# abhishekkrthakur/approachingalmost

Approaching (Almost) Any Machine Learning Problem

Repository: https://github.com/abhishekkrthakur/approachingalmost
Canonical: https://ross.abutalabs.com/products/approachingalmost
License Family: other
Last push: 2023-03-25T00:53:39+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": 2251, "days_push": 1258, "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 8373, forks 1122 (observed 2026-08-28T04:10:20.557555+00:00)

## What it is
The official repository for the book 'Approaching (Almost) Any Machine Learning Problem' by Abhishek Thakur, containing environment files and links to datasets. It is a code-along learning resource rather than a software library.

## Use cases
- learn practical machine learning
- how to approach a machine learning problem end to end
- find datasets used in the AAAMLP book
- set up a conda environment for the book's examples
- study feature engineering and cross-validation techniques

## When to choose
- you want a practical, hands-on introduction to applied machine learning
- you are preparing for Kaggle competitions or ML interviews
- you prefer learning by coding along with a book

## When to avoid
- you need production-ready ML code or a reusable library
- you want fully shared source code instead of a code-along book
- you need a maintained software tool with releases and support

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, data-science
- domain: machine-learning, data-science, tutorials
- platform: python, cross-platform
- tags: book, machine-learning, kaggle, datasets, education

## Member repositories
- abhishekkrthakur/approachingalmost (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:20.557555+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 2026-08-29T17:27:32.837479+00:00, confidence not recorded.
  - readme: https://github.com/abhishekkrthakur/approachingalmost (fetched 2026-08-28T04:10:20.557555+00:00, sha d6b00ed0ae8d)
- Data as of 2026-08-30T08:39:29.467469+00:00.