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Month-by-Month
Browser Statistics
Since 2002
Month-by-Month
Browser Statistics
Since 2002
The Most Popular Browsers
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W3Schools has over 60 million monthly visits.
From the statistics below (collected since 2002) you can read the long term trends of browser usage.
Click on the browser names to see detailed browser information:
| 2021 | Chrome | Edge/IE | Firefox | Safari | Opera |
|---|---|---|---|---|---|
| January | 80.3 % | 5.3 % | 6.7 % | 3.8 % | 2.3 % |
| 2020 | Chrome | Edge/IE | Firefox | Safari | Opera |
| December | 80.5 % | 5.2 % | 6.7 % | 3.7 % | 2.3 % |
| November | 80.0 % | 5.3 % | 7.1 % | 3.9 % | 2.3 % |
| October | 80.4 % | 5.2 % | 7.1 % | 3.7 % | 2.1 % |
| September | 81.0 % | 4.9 % | 7.2 % | 3.6 % | 2.0 % |
| August | 81.2 % | 4.6 % | 7.3 % | 3.4 % | 2.0 % |
| July | 81.3 % | 4.3 % | 7.6 % | 3.4 % | 2.0 % |
| June | 80.7 % | 3.9 % | 8.1 % | 3.7 % | 2.1 % |
| May | 80.7 % | 3.5 % | 8.5 % | 4.1 % | 1.6 % |
| April | 80.7 % | 3.4 % | 8.6 % | 4.2 % | 1.5 % |
| March | 81.4 % | 3.5 % | 8.7 % | 3.7 % | 1.3 % |
| February | 82.0 % | 3.4 % | 8.7 % | 3.4 % | 1.2 % |
| January | 81.9 % | 3.0 % | 9.1 % | 3.3 % | 1.3 % |
| 2019 | Chrome | Edge/IE | Firefox | Safari | Opera |
| November | 81.3 % | 3.2 % | 9.2 % | 3.5 % | 1.4 % |
| September | 81.4 % | 3.3 % | 9.1 % | 3.1 % | 1.6 % |
| July | 80.9 % | 3.3 % | 9.3 % | 2.7 % | 1.6 % |
| May | 80.4 % | 3.6 % | 9.5 % | 3.3 % | 1.7 % |
| March | 80.0 % | 3.8 % | 9.6 % | 3.3 % | 1.7 % |
| January | 79.5 % | 4.0 % | 10.2 % | 3.3 % | 1.6 % |
| 2018 | Chrome | IE/Edge | Firefox | Safari | Opera |
| November | 79.1 % | 4.1 % | 10.2 % | 3.8 % | 1.6 % |
| September | 79.6 % | 3.9 % | 10.3 % | 3.3 % | 1.5 % |
| July | 80.1 % | 3.5 % | 10.8 % | 2.7 % | 1.5 % |
| May | 79.0 % | 3.9 % | 10.9 % | 3.2 % | 1.6 % |
| March | 78.1 % | 4.0 % | 11.5 % | 3.3 % | 1.6 % |
| January | 77.2 % | 4.1 % | 12.4 % | 3.2 % | 1.6 % |
| 2017 | Chrome | IE/Edge | Firefox | Safari | Opera |
| November | 76.8 % | 4.3 % | 12.5 % | 3.3 % | 1.6 % |
| September | 76.5 % | 4.2 % | 12.8 % | 3.2 % | 1.2 % |
| July | 76.7 % | 4.2 % | 13.3 % | 3.0 % | 1.2 % |
| May | 75.8 % | 4.6 % | 13.6 % | 3.4 % | 1.1 % |
| March | 75.1 % | 4.8 % | 14.1 % | 3.6 % | 1.0 % |
| January | 73.7 % | 4.9 % | 15.4 % | 3.6 % | 1.0 % |
| 2016 | Chrome | IE/Edge | Firefox | Safari | Opera |
| November | 73.8 % | 5.2 % | 15.3 % | 3.5 % | 1.1 % |
| September | 72.5 % | 5.3 % | 16.3 % | 3.5 % | 1.0 % |
| July | 71.9 % | 5.2 % | 17.1 % | 3.2 % | 1.1 % |
| May | 71.4 % | 5.7 % | 16.9 % | 3.6 % | 1.2 % |
| March | 69.9 % | 6.1 % | 17.8 % | 3.6 % | 1.3 % |
| January | 68.4 % | 6.2 % | 18.8 % | 3.7 % | 1.4 % |
| Year | Chrome | IE | Firefox | Safari | Opera |
|---|---|---|---|---|---|
| 2015 | 63.3 % | 6.5 % | 21.6 % | 4.9 % | 2.5 % |
| 2014 | 59.8 % | 8.5 % | 24.9 % | 3.5 % | 1.7 % |
| 2013 | 52.8 % | 11.8 % | 28.9 % | 3.6 % | 1.6 % |
| 2012 | 42.9 % | 16.3 % | 33.7 % | 3.9 % | 2.1 % |
| 2011 | 29.4 % | 22.0 % | 42.0 % | 3.6 % | 2.4 % |
| 2010 | 16.7 % | 30.4 % | 46.4 % | 3.4 % | 2.3 % |
| 2009 | 6.5 % | 39.4 % | 47.9 % | 3.3 % | 2.1 % |
| 2008 | 52.4 % | 42.6 % | 2.5 % | 1.9 % | |
| 2007 | 58.5 % | 35.9 % | 1.5 % | 1.9 % | |
| Netscape | |||||
| 2006 | 62.4 % | 27.8 % | 0.4 % | 1.4 % | |
| 2005 | 73.8 % | 22.4 % | 0.5 % | 1.2 % | |
| Mozilla | |||||
| 2004 | 80.4 % | 12.6 % | 2.2 % | 1.6 % | |
| 2003 | 87.2 % | 5.7 % | 2.7 % | 1.7 % | |
| 2002 | 84.5 % | 3.5 % | 7.3 % |
- Chrome = Google Chrome
- Edge = Microsoft Edge
- IE = Microsoft Internet Explorer
- Firefox = Mozilla Firefox (identified as Mozilla before 2005)
- Mozilla = The Mozilla Suite (identified as Firefox after 2004)
- Safari = Apple Safari (and Konqueror. Both identified as Mozilla before 2007)
- Opera = Opera (from 2011; Opera Mini is included here)
- Netscape = Netscape Navigator (identified as Mozilla after 2006)
