Spam Detection with Naive Bayes Algorithm

Welcome to the Spam Detection project, a powerful tool for identifying and filtering spam messages using the Naive Bayes algorithm. This project is designed to provide an efficient and accurate solution for distinguishing between legitimate and spam messages. 🛡️📧

Features

• Naive Bayes Algorithm: Utilizes the Naive Bayes classification algorithm for robust and effective spam detection.
• Performance Metrics: Accurate measures of the model's performance including Accuracy, Precision, Recall, and F1 Score.

How It Works

1. Data Collection: Gather a dataset containing examples of both spam and non-spam (ham) messages.(NOTE: the data will be downloaded automatically after running the notebook, otherwise you can download the data from here
2. Training the Model: Train the Naive Bayes algorithm using the collected dataset to build a reliable spam detection model.
3. Testing and Validation: Evaluate the model's performance with testing data to ensure accurate classification. 🧪
4. Model Integration: Incorporate the trained model into your application for real-time spam detection.

Getting Started

1. Clone the repository.

git clone https://github.com/omarnahdi/Spam-Detection-Model.git

2. Set up your environment.

cd Spam-Detection-Model
virtualenv venv
source venv/bin/activate

Feedback and Contributions

We appreciate your feedback and contributions to enhance the Spam Detection with Naive Bayes Algorithm project. Feel free to submit issues, pull requests, or reach out to us with your ideas.