Often we need to select certain html DOM elements excluding ones with certain names/ attributes/ attribute values. Let’s show how to do that.
In this post we’ll show how to build classification linear models using the sklearn.linear.model module.
The code as an IPython notebook
The Regularization is applying a penalty to increasing the magnitude of parameter values in order to reduce overfitting. When you train a model such as a logistic regression model, you are choosing parameters that give you the best fit to the data. This means minimizing the error between what the model predicts for your dependent variable given your data compared to what your dependent variable actually is.
See the practical example how to deal with overfitting by the regularization.
In the post we’ll get to know the Cross-validation strategies as from the Sklearn module. We’ll show the methods of how to perform k-fold cross-validation. All the iPython notebook code is correct for Python 3.6.
The iPython notebook code
In the post we will show how to generate model data and load standard datasets using the sklearn datasets module. We use sklearn.datasets in the Python 3.
The code of an iPython notebook
In this post we’ll show how to make a linear regression model for a data set and perform a stochastic gradient descent in order to optimize the model parameters. As in a previous post we’ll calculate MSE (Mean squared error) and minimize it.
In the previous post we shared how to disguise Selenium Chrome automation against Fingerprint checks. In this post we share the Puppeteer-extra with Stealth plugin to do the same. The test results are available as html files and screenshots.
In a previous post we’ve considered the ways to disguise an automated Chrome browser by spoofing some of its parameters – Headless Chrome detection and anti-detection. Here we’ll share the practical results of Fingerprints testing against a benchmark for both human-operated and automated Chrome browsers.
In the post we summarize how to detect the headless Chrome browser and how to bypass the detection. The headless browser testing should be a very important part of todays web 2.0. If we look at some of the site’s JS, we find them to checking on many fields of a browser. They are similar to those collected by fingerprintjs2.
So in this post we consider most of them and show both how to detect the headless browser by those attributes and how to bypass that detection by spoofing them.
See the test results of disguising the browser automation for both Selenium and Puppeteer extra.
In this post we’ll share with you the vivid yet simple application of the Linear regression methods. We’ll be using the example of predicting a person’s height based on their weight. There you’ll see what kind of math is behind this. We will also introduce you to the basic Python libraries needed to work in the Data Analysis.
