How aleebiotec/breast_cancer_analysis works
This repository is a notebook-based breast-cancer classification analysis, not a deployed diagnostic service. An analyst supplies the Wisconsin diagnostic dataset and runs the notebook through cleaning, exploratory analysis, preparation, model comparison, and tuning to assess benign-versus-malignant classification, with particular attention to malignant recall. The README documents these stages and the comparison graphic; the notebook contents were not excerpted, so the graph’s workflow links rely on README evidence rather than inspected implementation details.
Main entry points: Breast_Cancer_Analysis.ipynb Evaluate models
Components 10
Data analysis
- Clean data
Breast_Cancer_Analysis.ipynb - Explore data
Breast_Cancer_Analysis.ipynb - Prepare features
Breast_Cancer_Analysis.ipynb
Modeling and evaluation
- Train models
Breast_Cancer_Analysis.ipynb - Evaluate models
Breast_Cancer_Analysis.ipynb - Tune models
Breast_Cancer_Analysis.ipynb
Analysis outputs
- Model findings
Breast_Cancer_Analysis.ipynb - Model comparison
Other components
- Analyst
- Wisconsin dataset
Connections 10
- Analyst → Clean data: runs notebook
README.md - Wisconsin dataset → Clean data: provides measurements
README.md - Clean data → Explore data: feeds analysis
README.md - Explore data → Prepare features: informs preparation
README.md - Prepare features → Train models: provides features
README.md - Train models → Evaluate models: produces predictions
README.md - Evaluate models → Tune models: identifies candidates
README.md - Evaluate models → Model findings: summarizes performance
README.md - Tune models → Model findings: updates performance
README.md - Evaluate models → Model comparison: visualizes accuracy
README.md