📧 Ham-Spam Detection Model.

Welcome to the Ham-Spam Detection Model! This project implements a spam detection system using the Naive Bayes algorithm to classify messages as either 'ham' (not spam) or 'spam' (unwanted). It provides an efficient and accurate method for filtering out unwanted messages from your inbox.

🔍 Overview

The Ham-Spam Detection Model leverages the Naive Bayes algorithm, a probabilistic classifier, to distinguish between ham and spam messages. The model is trained on a dataset of labeled messages and can be used to classify new messages with high accuracy.

🚀 Features

• Spam Classification: Identifies whether an email is spam or ham.
• Naive Bayes Algorithm: Uses a probabilistic approach for classification.
• Efficient Filtering: Helps manage and clean your message inbox by filtering out spam.

📊 How It Works

1. Data Preprocessing:

   • Cleans and prepares the message data for training.

2. Model Training:

   • Trains a Naive Bayes classifier on the preprocessed message dataset.

3. Prediction:

   • Classifies new messages as spam or ham based on the trained model.

🛠️ How to Run

1. Clone the Repository:

   git clone https://github.com/Jimil1407/spam_detection.git
   cd spam_detection
   

2. Run the notebook:
   • you can use your own data or the data attached in the notebook.

📝 Requirements

• Python: Programming language used for implementation.
• scikit-learn: For machine learning functionalities.
• pandas: For data manipulation and analysis.
• numpy: For numerical operations.

🤝 Contributing

Contributions are welcome! If you have suggestions for improvements or additional features, please open an issue or submit a pull request.

🌟 Acknowledgements

• Thanks to the scikit-learn community for their powerful machine learning tools.
• Special thanks to the open-source community for their support and resources.

Thank you for exploring the Ham-Spam Detection Model! We hope it helps you manage your email effectively and efficiently.