https://github.com/hanymedhat10/spam-emails-classify-ml

A machine learning model that classifies emails as spam or legitimate using natural language processing techniques.
https://github.com/hanymedhat10/spam-emails-classify-ml

machine-learning ml nlp nlp-machine-learning nltk sklearn spam spam-email-classifier

Last synced: 19 days ago
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A machine learning model that classifies emails as spam or legitimate using natural language processing techniques.

• Host: GitHub
• URL: https://github.com/hanymedhat10/spam-emails-classify-ml
• Owner: HanyMedhat10
• Created: 2024-10-20T12:13:39.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-10-20T20:29:25.000Z (30 days ago)
• Last Synced: 2024-10-31T11:07:18.086Z (19 days ago)
• Topics: machine-learning, ml, nlp, nlp-machine-learning, nltk, sklearn, spam, spam-email-classifier
• Language: Jupyter Notebook
• Homepage:
• Size: 5.02 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Spam Email Classifier

A machine learning model that classifies emails as spam or legitimate using natural language processing techniques.

## 🎯 Project Overview

This project implements a machine learning model to automatically detect and classify spam emails. The classifier helps users filter out unwanted emails by analyzing email content and metadata using various ML techniques.

## ✨ Features

- Email text preprocessing and cleaning

- Feature extraction using TF-IDF vectorization

- Machine learning classification model

- High accuracy in distinguishing between spam and legitimate emails

- Easy-to-use interface for email classification

## 🛠️ Technologies Used

- Python 3.x

- scikit-learn

- pandas

- NLTK

- NumPy

- Seaborn

- XGboost

## 📋 Requirements

```

pandas>=1.2.0

scikit-learn>=0.24.0

nltk>=3.6.0

numpy>=1.19.0

seaborn>=0.7.1

xgboost>=2.0.1

```

## 🚀 Installation

1. Clone the repository

```bash

git clone https://github.com/HanyMedhat10/Spam-Emails-Classify-ML.git

cd spam-email-classifier-ML

```

2. Install required packages

```bash

pip install -r requirements.txt

```

## 📊 Models comparison Accuracy



## 📊 The Best Model Performance

- Accuracy: 98%

- Precision: 99%

- Recall: 98%

- F1-Score: 98%

## 🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## 📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

## 👥 Contact

- Hany Medhat

- Email: 

- GitHub: [@HanyMedhat10](https://github.com/HanyMedhat10)