# mabdullah10/100-days-of-ml architecture

> Architecture diagram of the GitHub repository mabdullah10/100-days-of-ml, 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/mabdullah10/100-days-of-ml
- Repository: https://github.com/mabdullah10/100-days-of-ml

## Overview

This repository is a notebook-based data science learning curriculum, not a single runtime application. A learner studies foundations, data acquisition and analysis, preprocessing, then modeling-related material. The tree identifies these topics, but there is no README and only the online-learning script was sampled; connections between individual lessons and their datasets or saved artifacts are therefore not asserted.

Main entry points: [`001-python-basics.ipynb`](https://github.com/mabdullah10/100-days-of-ml/blob/main/001-python-basics.ipynb) (Python Basics), [`002-Indexing--data-structures.ipynb`](https://github.com/mabdullah10/100-days-of-ml/blob/main/002-Indexing--data-structures.ipynb) (Data Structures), [`003 numpy-arrays(1d, 2d, 3d).ipynb`](https://github.com/mabdullah10/100-days-of-ml/blob/main/003%20numpy-arrays(1d%2C%202d%2C%203d).ipynb) (NumPy Arrays), [`004-pandas.ipynb`](https://github.com/mabdullah10/100-days-of-ml/blob/main/004-pandas.ipynb) (Pandas)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_foundations["Foundations"]
  node_basics["Python Basics"]
  node_structures["Data Structures"]
  node_numpy["NumPy Arrays"]
  node_pandas["Pandas<br/>[004-pandas.ipynb]"]
end

subgraph group_data["Data Work"]
  node_data_import["Data Import"]
  node_api_scraping["API and Scraping"]
  node_understanding["Data Understanding"]
  node_eda["Exploratory Analysis"]
  node_multivariate["Multivariate Analysis"]
end

subgraph group_preprocessing["Preprocessing"]
  node_scaling["Feature Scaling"]
  node_encoding["Categorical Encoding"]
  node_pipelines["Pipelines"]
  node_missing["Missing Data"]
  node_outliers["Outlier Handling"]
end

subgraph group_modeling["Modeling"]
  node_online_ml["Online Learning<br/>[005-online-ml.py]"]
  node_toy_project["Toy Project"]
end

node_learner(("Learner"))

node_learner -->|"studies"| node_basics
node_learner -->|"studies"| node_structures
node_learner -->|"studies"| node_numpy
node_learner -->|"studies"| node_pandas
node_learner -->|"studies"| node_data_import
node_learner -->|"studies"| node_api_scraping
node_learner -->|"studies"| node_understanding
node_learner -->|"studies"| node_eda
node_learner -->|"studies"| node_multivariate
node_learner -->|"studies"| node_scaling
node_learner -->|"studies"| node_encoding
node_learner -->|"studies"| node_pipelines
node_learner -->|"studies"| node_missing
node_learner -->|"studies"| node_outliers
node_learner -->|"studies"| node_online_ml
node_learner -->|"studies"| node_toy_project

