This project focuses on building a machine learning-based system to detect email spam. Email spam, also known as unsolicited bulk email, is a prevalent issue affecting communication channels. The goal of this project is to develop an efficient and accurate method to classify incoming emails as either spam or legitimate (ham).
Utilizing various machine learning algorithms such as Random Forest Classifier, Support Vector Classifier, and Multinomial Naive Bayes, this system aims to analyze the content and attributes of emails to distinguish between spam and non-spam messages. By employing these algorithms, we aim to create a robust and effective solution for filtering unwanted emails while allowing genuine communication to reach users' inboxes.
The project involves training these algorithms on labeled email datasets, fine-tuning them to achieve optimal performance, and implementing them within a user-friendly application or service. Ultimately, the objective is to provide users with a reliable tool that can significantly reduce the influx of unwanted and potentially harmful email content.
-
Random Forest Classifier
- Description or usage of the Random Forest Classifier in your project.
-
Support Vector Classifier
- Description or usage of the Support Vector Classifier in your project.
-
Multinomial Naive Bayes
- Description or usage of the Multinomial Naive Bayes in your project.
- Python (3.x recommended)
- Django
- Python libraries used in Machine Learning (e.g., scikit-learn, pandas, etc.)
-
Clone the repository:
git clone https://github.com/neeraj46665/email-spam-detection-using-ML.git
-
Navigate to the project directory:
cd your-project -
Create and activate a virtual environment (optional):
python -m venv venv source venv/bin/activate # For Windows: venv\Scripts\activate
-
Install Python dependencies:
pip install -r requirements.txt
-
Run migrations (if using Django):
python manage.py migrate
-
Start the server:
python manage.py runserver
-
Access the application in your browser at
http://localhost:8000/.
We welcome contributions from the community to improve this project! Here are a few guidelines to follow:
- Fork the repository and clone it to your local machine.
- Create a new branch for your feature or bug fix.
- Make your changes and test thoroughly.
- Ensure your code follows the project's coding style and conventions.
- Submit a pull request describing your changes and why they're needed.
This project is licensed under the MIT License.
You are free to use, modify, and distribute this software. However, please include the original attribution and license in any copy of the software/source.