Welcome to the Ham-Spam Detection Model! This project implements a spam detection system using the Naive Bayes algorithm to classify email messages as either 'ham' (not spam) or 'spam' (unwanted). It provides an efficient and accurate method for filtering out unwanted messages from your inbox.
The Ham-Spam Detection Model leverages the Naive Bayes algorithm, a probabilistic classifier, to distinguish between ham and spam emails. The model is trained on a dataset of labeled emails and can be used to classify new messages with high accuracy.
- 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 email inbox by filtering out spam.
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Data Preprocessing:
- Cleans and prepares the email data for training.
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Model Training:
- Trains a Naive Bayes classifier on the preprocessed email dataset.
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Prediction:
- Classifies new email messages as spam or ham based on the trained model.
- Clone the Repository:
git clone https://github.com/Jimil1407/ML_hackathon.git cd ML_hackathon - Run the notebook:
- it will generate a pickle file, use that in the streamlit app.
streamlit run host_website.py
- Python: Programming language used for implementation.
- scikit-learn: For machine learning functionalities.
- pandas: For data manipulation and analysis.
- numpy: For numerical operations.
- streamlit: For creating the web interface.
Contributions are welcome! If you have suggestions for improvements or additional features, please open an issue or submit a pull request.
- 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.