Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/muhammadshavaiz/spam_or_ham_emaildetection
The Spam Email Detection project uses Scikit-learn and Pandas to classify emails as spam or not, leveraging a dataset of 5,000 emails. It demonstrates practical statistical analysis and model training for effective email classification.
https://github.com/muhammadshavaiz/spam_or_ham_emaildetection
csv naive-bayes pandas python random-forest sklearn svm
Last synced: about 8 hours ago
JSON representation
The Spam Email Detection project uses Scikit-learn and Pandas to classify emails as spam or not, leveraging a dataset of 5,000 emails. It demonstrates practical statistical analysis and model training for effective email classification.
- Host: GitHub
- URL: https://github.com/muhammadshavaiz/spam_or_ham_emaildetection
- Owner: MuhammadShavaiz
- Created: 2024-08-19T18:10:49.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-08-19T20:10:27.000Z (about 1 month ago)
- Last Synced: 2024-09-23T06:32:27.326Z (4 days ago)
- Topics: csv, naive-bayes, pandas, python, random-forest, sklearn, svm
- Language: Jupyter Notebook
- Homepage:
- Size: 305 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Spam Email Detection
This repository contains a basic notebook demonstrating how to predict whether emails are spam or not using Scikit-learn and Pandas. The notebook leverages statistical analysis and model training to classify emails, making it a solid foundation for understanding email classification using machine learning.
## Project Overview
- **Data**: The project uses a publicly available email dataset containing labeled spam and non-spam emails.
- **Libraries**: Scikit-learn, Pandas
- **Key Steps**:
- Statistical analysis of the dataset.
- Model training using Scikit-learn's classification models.
- Evaluation of model performance.
## Getting Started
1. **Download the Notebook**:
- Click on the `.ipynb` file in this repository.
- Use the "Download" button on GitHub to save the notebook to your local machine.
2. **Open in Google Colab**:
- Visit [Google Colab](https://colab.research.google.com/).
- In the Colab interface, go to `File` > `Upload notebook`.
- Select the downloaded `.ipynb` file from your local machine to upload.
3. **Run the Notebook**:
- Once the notebook is uploaded, you can run the cells step-by-step to follow the statistical analysis and model training process.
- Feel free to modify the code and experiment with different models or techniques.
## Contributing
Contributions are welcome! If you have suggestions for improvements or new features, feel free to open an issue or submit a pull request.
## Contact
For any questions or feedback, feel free to reach out via [[email protected]](mailto:[email protected]) or through GitHub issues.