# arielf/weight-loss

Machine Learning meets  ketosis: how to effectively lose weight

Repository: https://github.com/arielf/weight-loss
Canonical: https://ross.abutalabs.com/products/weight-loss
Language: Python
License: NOASSERTION
License Family: other
Last push: 2022-01-18T06:52:30+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": 3707, "days_push": 1688, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3305, forks 143 (observed 2026-08-28T04:07:55.827564+00:00)

## What it is
A personal data-science project and write-up analyzing weight-loss and ketosis using Python and R code applied to the author's own weight data. It combines narrative explanation, CSV datasets, and plotting scripts to separate signal from noise in weight measurements.

## Use cases
- analyze my own weight loss data
- learn how to separate signal from noise in time-series data
- example of data science applied to personal health tracking
- visualize weight trends with R and ggplot2
- understand ketosis and weight loss through data

## When to choose
- you want a real-world example of personal data analysis with Python and R
- you're interested in quantified-self health experiments
- you want a narrative-driven learning resource on data analysis

## When to avoid
- you need production-ready or maintained software
- you want medically validated weight-loss advice
- you need a reusable library or tool

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, data-visualization, analytics
- domain: data-science, healthcare, education
- platform: python, cross-platform
- tags: weight-loss, ketosis, personal-data-analysis, ggplot2, signal-processing, self-quantification

## Member repositories
- arielf/weight-loss (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:55.827564+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 2026-08-29T18:42:39.166881+00:00, confidence not recorded.
  - readme: https://github.com/arielf/weight-loss (fetched 2026-08-28T04:07:55.827564+00:00, sha b5f708dc05c9)
- Data as of 2026-08-30T08:39:29.467469+00:00.