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

intermediate Published 3 Jun 2026

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

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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