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+separately for each and every variable whose summary statistics are being computed. K Srikanth, Associate Professor, CSM Dept.,

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+Data Visualization, T wo or Higher Dimensions  Visualizing data in two or more dimensions provides additional insights into data, but because of the combinatorial explosion, the number of plots can become unmanageable.  In this section, all plots will use the Nasadata data set because the value of higher-dimensional visualization is seen more readily. K Srikanth, Associate Professor, CSM Dept.,

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+What is Predictive Analytics?  First, predictive analytics is data-driven, meaning that algorithms derive key characteristic of the models from the data itself rather than from assumptions made by the analyst.  Data-driven algorithms induce models from the data. The induction process can include identification of variables to be included in the model, parameters that define the model, weights or coefficients in the model, or model complexity. K Srikanth, Associate Professor, CSM Dept.,

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+Beautiful Soup 46 >>> print(p_copy) <p>Learn Python and <b>Java</b> and advanced <b>Java</b>! from Tutorialspoint</p> >>> Although the two copies (original and copied one) contain the same markup however, the two do not represent the same object: >>> print(soup.p == p_copy) True >>> >>> print(soup.p is p_copy) False >>> The only real difference is that the copy is completely detached from the original Beautiful Soup object tree, just as if extract() had been called on it.

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+""" >>> >>> from bs4 import BeautifulSoup >>> soup = BeautifulSoup(html_doc, 'html.parser') >>> Based on the above document, we will try to move from one part of document to another. Going down One of the important pieces of element in any piece of HTML document are tags, which may contain other tags/strings (tag’s children). Beautiful Soup provides different ways to navigate and iterate over’s tag’s children. 5. Beautiful Soup — Navigating by Tags

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+Histograms ◦ Overlay a second categorical variable: Some software allows the histogram to be color coded by a categorical variable, such as the target variable. This is helpful to determine if there is an obvious, visible relationship between the input variable and the target K Srikanth, Associate Professor, CSM Dept.,

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+indicative of unusual groups of data, and therefore could represent multidimensional outliers. ◦ To mitigate the effects of multidimensional outliers, the modeler could apply the same approaches described for single-variable outliers. K Srikanth, Associate Professor, CSM Dept.,

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+Single Variable Summaries  Applying simple statistics in Data Understanding ◦ Third, consider FISTDATE, the date of the first gift to the organization. The maximum value is 9603, meaning that the most recent date of the first gift in this data is March 1996. Given the timeframe for the data, this makes sense. But the minimum value is 0. The most likely explanation is that the value was unknown but at some time a value of 0 was used to replace the missing or null value.

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+--------------------------------------------------------------------------------
+Beautiful Soup 19 Navigating using tag names Easiest way to search a parse tree is to search the tag by its name. If you want the <head> tag, use soup.head: >>> soup.head <head><title>Tutorials Point</title></head> >>> soup.title <title>Tutorials Point</title> To get specific tag (like first <b> tag) in the <body> tag. >>> soup.body.b <b>The Biggest Online Tutorials Library, It's all Free</b> Using a tag name as an attribute will give you only the first tag by that name: >>> soup.a

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+>>> soup = BeautifulSoup(markup, "html.parser") >>> first_b, second_b = soup.find_all('b') >>> print(first_b == second_b) True >>> print(first_b.previous_element == second_b.previous_element) False However, to check if the two variables refer to the same objects, you can use the following: >>> print(first_b is second_b) False Copying Beautiful Soup objects To create a copy of any tag or NavigableString, use copy.copy() function, just like below: >>> import copy >>> p_copy = copy.copy(soup.p)

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+Beautiful Soup 27 The .previous_element attribute i s the exact opposite of .next_element. It points to whatever element was parsed immediately before this one. >>> last_a_tag.previous_element ' and\n' >>> >>> last_a_tag.previous_element.next_element <a class="prog" href=" [URL] id="link5">C</a> .next_elements and .previous_elements We use these iterators to move forward and backward to an element. >>> for element in last_a_tag.next_e lements:

