Webscraping.proThe problem is taken from kaggle.com. Based on historical data on bicycle rental and weather conditions, it is necessary to evaluate the demand for bicycle rental.
In the original problem statement, there are 11 features available. The feature set contains both real, categorical, and binary data. For the demonstration, a training sample bike_sharing_demand.csv is used from the original data.
See the Bike Sharing Demand, part 2 of the task where we performed advanced problem analysis.
Let’s answer the question: how do the parameters of the model affect its quality? And how can we select the optimal parameters for the task to be solved? We will look at the grid_search module in the sklearn library and learn how to select model parameters from the grid.
“Out of the 15 bank customers to whom the manager offered to connect autopayments, four agreed. Service activation is a binary feature that can be described by the Bernoulli distribution.”.
Let’s find the maximum likelihood estimate for the parameter p out of such a sample.
1) Likelihood function:
L(Xn, p) = ∏ p[Xi=1]*(1−p)[Xi=0] = p^4 * (1-p)^11
2) We find the maximum likelihood estimate for the parameter p.
We logarithm L(Xn, p) and get the following:
ln(p^4 * (1-p)^11) = 4*ln(p) + 11*ln(1-p)
3) Now we take its derivative and equate it to zero to find p.
[4ln(p) + 11ln(1-p)]` = 4 (ln(p))` + 11 (ln(1-p))` = 4/p + 11/(1-p) * (-1) = 0
Following: 4/p = 11/(1-p) => 4(1-p) = 11p => 15p = 4 => p = 4/15 =~ 0.26667.
We attach here a link to the User-Agents presented/selected by most popular browsers. U-A’s total number is over 1600.
node.exe index.js > scrape.log 2>&1
When executing file index.js we redirect all the console.log() output from console into a file scrape.log .
Sometimes we have the code with html tags that contain nothing but whitespace characters. Often those tags are nested. See a code below:
<div>
<div>
<div></div>
</div>
</div>What regex might be used to find and remove those tags?
Obvious solution is <div>\s*?<\/div> .
\s stands for “whitespace character”. It includes [ \t\n\x0B\f\r]. That is: \s matches a space( ) or a tab (\t) or a line(\n) break or a vertical tab (\x0B) sometimes referred as (\v) or a form feed (\f) or a carriage return (\r) .
In general case, we use the following regex:<(?<tag>[a-z]+?)( [^>]+?|)>\s*?<\/(\k<tag>)>
where <tag> is a named match group: [a-z]+?
When applying it recursively we might use the following code, JAVA:
public static String removeEmptyTags(String html) {
boolean compareFound = true;
Pattern pattern = Pattern.compile("<(?<tag>[a-z]+?)( [^>]+?|)>\\s*?</(\\k<tag>)>", Pattern.MULTILINE | Pattern.DOTALL);
while (compareFound) {
compareFound = false;
Matcher matcher = pattern.matcher(html);
if(matcher.find()) {
compareFound = true;
html = matcher.replaceAll("");
}
}
return html;
}How to handle cookie, user-agent, headers when scraping with JAVA? We’ll use for this a static class ScrapeHelper that easily handles all of this. The class uses Jsoup library methods to fetch from data from server and parse html into DOM document.
We’ll also interpret the found linear dependencies. That means we check whether the discovered pattern corresponds to common sense. The main purpose of the task is to show and explain by example what causes overfitting and how to overcome it.
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]]
Sequentum Enterprise is a powerful, multi-featured enterprise data pipeline platform and web data extraction solution. Sequentum’s CEO Sarah Mckenna doesn’t like to call it web scraping because, in its description, the web scraping refers to many different types of unmanaged and non-compliant techniques for obtaining web-based datasets.