Here's how RASBT.GITHUB.IO makes money* and how much!

*Please read our disclaimer before using our estimates.
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RASBT . GITHUB . IO {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Rasbt.github.io Make Money
  6. Keywords
  7. Topics
  8. Questions
  9. External Links
  10. Analytics And Tracking
  11. Libraries
  12. CDN Services

We are analyzing https://rasbt.github.io/mlxtend/.

Title:
mlxtend
Description:
A library consisting of useful tools and extensions for the day-to-day data science tasks.
Website Age:
12 years and 4 months (reg. 2013-03-08).

Matching Content Categories {📚}

  • Education
  • Technology & Computing
  • Video & Online Content

Content Management System {📝}

What CMS is rasbt.github.io built with?

Custom-built

No common CMS systems were detected on Rasbt.github.io, but we identified it was custom coded using Bootstrap (CSS).

Traffic Estimate {📈}

What is the average monthly size of rasbt.github.io audience?

🚄 Respectable Traffic: 10k - 20k visitors per month


Based on our best estimate, this website will receive around 10,361 visitors per month in the current month.

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How Does Rasbt.github.io Make Money? {💸}

We can't see how the site brings in money.

Not all websites focus on profit; some are designed to educate, connect people, or share useful tools. People create websites for numerous reasons. And this could be one such example. Rasbt.github.io might be cashing in, but we can't detect the method they're using.

Keywords {🔍}

import, clf, mlxtend, license, documentation, data, source, journal, open, github, links, examples, contact, machine, learning, extensions, science, repository, questions, discussions, irisdata, eclf, fig, labels, grd, scientific, doi, sebastian, raschka, bsd, addition, creative, channel, write, email, google, groups, issue, home, user, guide, api, installation, search, previous, edit, mlxtends, python, library, tools,

Topics {✒️}

linear_model import logisticregression org/pypi/mlxtend questions open source software} np import matplotlib bsd license mentioned svm import svc classifier import ensemblevoteclassifier 'rbf kernel svm' providing machine learning artistic creative works computer-generated graphics file license-cc scientific computing stack} public communication channel data import iris_data gridspec import itertools ensemble import randomforestclassifier plt import matplotlib machine learning extensions /rasbt/mlxtend pypi fig = plot_decision_regions links documentation google groups live discussions mlxtend repository clfs=[clf1 org/papers/10 license-bsd3 0 international license gs[grd[0] matplotlib fall scientific publication gitter channel open journal} mlxtend recently search previous probability=true eclf = ensemblevoteclassifier voting='soft' 'random forest' commercially usable plots produced feel free feature requests bug reports clf2 = randomforestclassifier clf3 = svc fig = plt ax = plt issue tracker

Questions {❓}

  • Questions?

Analytics and Tracking {📊}

  • Google Analytics
  • Google Universal Analytics

Libraries {📚}

  • Bootstrap
  • FontAwesome
  • jQuery

CDN Services {📦}

  • Cloudflare

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