{"id":18010372,"url":"https://github.com/dissorial/prx21_erikz","last_synced_at":"2026-05-02T19:33:10.138Z","repository":{"id":143107259,"uuid":"421315019","full_name":"dissorial/prx21_erikz","owner":"dissorial","description":"Analysis of self-tracked data: interactive visualizations \u0026 predictive algorithms","archived":false,"fork":false,"pushed_at":"2022-12-10T00:48:51.000Z","size":2602,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-04T12:52:00.625Z","etag":null,"topics":["analytics","data-analysis","data-science","data-visualization","machine-learning","matplotlib","pandas","python","python3","visualization"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dissorial.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-10-26T06:58:04.000Z","updated_at":"2023-02-28T12:20:22.000Z","dependencies_parsed_at":null,"dependency_job_id":"63dbb346-5441-4494-994a-e316a2a08866","html_url":"https://github.com/dissorial/prx21_erikz","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dissorial/prx21_erikz","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dissorial%2Fprx21_erikz","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dissorial%2Fprx21_erikz/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dissorial%2Fprx21_erikz/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dissorial%2Fprx21_erikz/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dissorial","download_url":"https://codeload.github.com/dissorial/prx21_erikz/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dissorial%2Fprx21_erikz/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32547645,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-02T19:18:06.202Z","status":"ssl_error","status_checked_at":"2026-05-02T19:16:21.335Z","response_time":132,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analytics","data-analysis","data-science","data-visualization","machine-learning","matplotlib","pandas","python","python3","visualization"],"created_at":"2024-10-30T02:14:03.881Z","updated_at":"2026-05-02T19:33:10.119Z","avatar_url":"https://github.com/dissorial.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\n\n[![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io/dissorial/prx21_erikz/app.pyy)\n\n_This is a TL;DR version. If you'd like to read more about anything mentioned below, head over to the [web application itself](https://share.streamlit.io/dissorial/prx21_erikz/app.py)._\n\n# PRX21: QUANTIFIED SELF\n\n\u003ca href='https://www.linkedin.com/in/erik-z%C3%A1vodsk%C3%BD-126a82144/'\u003e![LinkedIn](https://img.shields.io/badge/Erik%20Z%C3%A1vodsk%C3%BD-blue?style=for-the-badge\u0026logo=linkedin\u0026labelColor=blue)\u003c/a\u003e\n\n![sklearn](https://img.shields.io/badge/sklearn-blueviolet?style=flat-square)\n![altair](https://img.shields.io/badge/altair-blueviolet?style=flat-square)\n![joypy](https://img.shields.io/badge/joypy-blueviolet?style=flat-square)\n![pandas](https://img.shields.io/badge/pandas-blueviolet?style=flat-square)\n![numpy](https://img.shields.io/badge/numpy-blueviolet?style=flat-square)\n![matplotlib](https://img.shields.io/badge/altair-blueviolet?style=flat-square)\n![seaborn](https://img.shields.io/badge/seaborn-blueviolet?style=flat-square)\n![streamlit](https://img.shields.io/badge/streamlit-blueviolet?style=flat-square)\n\n![python](https://camo.githubusercontent.com/3cdf9577401a2c7dceac655bbd37fb2f3ee273a457bf1f2169c602fb80ca56f8/68747470733a2f2f666f7274686562616467652e636f6d2f696d616765732f6261646765732f6d6164652d776974682d707974686f6e2e737667)\n\n\u003e \"The quantified self (QS) is any individual engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information.\"\n\u003e\n\u003e _Definition borrowed from: [The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery](https://www.liebertpub.com/doi/10.1089/big.2012.0002)_\n\nIf you think the title of this scientific article is a little far-fetched, I agree. When I started this project at the beginning of 2021, I wouldn't have guessed that analysis of self-tracked data is actually quite common. My inspiration came from a Reddit post I stumbled upon a few years ago \u0026ndash; someone tracked their time for an entire year and analyzed the data to understand themselves better. It seemed like an interesting idea that requires a negligible time investment in exchange for a possibly significant discovery, so I went ahead with it.\n\n### TIME\n\nThe first major part of what I tracked is time. I divided days into 30-minute intervals and assigned each one of 15 pre-defined _time categories_, such as sleep, internet, fun, hobbies, and more.\n\n### QUANTIFIED SELF (QS)\n\nWhile `time` technically falls under `QS`, I treat the two individually because I came up with 45 variables to track in `QS`, and grouping them together with `time` would be messy. These variables are subjectively evaluated attributes of my day and include things like mood, restfulness, productivity, health problems, and others.\n\n---\n\nWith most of the year 2021 behind us, I thought now would be a good time to delve deeper into the data I've collected and hopefully uncover otherwise hidden patterns about how I function on a daily basis. The result is this web application, which is divided into two parts: **interactive visualizations** and **predictive algorithms**.\n\n### Interactive visualizations\n\n`Line charts | Ridgeline plots | Heatmaps | Scatterplots | K-means clustering`\n\n### Predictive algorithms (also interactive)\n\n`Decision tree classifier | CN2 rule induction | Support vector machine | Multiple linear regression | K-nearest neighbors`\n\n---\n\n## Acknowledgements\n\n[Prakhar Rathi](https://github.com/prakharrathi25) for the multi-page setup in Streamlit\n\n[Avik Jain](https://github.com/Avik-Jain) for model and algorithm infographics\n\n[This reddit post](https://www.reddit.com/r/dataisbeautiful/comments/bdf1ta/every_single_%C2%BD_hour_of_my_2018_recorded_oc/) for the initial inspiration to do this\n\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdissorial%2Fprx21_erikz","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdissorial%2Fprx21_erikz","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdissorial%2Fprx21_erikz/lists"}