# cerlymarco/MEDIUM_NoteBook

Repository containing notebooks of my posts on Medium

Repository: https://github.com/cerlymarco/MEDIUM_NoteBook
Canonical: https://ross.abutalabs.com/products/medium_notebook
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: notebooks, machine-learning, deep-learning, artificial-intelligence, data-science
Last push: 2024-09-22T08:08:30+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2690, "days_push": 710, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2144, forks 964 (observed 2026-08-28T04:06:18.586532+00:00)

## What it is
A collection of Jupyter notebooks accompanying the author's Medium posts on machine learning and data science topics. It covers practical tutorials on time series forecasting, explainability, causal inference, and statistical methods with ML approaches.

## Use cases
- learn time series forecasting techniques with sklearn
- understand SHAP explainability with proxy models
- study causal inference with synthetic control and ML
- explore hypothesis testing with machine learning approaches
- learn PCA for multivariate time series forecasting
- find example notebooks for imbalanced data model selection

## When to choose
- you want hands-on notebook examples accompanying in-depth Medium tutorials
- you are learning applied ML topics like forecasting, explainability, and causal inference
- you want reusable code snippets for data science experiments

## When to avoid
- you need a production-ready library or package
- you want a single cohesive tool rather than independent tutorial notebooks
- you need guaranteed maintenance or support

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, data-science, nlp
- domain: machine-learning, data-science, tutorials, artificial-intelligence
- platform: python
- tags: jupyter-notebooks, medium-posts, time-series-forecasting, explainability, causal-inference, educational

## Member repositories
- cerlymarco/MEDIUM_NoteBook (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:18.586532+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:51:13.809963+00:00, confidence not recorded.
  - readme: https://github.com/cerlymarco/MEDIUM_NoteBook (fetched 2026-08-28T04:06:18.586532+00:00, sha a5dd0cfdbeb2)
- Data as of 2026-08-30T08:39:29.467469+00:00.