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Screenshot 2024-08-06 202809

Spam Filter AI

Spam Filter AI is a Python application designed to classify emails as spam or non-spam using machine learning techniques. By utilizing Natural Language Processing (NLP) and Naive Bayes classification, this tool helps maintain an organized and spam-free inbox.

🚀 Project Overview

Spam Filter AI employs advanced machine learning methods to process and analyze email content, categorizing it as spam or non-spam. Key components include:

  • Natural Language Processing (NLP): For analy! zing and understanding text.
  • Naive Bayes Classification: For spam detection.
  • TF-IDF Vectorization: To convert text into numerical features.

Key Features

  • Direct Email Pasting: Users can paste email content directly into the application.
  • Real-Time Classification: Provides instant classification of email content.
  • Modern GUI: Intuitive interface for ease of use.
  • Cross-Platform Compatibility: Works on Windows, macOS, and Linux.

🛠️ Technologies Used

  • Python: Main programming language.
  • scikit-learn: For machine learning algorithms and preprocessing.
  • tkinter: For creating the graphical user interface.
  • pandas: For data manipulation and analysis.
  • NLTK: For text processing and NLP.

📂 Project Structure

Here's the structure of the project directory:

Spam-Filter-AI/
├── data/
│   ├── email.csv
│   ├── emails.csv
│   ├── preprocessed_emails.csv
├── src/
│   ├── __pycache__/
│   ├── __init__.py
│   ├── data_preprocessing.py
│   ├── evaluation.py
│   ├── feature_extraction.py
│   ├── gui.py
│   ├── model.py
├── venv/
│   ├── Include/
│   ├── Lib/
│   ├── Scripts/
│   ├── pyvenv.cfg
├── .gitignore
├── LICENSE
├── README.md
├── requirements.txt
├── spam_detector_model.pkl
├── tfidf_vectorizer.pkl
├── X_features.pkl
├── X_test.pkl
├── y_test.pkl

Data Directory

  • data/: This directory is used for storing datasets.
    • email.csv: Contains raw email data for processing.
    • emails.csv: A dataset used for training and testing the model.
    • preprocessed_emails.csv: Contains emails that have been preprocessed for model training.

Source Code Directory

  • src/: Contains all the source code files.
    • data_preprocessing.py: Handles the preprocessing of raw email data.
    • evaluation.py: Evaluates the performance of the model.
    • feature_extraction.py: Extracts features from email content for model training.
    • gui.py: Manages the graphical user interface.
    • model.py: Contains code for model training and prediction.

📥 Installation Guide

Prerequisites

Setup Instructions

  1. Clone the Repository

    git clone https://github.com/sd338/spam-filter-ai.git

  2. Navigate to the Project Directory

    cd spam-filter-ai

  3. Create and Activate a Virtual Environment

    • Windows:

      python -m venv venv
.\venv\Scripts\activate

    • macOS/Linux:

      python3 -m venv venv
source venv/bin/activate

  4. Install Required Packages

    pip install -r requirements.txt

📈 Usage Instructions

Running the Application

  • Windows:

    python src/gui.py

  • macOS/Linux:

    python3 src/gui.py

How to Use

  1. Paste Email Content: Copy and paste email content into the text area in the GUI.
  2. Submit Email: Click "Submit Email" to classify the content.
  3. Delete Mail: Click "Delete Mail" to clear the text area.

Data Files

  • Place your raw email data files (e.g., email.csv, emails.csv) in the data/ directory.
  • The preprocessed data file (preprocessed_emails.csv) should also be placed in the data/ directory after preprocessing.

📊 Data

Datasets are sourced from Kaggle. To obtain:

  1. Visit Kaggle: Go to Kaggle Datasets.
  2. Search for Spam Datasets: Use keywords like "spam email dataset."
  3. Download and Place in data/ Directory: Save the datasets here.

Example Datasets:

🤝 Contributing

Contributions are welcome! Here’s how to contribute:

  1. Fork the Repository: Click "Fork" on GitHub.
  2. Clone Your Fork: 
    git clone https://github.com/your-username/spam-filter-ai.git
  3. Create a New Branch: 
    git checkout -b feature-or-bugfix-name
  4. Make Changes: Implement your features or fixes.
  5. Commit and Push: 
    git add .
git commit -m "Description of changes"
git push origin feature-or-bugfix-name
  6. Submit a Pull Request: Open a pull request on GitHub.

📝 License

This project is licensed under the GNU General Public License v3.0. The GPL-3.0 is a strong copyleft license that requires you to make the source code of the project available if you distribute or modify the software. For more details, visit the GNU General Public License v3.0 page.

Permissions

  • Commercial Use: Allowed
  • Modification: Allowed
  • Distribution: Allowed
  • Patent Use: Allowed
  • Private Use: Allowed

Limitations

  • Liability: No warranty is provided.
  • Warranty: The software is provided "as-is."

Conditions

  • License and Copyright Notice: Must be included in all copies and substantial portions of the software.
  • State Changes: Modified versions must also be licensed under GPL-3.0.
  • Disclose Source: Source code must be made available when distributing binaries or modified versions.
  • Same License: Modified versions must be distributed under GPL-3.0.

📧 Contact

For questions or support, please reach out via the contact methods on my GitHub profile. Note that the email address provided in the GUI (support@spamfilterai.com) is fictional and used for demonstration purposes only.