semvec/pypi-packages huggingface dataset:
sitechecks 0.3.0
A no-nonsense set of best-practices for public HTML marketing websites.
License: MIT
Requires Python: >=3.6,<4.0

# sitechecks

Sitechecks is a set of no-nonsense tests for best practices when it comes
to building and publishing public-facing websites. It does not care about
the latest and greatest technologies. It cares about doing the basics --
making your site usable, discoverable, and shareable.

The issues that sitechecks points out are all actionable and
important enough to actually deal with. We carefully decide what it does
and does not check for in an effort to keep it this way. There are
sometimes rare situations where you will not want to deal with an issue
(usually because you don't have the ability to actually fix it, due to
the way your site is built or how the issue came to be). When that happens,
you have the option to exclude that particular instance from future reports,
so that your open issue list remains actionable. **The goal is to have 0 open issues.**

Things that it checks for:
- Broken links
- Broken images
- Duplicate `id` attributes on the same page
- Delivering images that are far too big (usually by mistake)
- HTTP assets on HTTPS pages (mixed content)
- Not using HTTPS
- Not redirecting HTTP to HTTPS
- Page titles missing
- Page titles that are too long (SEO)
- Page titles that are too short (likely on accident)
- `favicon.ico` in place
- *Soon...*
- Custom string (regular expression) present (Google Analytics, etc.)
- Stray HTML tags
- Meta description tag (SEO, social)
- Open Graph basic tags present
- h1 missing?
- Spaces or other illegal characters in page url
- html lang attribute usage
- Basic spell-check?
- Valid robots.txt

What is it not for?
- Hyper-tuning performance
- Javascript-heavy sites
- Progressive web app tests
- Advanced server configuration issues

## How it works

- sitechecks.io does automated checks of your *live* website, factoring in any live CMS data, etc.
- check a single page for free (rate limited per domain)
- GitHub integration to keep settings in your repo, and share with sitechecks CLI
- free to use sitechecks CLI / GitHub Action
- api key gets you comparative (this issue was introduced here) and SEO changes (this changed here, you might want to review)
- in charting history, could use git ref meta tag to note version changes (deploys) and change in issues
algo-ml 0.0.1
Machine learning Package
License: MIT
Keywords: machine learning ml ai artificial intelligence k-means clustering nearest neighbor regression

# This is Algo-ML
This is a machine learning module/library. Feel free to use any of the code.

## How to use: 
### Regression
Call the train function on the model, giving the data as the first input and there is no other input. The input data looks like this: [[x, y], [x, y], ........].

### KMeansClustering
Call the train function giving the data as input which should look like this [[x, y, ....], [x, y, ....], [x, y, ....], .......].

### KNearestNeighbor
Give it data to train on (just stores the data), which takes the form [[x, y, ......, LABEL], [x, y, ......, LABEL], ...........]. Use the predict function then to predict a label
for a particular point.



### That's it! :)


Change log
__________

0.0.1 (9/20/2020)
- - - - - - - - - 
- First Release