https://github.com/rabinverse/email_spam_classifier
https://github.com/rabinverse/email_spam_classifier
classifier dvc fastapi mlflow
Last synced: about 18 hours ago
JSON representation
- Host: GitHub
- URL: https://github.com/rabinverse/email_spam_classifier
- Owner: rabinverse
- License: mit
- Created: 2025-09-05T19:27:51.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-09-11T19:58:12.000Z (10 months ago)
- Last Synced: 2025-09-11T21:32:51.650Z (10 months ago)
- Topics: classifier, dvc, fastapi, mlflow
- Language: Jupyter Notebook
- Homepage: https://emailspamclassifier-e8g68qf6nurxzusaigjev7.streamlit.app/
- Size: 4.8 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Email_spam_classifier
[visit](https://emailspamclassifier-e8g68qf6nurxzusaigjev7.streamlit.app/)
[experiments_mlflow](https://dagshub.com/rabinverse/email_spam_classifier.mlflow/)
[dagshub](https://dagshub.com/rabinverse/email_spam_classifier) <- contains models,dataset,experiments
# Ml algorithm that classifies emails as spam and not spam
## Project Organization
```
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├
│
└── src <- Source code for use in this project.
│ │
│ ├── train_predict.py <- Script to train and predict the model
│
│
│---streamlit
│ └── app.py <- Streamlit web app
```
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