# sifat192/cancer-prediction architecture

> Architecture diagram of the GitHub repository sifat192/cancer-prediction, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-27). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/sifat192/cancer-prediction
- Repository: https://github.com/sifat192/cancer-prediction

## Overview

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`](https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb) (Notebook Workflow), [`cancer_prediction_dataset.zip`](https://github.com/sifat192/cancer-prediction/blob/main/cancer_prediction_dataset.zip) (Patient Dataset)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_preparation["Data Preparation"]
  node_notebook["Notebook Workflow"]
  node_preprocessing["Preprocessing"]
  node_selection["Feature Selection"]
  node_split["Train-Test Split"]
  node_scaling["Feature Scaling"]
end

subgraph group_modeling["Modeling & Evaluation"]
  node_classifier["Logistic Regression"]
  node_evaluation["Model Evaluation"]
  node_interpretation["Model Interpretation"]
  node_outputs["Metrics & Visualizations"]
end

node_user(("Analyst"))
node_dataset["Patient Dataset"]

node_user -->|"runs"| node_notebook
node_notebook -->|"loads"| node_dataset
node_notebook -->|"prepares data"| node_preprocessing
node_preprocessing -->|"selects features"| node_selection
node_selection -->|"splits data"| node_split
node_split -->|"scales features"| node_scaling
node_scaling -->|"trains"| node_classifier
node_classifier -->|"evaluates"| node_evaluation
node_classifier -->|"provides coefficients"| node_interpretation
node_evaluation -->|"produces metrics"| node_outputs
node_interpretation -->|"produces plots"| node_outputs

click node_dataset "https://github.com/sifat192/cancer-prediction/blob/main/cancer_prediction_dataset.zip"
click node_notebook "https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb"
click node_preprocessing "https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb"
click node_selection "https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb"
click node_split "https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb"
click node_scaling "https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb"
click node_classifier "https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb"
click node_evaluation "https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb"
click node_interpretation "https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb"
click node_outputs "https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_notebook,node_preprocessing,node_selection,node_split,node_scaling toneBlue
class node_classifier,node_evaluation,node_interpretation,node_outputs toneAmber
class node_user toneIndigo
class node_dataset toneTeal
```

## Components

### Data Preparation

- [Notebook Workflow](https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb): `01_logistic_regression.ipynb`
- [Preprocessing](https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb): `01_logistic_regression.ipynb`
- [Feature Selection](https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb): `01_logistic_regression.ipynb`
- [Train-Test Split](https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb): `01_logistic_regression.ipynb`
- [Feature Scaling](https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb): `01_logistic_regression.ipynb`

### Modeling & Evaluation

- [Logistic Regression](https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb): `01_logistic_regression.ipynb`
- [Model Evaluation](https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb): `01_logistic_regression.ipynb`
- [Model Interpretation](https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb): `01_logistic_regression.ipynb`
- [Metrics & Visualizations](https://github.com/sifat192/cancer-prediction/blob/main/01_logistic_regression.ipynb): `01_logistic_regression.ipynb`

### Other components

- **Analyst**
- [Patient Dataset](https://github.com/sifat192/cancer-prediction/blob/main/cancer_prediction_dataset.zip): `cancer_prediction_dataset.zip`

## Connections

- 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