click node_basics "https://github.com/mabdullah10/100-days-of-ml/blob/main/001-python-basics.ipynb"
click node_structures "https://github.com/mabdullah10/100-days-of-ml/blob/main/002-Indexing--data-structures.ipynb"
click node_numpy "https://github.com/mabdullah10/100-days-of-ml/blob/main/003%20numpy-arrays(1d%2C%202d%2C%203d).ipynb"
click node_pandas "https://github.com/mabdullah10/100-days-of-ml/blob/main/004-pandas.ipynb"
click node_data_import "https://github.com/mabdullah10/100-days-of-ml/blob/main/015-working-with-csv.ipynb"
click node_api_scraping "https://github.com/mabdullah10/100-days-of-ml/blob/main/017-fetching-data-from-api.ipynb"
click node_understanding "https://github.com/mabdullah10/100-days-of-ml/blob/main/019-understanding-your-data.ipynb"
click node_eda "https://github.com/mabdullah10/100-days-of-ml/blob/main/020-eda-using-univariate-analysis.ipynb"
click node_multivariate "https://github.com/mabdullah10/100-days-of-ml/blob/main/021-eda-using-multivariate-analysis.ipynb"
click node_scaling "https://github.com/mabdullah10/100-days-of-ml/blob/main/024-feature-scaling-standardization.ipynb"
click node_encoding "https://github.com/mabdullah10/100-days-of-ml/blob/main/027-categorical-data--one-hot-encoding.ipynb"
click node_pipelines "https://github.com/mabdullah10/100-days-of-ml/blob/main/029.1-pipelines.ipynb"
click node_missing "https://github.com/mabdullah10/100-days-of-ml/blob/main/039-multivariate-imputation--knn-imputer.ipynb"
click node_outliers "https://github.com/mabdullah10/100-days-of-ml/blob/main/043-outlier--iqr-method.ipynb"
click node_online_ml "https://github.com/mabdullah10/100-days-of-ml/blob/main/005-online-ml.py"
click node_toy_project "https://github.com/mabdullah10/100-days-of-ml/blob/main/013-toy-project.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_basics,node_structures,node_numpy,node_pandas toneBlue
class node_data_import,node_api_scraping,node_understanding,node_eda,node_multivariate toneAmber
class node_scaling,node_encoding,node_pipelines,node_missing,node_outliers toneMint
class node_online_ml,node_toy_project toneRose
class node_learner toneIndigo
```

## Components

### Foundations

- [Python Basics](https://github.com/mabdullah10/100-days-of-ml/blob/main/001-python-basics.ipynb): `001-python-basics.ipynb`
- [Data Structures](https://github.com/mabdullah10/100-days-of-ml/blob/main/002-Indexing--data-structures.ipynb): `002-Indexing--data-structures.ipynb`
- [NumPy Arrays](https://github.com/mabdullah10/100-days-of-ml/blob/main/003%20numpy-arrays(1d%2C%202d%2C%203d).ipynb): `003 numpy-arrays(1d, 2d, 3d).ipynb`
- [Pandas](https://github.com/mabdullah10/100-days-of-ml/blob/main/004-pandas.ipynb): `004-pandas.ipynb`

### Data Work

- [Data Import](https://github.com/mabdullah10/100-days-of-ml/blob/main/015-working-with-csv.ipynb): `015-working-with-csv.ipynb`
- [API and Scraping](https://github.com/mabdullah10/100-days-of-ml/blob/main/017-fetching-data-from-api.ipynb): `017-fetching-data-from-api.ipynb`
- [Data Understanding](https://github.com/mabdullah10/100-days-of-ml/blob/main/019-understanding-your-data.ipynb): `019-understanding-your-data.ipynb`
- [Exploratory Analysis](https://github.com/mabdullah10/100-days-of-ml/blob/main/020-eda-using-univariate-analysis.ipynb): `020-eda-using-univariate-analysis.ipynb`
- [Multivariate Analysis](https://github.com/mabdullah10/100-days-of-ml/blob/main/021-eda-using-multivariate-analysis.ipynb): `021-eda-using-multivariate-analysis.ipynb`

### Preprocessing

- [Feature Scaling](https://github.com/mabdullah10/100-days-of-ml/blob/main/024-feature-scaling-standardization.ipynb): `024-feature-scaling-standardization.ipynb`
- [Categorical Encoding](https://github.com/mabdullah10/100-days-of-ml/blob/main/027-categorical-data--one-hot-encoding.ipynb): `027-categorical-data--one-hot-encoding.ipynb`
- [Pipelines](https://github.com/mabdullah10/100-days-of-ml/blob/main/029.1-pipelines.ipynb): `029.1-pipelines.ipynb`
- [Missing Data](https://github.com/mabdullah10/100-days-of-ml/blob/main/039-multivariate-imputation--knn-imputer.ipynb): `039-multivariate-imputation--knn-imputer.ipynb`
- [Outlier Handling](https://github.com/mabdullah10/100-days-of-ml/blob/main/043-outlier--iqr-method.ipynb): `043-outlier--iqr-method.ipynb`

### Modeling

- [Online Learning](https://github.com/mabdullah10/100-days-of-ml/blob/main/005-online-ml.py): `005-online-ml.py`
- [Toy Project](https://github.com/mabdullah10/100-days-of-ml/blob/main/013-toy-project.ipynb): `013-toy-project.ipynb`

### Other components

- **Learner**

## Connections

- Learner → Python Basics: studies
- Learner → Data Structures: studies
- Learner → NumPy Arrays: studies
- Learner → Pandas: studies
- Learner → Data Import: studies
- Learner → API and Scraping: studies
- Learner → Data Understanding: studies
- Learner → Exploratory Analysis: studies
- Learner → Multivariate Analysis: studies
- Learner → Feature Scaling: studies
- Learner → Categorical Encoding: studies
- Learner → Pipelines: studies
- Learner → Missing Data: studies
- Learner → Outlier Handling: studies
- Learner → Online Learning: studies
- Learner → Toy Project: studies