# WillKoehrsen/machine-learning-project-walkthrough

An implementation of a complete machine learning solution in Python on a real-world dataset. This project is meant to demonstrate how all the steps of a machine learning pipeline come together to solve a problem!

Repository: https://github.com/WillKoehrsen/machine-learning-project-walkthrough
Canonical: https://ross.abutalabs.com/products/machine-learning-project-walkthrough
Language: Jupyter Notebook
License Family: other
Last push: 2023-04-29T08:15:42+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": 3035, "days_push": 1222, "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 1301, forks 567 (observed 2026-08-28T04:04:17.604395+00:00)

## What it is
A Jupyter Notebook-based walkthrough demonstrating a complete end-to-end machine learning solution on a real-world dataset. It shows how all steps of an ML pipeline—data cleaning, feature engineering, modeling, and evaluation—come together in Python.

## Use cases
- learn machine learning end to end with a real dataset
- example of a complete ml pipeline in python
- walkthrough of feature engineering and model building
- jupyter notebook tutorial for machine learning workflow
- see how ml project steps fit together
- beginner machine learning project example

## When to choose
- you want a guided, practical example of a full ML workflow in Python
- you learn best from annotated notebooks on real data
- you need a reference for structuring your first ML project

## When to avoid
- you need production-ready, maintained ML code or a library
- you want deep-learning or large-scale distributed training examples
- you require an actively updated project with a license

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, etl, data-visualization
- domain: machine-learning, data-science, tutorials, education
- platform: python
- tags: jupyter-notebook, tutorial, walkthrough, end-to-end-pipeline, scikit-learn, feature-engineering

## Member repositories
- WillKoehrsen/machine-learning-project-walkthrough (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:17.604395+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 2026-08-30T04:52:13.918872+00:00, confidence not recorded.
  - readme: https://github.com/WillKoehrsen/machine-learning-project-walkthrough (fetched 2026-08-28T04:04:17.604395+00:00, sha ddc925278263)
- Data as of 2026-08-30T08:39:29.467469+00:00.