# kootenpv/whereami

Uses WiFi signals :signal_strength: and machine learning to predict where you are

Repository: https://github.com/kootenpv/whereami
Canonical: https://ross.abutalabs.com/products/whereami
Language: Python
License: AGPL-3.0
License Family: copyleft
Topics: whereami, wifi-signal, access-point, distance, cross-platform, indoor-positioning, hacktoberfest, hacktoberfest2021
Last push: 2023-11-30T10:27:09+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": 3636, "days_push": 1007, "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 5139, forks 251 (observed 2026-08-28T04:09:11.120117+00:00)

## What it is
A Python CLI tool that uses WiFi access point signal strengths and a scikit-learn RandomForest model to predict your indoor location. It learns labeled location samples (e.g., bedroom, kitchen) and can distinguish positions as close as 2-10 meters apart.

## Use cases
- predict which room of the house I'm in from wifi signals
- train a model to recognize my desk vs couch location
- get indoor location estimates for home automation
- check cross-validated accuracy of wifi-based location learning
- pipe predicted location into other scripts or voice output
- distinguish locations a few meters apart using access point signals

## When to choose
- you want a simple, cross-platform indoor positioning tool using existing wifi hardware
- you need location prediction usable from the command line or as a Python library
- you want to label a handful of fixed indoor spots and classify between them

## When to avoid
- you need GPS-quality or continuously tracked outdoor positioning
- you require real-time streaming location updates rather than on-demand predictions
- you need a mobile app or production-grade indoor positioning system

## Facets
- artifact type: cli-tool
- maturity: maintenance
- function: machine-learning, cli, data-science
- domain: machine-learning, developer-tools
- platform: windows, python, cli, cross-platform
- tags: wifi-signals, indoor-positioning, random-forest, sklearn, location-prediction, command-line, macos, linux

## Member repositories
- kootenpv/whereami (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:11.120117+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 2026-08-29T18:02:00.792580+00:00, confidence not recorded.
  - readme: https://github.com/kootenpv/whereami (fetched 2026-08-28T04:09:11.120117+00:00, sha dbd769a5a9ed)
  - registry_pypi: https://pypi.org/pypi/whereami/json (fetched 2026-08-29T08:56:23.015177+00:00, sha 18884db70755)
- Data as of 2026-08-30T08:39:29.467469+00:00.