SMS Spam Classifier
Neural network that labels SMS messages as spam or ham, with a Streamlit interface. Trained on 5,572 messages; the README reports 98.6% validation accuracy.
Status
Completed
Year
2026
Core Tech
Python, TensorFlow, Streamlit
01 / The Problem
SMS spam is intrusive and relies on consistent patterns that are tedious to filter manually.
02 / The Solution
A machine learning model trained on 5,572 messages to classify texts as spam or ham, achieving 98.6% validation accuracy, wrapped in an interactive Streamlit UI.
03 / How It Works
The core pipeline follows a strictly decoupled architecture:
04 / Engineering Decisions
Streamlit UI
Chose Streamlit to rapidly deploy the model into a usable web interface without building a complex frontend.
05 / Technology
- Python
- TensorFlow
- Streamlit
Ready to see it in action?