2017 Browser Comparison Charts
Statistics Can Be Misleading
'The pure and simple truth is rarely pure and never simple.' Why can i not install google chrome.
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W3Schools' statistics may not be relevant to your web site. Different sites attract different audiences. Some web sites attract developers using professional hardware, while other sites attract hobbyists using older computers.
Anyway, data collected from W3Schools' log-files over many years clearly shows the long term trends.
Browsers Developer Tools
Browser's developer tools can be used to inspect, edit and debug HTML, CSS, and JavaScript of the curently-loaded page. To learn more, check out the browser's own manual for developer tools:
Other Statistics
Computer Speed
Os x snow leopard to mountain lion. The first electrical computer, Z3 (1941), could do 5 instructions per second.
The first electronic digital computer, ENIAC (1945), could do 5000 instructions per second.
Today's computers can do 5 billion instructions per second.
| Computer | Year | Instructions per Second | Bits per Instruction |
|---|---|---|---|
| Z3 | 1941 | 5 | 4 |
| ENIAC | 1945 | 5.000 | 8 |
| IBM PC | 1981 | 5.000.000 | 16 |
| Intel Pentium | 1995 | 100.000.000 | 32 |
| AMD | 2000 | 1.000.000.000 | 64 |
| Today | 2020 | 5.000.000.000 | 128 |
Automated Machine Learning (autoML) is a process of building Machine Learning models by the algorithm with no human intervention. There are several autoML packages available for building predictive models:
- autoML from h2o
- auto_ml (update: unmaintained)
Update: currently there are available many AutoML packages, the list of AutoML software is available here
Datasets
In this post we compare three autoML packages (auto-sklearn, h2o and mljar). The comparison is performed on binary classification task on 28 datasets from openml. Datasets are described below.
Comparison methodology
- Each dataset was divided into train and test sets (70% of samples for training and 30% of samples for testing). Packages were tested on the same data splits.
- The autoML model was trained on the train set, with 1 hour limit for training time.
- The final autoML model was used to compute predictions on the test set (on samples not used for training).
- The logloss was used to assess the performance of the model (the lower logloss the better model). The logloss was selected because is more accurate than the accuracy metric.
- The process was repeated 10 times (with different seeds used for splits). Final results are average over 10 repetitions.
Results
2017 Browser Comparison Guide
The results are presented in the table and chart below. The best approach for each dataset is bolded.
Discussion
The poor performance of the auto-sklearn algorithm can be explained with 1 hour limit for training time. Auto-sklearn is using Bayesian optimization for hyperparameters tuning which has sequential nature and requires many iterations to find a good solution. The 1-hour training limit was selected from a business perspective — in my opinion, a user that is going to use autoML package prefers to wait 1 hour than 72 hours for the result. The h2o results compared to auto-sklearn are better on almost all datasets.
The best results were obtained by mljar package — it was the best algorithm on 26 from 28 datasets. On average it was by 47.15% better than auto-sklearn and 13.31% better than h2o autoML solution.
The useful feature of mljar is a user interface, so all models after the optimization are available through a web browser (mljar is saving all models obtained during optimization).
2017 Browser Comparison Tool
The code used for comparison is available https://github.com/mljar/automl_comparison
Check our open-source AutoML framework for tabular data!
2017 Browser Comparison Chart
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