How sukstudent/ibm_csrbox works
This repository provides a Streamlit application for entering space-mission details and predicting an outcome with a saved Random Forest model. The README also describes dataset preprocessing, feature engineering, model comparison, and evaluation, but the corresponding training source and dataset are not present in the supplied tree, so their wiring cannot be established. The prediction implementation is excerpted only partially.
Main entry points: app.py Streamlit application · space_mission_model.pkl Random Forest model
Components 7
User interface
- Streamlit application
app.py - Mission details form
app.py - Prediction result
app.py
Prediction
- Outcome inference
app.py - Random Forest model
space_mission_model.pkl
Model development
- Preprocess, train, evaluate
Other components
- Mission user
Connections 7
- Mission user → Streamlit application: uses
- Streamlit application → Mission details form: renders
- Mission details form → Outcome inference: submits details
- Streamlit application → Random Forest model: loads
- Outcome inference → Random Forest model: predicts with
- Outcome inference → Prediction result: returns outcome
- Prediction result → Mission user: displays