant feedback.
• 🧠 Naive Bayes Algorithm: Trained on real-world spam data and labeled ham messages.
• 📊 Confidence Score: See how sure the model is in its classification.
• 🔄 Feedback Loop: Provide feedback on classification accuracy to help refine the system.
• 🛠️ Error Handling: Built-in exception handling for a smooth user experience.
• 📚 Educational Focus: Designed to teach how ML models work behind the scenes.

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🧪 How It Works

I trained this model using:

• A dataset composed of my own collected spam emails
• An online dataset of ham (non-spam) emails
• Manual labeling to ensure accuracy

Using the Naive Bayes algorithm, a classic and efficient classification model, I built a lightweight yet effective spam detector. Then, I integrated it into a Streamlit web application to make it interactive and intuitive.

This project is not just about filtering spam — it’s about demonstrating machine learning concepts in a hands-on, beginner-friendly way.

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🧰 Requirements

🖥️ Hardware

• A laptop or desktop

💾 Software

• Python 3.x
• Jupyter Notebook
• PyCharm (Community Edition recommended)
• Streamlit
• Pandas
• Numpy

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📦 Getting Started

1. Clone the repository

   git clone https://github.com/yourusername/mailguard-ai.git
   cd mailguard-ai

2. Install the dependencies

   pip install -r requirements.txt

3. Run the app

   streamlit run app.py

4. Use the interface

• Type or paste an email message
• Click to classify it
• View the spam/ham result with confidence score
• Submit feedback if desired

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📉 Limitations

• This is a simplified demo and not a production-level spam filter
• Naive Bayes, while fast and effective, is not state-of-the-art for complex spam patterns
• No support for advanced spam defenses like blacklists or dynamic keyword detection

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