# Weight-loss as a personal machine learning experiment: arielf/weight-loss reviewed > Arielf/weight-loss is a personal repository that turns daily weigh-ins and a diary of foods and activities into a regression problem for Vowpal Wabbit. It is a data-analysis toolkit for one person's metabolic signals, not a general diet app. **arielf/weight-loss** — GitHub describes it as Machine Learning meets ketosis: how to effectively lose weight. The repository metadata lists Python as its primary language. The metadata lists the NOASSERTION license. This article stays within the project description and details documented in the GitHub repository README. - Repository: https://github.com/arielf/weight-loss - Stars: 3,302 · Forks: 143 - Language: Python - License: NOASSERTION - Published: 2026-08-13 · Updated: 2026-08-18 · Language: en - Canonical page: https://hysenlabs.com/projects/arielf-weight-loss ## What this repository actually is The core problem it addresses is the difficulty of separating signal from noise in a noisy personal record. The author found that simple willpower-based dieting failed repeatedly, so he decided to collect data and let a regression model rank the factors. The repository is a concrete answer to the question: can everyday self-tracking, combined with a general-purpose learning algorithm, produce actionable insights? The answer it offers is a qualified yes, with heavy caveats about noise and overfitting. ## The data format and the feature model The conversion script `lifestyle-csv2vw` transforms this CSV into Vowpal Wabbit regression format. The label is the change in weight (delta) over the past 24 hours, and the features are the tokens from the third column. The README notes that the author sorted lines descending by absolute delta before running multiple passes, to amplify weak signals. This is a preprocessing step that the Makefile presumably applies. The feature weights that come out of Vowpal Wabbit are then ranked by a relative score. The sample output shows `nosleep` with a positive weight, meaning it was associated with weight gain in the author's data, and `bacon` appears later with a negative weight, though the README warns the data is too noisy anyway. ## How the machine learning step works The R script `date-weight.r` generates a chart of weight over time. It requires R and ggplot2. This is a separate visualization step from the regression. The chart is meant to show the overall trend, not the feature weights. The README says the chart was generated from `weight.2015.csv`, so the data file is the source for both the plot and the regression input. The Makefile ties these together: running `make` in the repository directory should produce the chart and run the learning script, assuming all dependencies are installed. ## Getting it running: commands and prerequisites The README shows a sample `make` output with a table of feature weights. The output is trimmed, so the full output likely includes more features and a progress bar from Vowpal Wabbit. The Makefile may also sort the final weights by RelScore. The user's own data will produce different features and weights, which is the point. The workflow is: collect data for weeks or months, write it in the CSV format, run `make`, and inspect the ranked feature list. The author suggests that this can help identify foods or behaviors that correlate with weight gain or loss for that individual. ## Limitations and failure modes A further failure mode is that the model treats each day as independent, ignoring trends and autocorrelation. Weight changes are not independent events; a day of water retention can mask a fat loss, and the model has no way to account for that. The README does not mention any time-series handling. For a person who wants to lose weight, this tool can point to suspicious foods, but it cannot tell you whether the effect is real. The author's own experience is that the results helped him, but he frames it as a personal journey, not a general method. If you have a more complex diet, or if you travel and eat out often, the token vocabulary will become large and sparse, making the regression even noisier. ## Alternative approaches and what makes this one different The README also mentions the author's earlier attempts with Atkins-style carb reduction, which failed due to willpower. The machine learning approach is meant to be more sustainable because it provides feedback. That is a motivational difference, not a technical one. If you are not interested in running regression yourself, you could simply plot your weight and manually review your diary, which is what the R script does. The repository gives you both the plot and the regression, but the regression is the novel part. For someone who wants to avoid the complexity of Vowpal Wabbit, a simpler alternative is to calculate the average weight change on days when a given food appears versus days when it does not, using a small Python script. That would be more transparent and easier to validate, but it would not handle interactions between foods. The repository's approach is a trade-off between sophistication and interpretability. ## Maintenance, upgrade cost, and license implications The license is listed as NOASSERTION, which means the repository does not declare a standard open-source license. This is a significant point. Without a license, you have no explicit permission to copy, modify, or distribute the code. The README says 'I wrote a HOWTO file with more detailed instructions. Please open an issue, if anything doesn't work for you,' which implies the author intends for people to use it, but that is not a legal license. If you plan to use the code in your own project, you should contact the author or find a differently licensed alternative. For personal experimentation, you can read the code and learn from it, but you should not redistribute it without permission. The data file is personal health data, which raises privacy considerations if you were to share it. ## Conclusion Adopt this repository if you are a technically comfortable individual who already tracks daily weight and lifestyle factors in a structured text file, and you want a reproducible script that turns that log into a ranked list of feature weights. Do not adopt it if you expect a turnkey diet app, a validated scientific study, or a tool that handles missing data gracefully. Before running `make`, verify that you have R, ggplot2, and Vowpal Wabbit installed, and be prepared to interpret noisy output. The repository is a snapshot of one person's method; treat its results as hypotheses about your own body, not as medical advice. The concrete next step is to clone the repo, read HOWTO.md, and run `make` only after you have your own CSV in the documented three-column format. ## FAQ ### How does the weight-loss repository collect data? You weigh yourself once a day and record the weight plus what you did or ate in the previous twenty-four hours, including exercise and sleep. The Makefile states both requirements, and the file is a CSV with three columns: Date, MorningWeight and YesterdayFactors. ### What machine learning does weight-loss use? vowpal wabbit, in regression mode. A conversion script named lifestyle-csv2vw rewrites the CSV into its training-set format, with the change in weight over the past day as the label and the third column as the input features. ### What does the confidence range in weight-loss mean? Each item is reported with an interval alongside its weight. The worked example is a carrot at -0.024568 with a range from -0.071207 to 0.026108, which the Makefile explains as a small average loss with a range reaching into gain, and therefore low confidence. ### How do I install and run weight-loss? Install vowpal wabbit, clone https://github.com/arielf/weight-loss, place your own data in a CSV named after the output of the username file in the repository, and type make. A HOWTO.md in the tree carries longer instructions. ### Is the weight-loss project still being worked on? The repository has no releases, and the last push on the default branch is dated 2022-01-18. The recorded licence field is unresolved while the tree carries a file named Licence.md, and the two are not reconciled anywhere in the file. ## Sources - [Official README](https://github.com/arielf/weight-loss#readme) - [Project repository](https://github.com/arielf/weight-loss) - [README](https://github.com/arielf/weight-loss/blob/master/README.md) - [Releases](https://github.com/arielf/weight-loss/releases) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/arielf-weight-loss