{"id":24776859,"url":"https://github.com/docsallover/spam-detection","last_synced_at":"2026-04-09T09:38:54.505Z","repository":{"id":270551886,"uuid":"890400948","full_name":"docsallover/spam-detection","owner":"docsallover","description":"Building a Spam Filter with Python: Using Machine Learning to Combat Spam","archived":false,"fork":false,"pushed_at":"2025-01-01T08:29:36.000Z","size":320,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-29T07:38:44.277Z","etag":null,"topics":["datascience","flask","jinja2","machine-learning","numpy","numpy-library","pandas","pandas-python","python","python3","scikit-learn"],"latest_commit_sha":null,"homepage":"https://docsallover.com/blog/data-science/building-a-spam-filter-with-python-using-ml/","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/docsallover.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-18T14:02:12.000Z","updated_at":"2025-01-01T08:29:39.000Z","dependencies_parsed_at":"2025-01-01T09:33:19.018Z","dependency_job_id":null,"html_url":"https://github.com/docsallover/spam-detection","commit_stats":null,"previous_names":["docsallover/spam-detection"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/docsallover%2Fspam-detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/docsallover%2Fspam-detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/docsallover%2Fspam-detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/docsallover%2Fspam-detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/docsallover","download_url":"https://codeload.github.com/docsallover/spam-detection/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245191591,"owners_count":20575250,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["datascience","flask","jinja2","machine-learning","numpy","numpy-library","pandas","pandas-python","python","python3","scikit-learn"],"created_at":"2025-01-29T07:38:51.705Z","updated_at":"2025-12-30T23:28:35.138Z","avatar_url":"https://github.com/docsallover.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Building a Spam Filter with Python: Using ML to Combat Spam\n\nThis is a basic web application that uses a Naive Bayes Classifier to detect spam messages. It uses a pre-existing dataset of labeled messages, trains a model, and uses it to predict whether a given message is spam or not.\n\n## Technologies Used\n\n- Flask (web framework)\n- Scikit-Learn (machine learning library)\n- Pandas (data manipulation library)\n- NumPy (numerical computing library)\n- Jinja2 (template engine)\n\n## How to Run\n\n1. Clone the repository\n2. Set up a virtual environment by running `python -m venv env` (on Windows) or `python3 -m venv env` (on Linux and macOS)\n3. Activate the virtual environment by running `env\\Scripts\\activate` (on Windows) or `source env/bin/activate` (on Linux and macOS)\n4. Install the required packages by running `pip install -r requirements.txt`\n5. Run the application by running `python spam-classifier.py`\n6. Open a web browser and navigate to `http://localhost:5000`\n\n## How it Works\n\n1. The application reads a pre-existing dataset of labeled messages from a CSV file.\n2. It trains a Naive Bayes Classifier using the dataset.\n3. It uses the trained model to predict whether a given message is spam or not.\n4. The application displays the prediction result on the web page.\n\n## Features\n\n- Detects spam messages using a Naive Bayes Classifier\n- Displays the prediction result on the web page\n- Allows users to input a message and get a prediction\n\n## Limitations\n\n- The application is not perfect and may make mistakes\n- The application does not store any data and does not have any user authentication\n- The application is not optimized for performance\n\n\n## License\nThis project is licensed under the MIT License. See the LICENSE file for details.\n\n\n## Visit and Follow\nFor more details, tutorials, tools, snippets, and resources, visit the website: [DocsAllOver](https://docsallover.com/).\n\nFollow us on:\n- [Facebook](https://www.facebook.com/docsallover)\n- [Instagram](https://www.instagram.com/docsallover.tech/)\n- [x.com](https://www.x.com/docsallover/)\n- [LinkedIn](https://www.linkedin.com/company/docsallover/)\n- [YouTube](https://www.youtube.com/@docsallover)\n- [Threads.net](https://threads.net/docsallover.tech)\n\nand visit our website to know more about our tutorials, tools, snippets, and blogs.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdocsallover%2Fspam-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdocsallover%2Fspam-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdocsallover%2Fspam-detection/lists"}