shaiknawazshareef/breast-cancer-classification-using-machine-learning

How shaiknawazshareef/breast-cancer-classification-using-machine-learning works

This Colab-style script uploads and reads the breast-cancer dataset, preprocesses its categorical data, splits features and labels, trains seven classifier configurations, and prints training/testing accuracy and test-set metrics. The README also describes comparing model performance. The graph groups preprocessing and modeling stages while keeping data loading and results visible. All implementation paths point to the single script; relationships are supported by its excerpt or explicit README workflow statements. The repository has no separate application layers or services beyond the Colab upload facility.

Main entry points: breast_cancer.py Feature split · breast-cancer .data Breast cancer data

Components 15

Data preparation

Breast cancer data breast-cancer .data
CSV loading breast_cancer.py
Missing-value imputation breast_cancer.py
Label encoding breast_cancer.py
Feature split breast_cancer.py

Model training

L1 perceptron breast_cancer.py
Logistic regression breast_cancer.py
Decision trees breast_cancer.py
Polynomial SVM breast_cancer.py
3-neighbor KNN breast_cancer.py
Gaussian naive Bayes breast_cancer.py

Evaluation

Metrics evaluation breast_cancer.py
Model comparison breast_cancer.py

Other components

Notebook user
Colab upload

Connections 20

Notebook user → Colab upload: uploads dataset README.md
Notebook user → CSV loading: runs workflow README.md
CSV loading → Colab upload: requests upload breast_cancer.py
CSV loading → Breast cancer data: reads CSV breast_cancer.py
CSV loading → Missing-value imputation: checks and imputes breast_cancer.py
Missing-value imputation → Label encoding: encodes categories breast_cancer.py
Label encoding → Feature split: separates and splits breast_cancer.py
Feature split → L1 perceptron: fits model breast_cancer.py
Feature split → Logistic regression: fits model breast_cancer.py
Feature split → Decision trees: fits models breast_cancer.py
Feature split → Polynomial SVM: fits model breast_cancer.py
Feature split → 3-neighbor KNN: fits model breast_cancer.py
Feature split → Gaussian naive Bayes: fits model breast_cancer.py
L1 perceptron → Metrics evaluation: predicts for scoring breast_cancer.py
Logistic regression → Metrics evaluation: predicts for scoring breast_cancer.py
Decision trees → Metrics evaluation: predicts for scoring breast_cancer.py
Polynomial SVM → Metrics evaluation: predicts for scoring breast_cancer.py
3-neighbor KNN → Metrics evaluation: predicts for scoring breast_cancer.py
Gaussian naive Bayes → Metrics evaluation: predicts for scoring breast_cancer.py
Metrics evaluation → Model comparison: prints results README.md

Drawn by GitDiagram on November 10, 2026 from the repository's file tree, README and source files. Read as Markdown ·