Email Spam Classifier with Streamlit and Docker
📰 Dev.to AI
Build an email spam classifier using Machine Learning and deploy it with Streamlit and Docker
Action Steps
- Ingest and preprocess the email dataset using Python
- Train and compare Naive Bayes and fine-tuned RoBERTa models for spam classification
- Build an interactive visualization with Streamlit to display model performance
- Configure and deploy the model using Docker
- Test and evaluate the deployed model with sample email data
Who Needs to Know This
Data scientists and Machine Learning engineers can use this guide to build and deploy a spam classifier, while DevOps teams can utilize the Docker deployment instructions
Key Insight
💡 Fine-tuned RoBERTa models can outperform Naive Bayes for spam classification tasks
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Build and deploy an email spam classifier with Streamlit and Docker!
Key Takeaways
Build an email spam classifier using Machine Learning and deploy it with Streamlit and Docker
Full Article
This guide details an end-to-end Machine Learning pipeline for email spam classification, covering text preprocessing, comparative evaluations between Naive Bayes and fine-tuned RoBERTa models, interactive visualization with Streamlit, and deployment using Docker. Index Introduction and Overview Dataset Ingestion and Preprocessing Vocabulary Building and Filtering Feature Extraction and Engineering Model Training and Seriali
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