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https://github.com/dissorial/prx21_erikz
Analysis of self-tracked data: interactive visualizations & predictive algorithms
https://github.com/dissorial/prx21_erikz
analytics data-analysis data-science data-visualization machine-learning matplotlib pandas python python3 visualization
Last synced: 29 days ago
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Analysis of self-tracked data: interactive visualizations & predictive algorithms
- Host: GitHub
- URL: https://github.com/dissorial/prx21_erikz
- Owner: dissorial
- License: mit
- Created: 2021-10-26T06:58:04.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-12-10T00:48:51.000Z (about 2 years ago)
- Last Synced: 2024-12-13T19:49:09.391Z (about 2 months ago)
- Topics: analytics, data-analysis, data-science, data-visualization, machine-learning, matplotlib, pandas, python, python3, visualization
- Language: Python
- Homepage:
- Size: 2.48 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://share.streamlit.io/dissorial/prx21_erikz/app.pyy)
_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)._
# PRX21: QUANTIFIED SELF
> "The quantified self (QS) is any individual engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information."
>
> _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)_
If 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 – 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.
### TIME
The 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.
### QUANTIFIED SELF (QS)
While `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.
---
With 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**.
### Interactive visualizations
`Line charts | Ridgeline plots | Heatmaps | Scatterplots | K-means clustering`
### Predictive algorithms (also interactive)
`Decision tree classifier | CN2 rule induction | Support vector machine | Multiple linear regression | K-nearest neighbors`
---
## Acknowledgements
[Prakhar Rathi](https://github.com/prakharrathi25) for the multi-page setup in Streamlit
[Avik Jain](https://github.com/Avik-Jain) for model and algorithm infographics
[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