We’ve got some code provided by Akash D. working on ticketmaster.co.uk. He automates browser (Chrome as well as Edge) using Selenium with Python. The rotating authenticated proxies are leveraged to keep undetected. Yet, the site is protected with Distil network.

# Tag: python

Suppose we’ve a following array:

```
arr = [[ 5.60241616e+02, 1.01946349e+03, 8.61527813e+01],
[ 4.10969632e+02 , 9.77019409e+02 , -5.34489688e+01],
[ 6.10031512e+02, 9.10689615e+01, 1.45066095e+02 ]]
```

How to print it with rounded elements using **map() **and **lamba() **functions?

```
l = list(map(lambda i: list(map(lambda j: round(j, 2), i)), arr))
print(l)
```

The result will be the following:

```
[[560.24, 1019.46, 86.15],
[410.97, 977.02, -53.45],
[610.03, 91.07, 145.07]]
```

in the post will reviewed a number of metrics for **evaluating classification** and **regression** models. For that we use the functions we use of the **sklearn** library. We’ll learn how to generate model data and how to train linear models and evaluate their quality.

#### The code as an IPython notebook

In this post we’ll show how to build regression linear models using the **sklearn.linear.model **module.

See also the post on classification linear models using the **sklearn.linear.model **module.

#### The code as an IPython notebook

In this post we’ll show how to build classification linear models using the **sklearn.linear.model **module.

#### The code as an IPython notebook

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 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.

#### The iPython notebook code

In this post we share how to plot distribution histogram for the Weibull ditribution and the distribution of sample averages as approximated by the Normal (Gaussian) distribution. We’ll show how the approximation accuracy changes with samples volume increase.

One may get the full .ipynb file here.