# pratham-mishra225/classical-ml-notes architecture

> Architecture diagram of the GitHub repository pratham-mishra225/classical-ml-notes, 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/pratham-mishra225/classical-ml-notes
- Repository: https://github.com/pratham-mishra225/classical-ml-notes

## Overview

This repository is a set of machine-learning study materials, not an application with a shared runtime pipeline. Its principal learner workflow is to study a topic’s theory and work through its implementation notebook. The tree supports five topics: linear and logistic regression, decision trees, random forests, and K-nearest neighbors. Notebook contents were not sampled, and the README is empty, so implementation details, data flow, and connections to specific libraries cannot be established.

Main entry points: [`01_linear_regression/theory.md`](https://github.com/pratham-mishra225/classical-ml-notes/blob/main/01_linear_regression/theory.md) (Linear Theory), [`01_linear_regression/implementation.ipynb`](https://github.com/pratham-mishra225/classical-ml-notes/blob/main/01_linear_regression/implementation.ipynb) (Linear Notebook), [`02_Logistic_regression/theory.md`](https://github.com/pratham-mishra225/classical-ml-notes/blob/main/02_Logistic_regression/theory.md) (Logistic Theory), [`02_Logistic_regression/implementation.ipynb`](https://github.com/pratham-mishra225/classical-ml-notes/blob/main/02_Logistic_regression/implementation.ipynb) (Logistic Notebook)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_regression["Regression"]
  node_linear_theory["Linear Theory<br/>[theory.md]"]
  node_linear_notebook["Linear Notebook"]
  node_logistic_theory["Logistic Theory<br/>[theory.md]"]
  node_logistic_notebook["Logistic Notebook"]
end

subgraph group_tree_models["Tree Models"]
  node_decision_theory["Decision Tree Theory<br/>[theory.md]"]
  node_decision_notebook["Decision Tree Notebook"]
  node_forest_theory["Random Forest Theory<br/>[theory.md]"]
  node_forest_notebook["Random Forest Notebook"]
end

subgraph group_neighbors["Neighbor Methods"]
  node_knn_theory["KNN Theory<br/>[theory.md]"]
end

node_learner(("Learner"))

node_learner -->|"studies"| node_linear_theory
node_learner -->|"works through"| node_linear_notebook
node_learner -->|"studies"| node_logistic_theory
node_learner -->|"works through"| node_logistic_notebook
node_learner -->|"studies"| node_decision_theory
node_learner -->|"works through"| node_decision_notebook
node_learner -->|"studies"| node_forest_theory
node_learner -->|"works through"| node_forest_notebook
node_learner -->|"studies"| node_knn_theory

click node_linear_theory "https://github.com/pratham-mishra225/classical-ml-notes/blob/main/01_linear_regression/theory.md"
click node_linear_notebook "https://github.com/pratham-mishra225/classical-ml-notes/blob/main/01_linear_regression/implementation.ipynb"
click node_logistic_theory "https://github.com/pratham-mishra225/classical-ml-notes/blob/main/02_Logistic_regression/theory.md"
click node_logistic_notebook "https://github.com/pratham-mishra225/classical-ml-notes/blob/main/02_Logistic_regression/implementation.ipynb"
click node_decision_theory "https://github.com/pratham-mishra225/classical-ml-notes/blob/main/03_Decision_trees/theory.md"
click node_decision_notebook "https://github.com/pratham-mishra225/classical-ml-notes/blob/main/03_Decision_trees/implementation.ipynb"
click node_forest_theory "https://github.com/pratham-mishra225/classical-ml-notes/blob/main/04_Random_forest/theory.md"
click node_forest_notebook "https://github.com/pratham-mishra225/classical-ml-notes/blob/main/04_Random_forest/implementation.ipynb"
click node_knn_theory "https://github.com/pratham-mishra225/classical-ml-notes/blob/main/05_KNN/theory.md"

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_linear_theory,node_linear_notebook,node_logistic_theory,node_logistic_notebook toneBlue
class node_decision_theory,node_decision_notebook,node_forest_theory,node_forest_notebook toneAmber
class node_knn_theory toneMint
class node_learner toneIndigo
```

## Components

### Regression

- [Linear Theory](https://github.com/pratham-mishra225/classical-ml-notes/blob/main/01_linear_regression/theory.md): `01_linear_regression/theory.md`
- [Linear Notebook](https://github.com/pratham-mishra225/classical-ml-notes/blob/main/01_linear_regression/implementation.ipynb): `01_linear_regression/implementation.ipynb`
- [Logistic Theory](https://github.com/pratham-mishra225/classical-ml-notes/blob/main/02_Logistic_regression/theory.md): `02_Logistic_regression/theory.md`
- [Logistic Notebook](https://github.com/pratham-mishra225/classical-ml-notes/blob/main/02_Logistic_regression/implementation.ipynb): `02_Logistic_regression/implementation.ipynb`

### Tree Models

- [Decision Tree Theory](https://github.com/pratham-mishra225/classical-ml-notes/blob/main/03_Decision_trees/theory.md): `03_Decision_trees/theory.md`
- [Decision Tree Notebook](https://github.com/pratham-mishra225/classical-ml-notes/blob/main/03_Decision_trees/implementation.ipynb): `03_Decision_trees/implementation.ipynb`
- [Random Forest Theory](https://github.com/pratham-mishra225/classical-ml-notes/blob/main/04_Random_forest/theory.md): `04_Random_forest/theory.md`
- [Random Forest Notebook](https://github.com/pratham-mishra225/classical-ml-notes/blob/main/04_Random_forest/implementation.ipynb): `04_Random_forest/implementation.ipynb`

### Neighbor Methods

- [KNN Theory](https://github.com/pratham-mishra225/classical-ml-notes/blob/main/05_KNN/theory.md): `05_KNN/theory.md`

### Other components

- **Learner**

## Connections

- Learner → Linear Theory: studies
- Learner → Linear Notebook: works through
- Learner → Logistic Theory: studies
- Learner → Logistic Notebook: works through
- Learner → Decision Tree Theory: studies
- Learner → Decision Tree Notebook: works through
- Learner → Random Forest Theory: studies
- Learner → Random Forest Notebook: works through
- Learner → KNN Theory: studies