How sifat192/cancer-prediction works
This repository is a notebook-based cancer-risk classification project. Its documented workflow takes the packaged patient dataset through preprocessing, feature selection, splitting, scaling, and logistic-regression training, then evaluates and interprets the classifier. The README documents these stages and outputs, but no notebook source was available to verify implementation details or finer-grained call ownership; the connections below reflect only that documented workflow.
Main entry points: 01_logistic_regression.ipynb Notebook Workflow · cancer_prediction_dataset.zip Patient Dataset
Components 11
Data Preparation
- Notebook Workflow
01_logistic_regression.ipynb - Preprocessing
01_logistic_regression.ipynb - Feature Selection
01_logistic_regression.ipynb - Train-Test Split
01_logistic_regression.ipynb - Feature Scaling
01_logistic_regression.ipynb
Modeling & Evaluation
- Logistic Regression
01_logistic_regression.ipynb - Model Evaluation
01_logistic_regression.ipynb - Model Interpretation
01_logistic_regression.ipynb - Metrics & Visualizations
01_logistic_regression.ipynb
Other components
- Analyst
- Patient Dataset
cancer_prediction_dataset.zip
Connections 11
- Analyst → Notebook Workflow: runs
- Notebook Workflow → Patient Dataset: loads
- Notebook Workflow → Preprocessing: prepares data
- Preprocessing → Feature Selection: selects features
- Feature Selection → Train-Test Split: splits data
- Train-Test Split → Feature Scaling: scales features
- Feature Scaling → Logistic Regression: trains
- Logistic Regression → Model Evaluation: evaluates
- Logistic Regression → Model Interpretation: provides coefficients
- Model Evaluation → Metrics & Visualizations: produces metrics
- Model Interpretation → Metrics & Visualizations: produces plots