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+collect and analyze data from the internet. Why Web-scraping? Web-scraping provides one of the great tools to automate most of the things a human does while browsing. Web-scraping is used in an enterprise in a variety of ways: Data for Research Smart analyst (like researche r or journalist) uses web scrapper instead of manually collecting and cleaning data from the websites. Products prices & popularity comparison Currently there are couple of services which use web scrappers to collect data from

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+Single Variable Summaries  CategoricalVariableAssessment K Srikanth, Associate Professor, CSM Dept.,

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+Feature Creation ( Feature Engineering)  Date andTimeVariable Features ◦ Common Date & Time Transformations K Srikanth, Associate Professor, CSM Dept.,

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+which is a key assumption for many statistical tests like t-tests or ANOVA. K Srikanth, Associate Professor, CSM Dept.,

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+What is Analytics?  Analytics includes both descriptive and exploratory types of analysis.  Answers questions like: What happened? and What patterns are present?  Types of Analytics: K Srikanth, Associate Professor, CSM Dept.,

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+compromise on speed. Further studies can be done by researchers who wish to reuse this implementation and modify it according to the data they want to extract, the analysis they wish to perform, and the website they wish to scrape. The implementation can be helpful, thus, to developers who are novices in the w eb scraping field or to researchers that wish to reuse the code for small data analytics projects. Keywords—web scraping, BeautifulSoup, data gathering, data analytics, data visualization

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+Multiple Variable Summaries  Spurious Correlations ◦ However, some spurious correlations are related, albeit not directly. ◦ For example, consider a town where it is noticed that as ice cream cone sales increase, swimsuit sales also increase. The ice cream sales don’t cause the increased sales in swimsuits, but they are related by an unobserved variable: the outdoor temperature. The correlation is spurious but not a random relationship in this case. K Srikanth, Associate Professor, CSM Dept.,

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+Feature Creation ( Feature Engineering)  Fixing Skew ◦ In summary, Table 4-7 lists transformations that are effective in converting positively or negatively skewed distributions to distributions that are at least more balanced and often are more normally distributed. K Srikanth, Associate Professor, CSM Dept.,

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+Single Variable Summaries  The Uniform Distribution: ◦ Some interesting properties of uniform distributions include:  The distribution is symmetric about the mean.  The distribution is finite, with a maximum and minimum value.  The mean and midpoint of the distribution are the same value.  Random number generators create uniform random distributions, most often in the range 0 to 1 as their default. K Srikanth, Associate Professor, CSM Dept.,

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+Beautiful Soup 26 ',\n' <a class="prog" href=" [URL] id="link2">C</a> ',\n' <a class="prog" href=" [URL] id="link1">Java</a> 'Top 5 most used Programming Languages are: \n' Going back and forth Now let us get back to first two lines in our previous “html_doc” example: <html><head><title>Tutorials Point</title></head> <body>

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+4 if (soup.table["id"] == "mytable"): print(soup.table) Children and Parents Tags have the built-in generator children and the built-in list parents, which allow you to access all the tags nested inside of that tag, or all of the tags that tag is nested inside. Generators allow you to loop through a list one at a time without having to produce the whole list at once, whi ch can be much more efficient when you might have many hundreds of children in one tag. For example:

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+>>> soup = BeautifulSoup("<h2 id='message'>Hello, Tutorialspoint!</h2>") >>> soup.string.replace_with("Online Learning!") 'Hello, Tutorialspoint!' >>> soup.string 'Online Learning!' >>> soup <html><body><h2 id="message">Online Learning!</h2></body></html> BeautifulSoup BeautifulSoup is the object created when we try to scrape a web resource. So, it is the complete document which we are trying to scrape. Most of the time, it is treated tag object. >>> from bs4 import BeautifulSoup

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+Multiple Variable Summaries  Hidden Value in Variable Interactions: Simpson’s Paradox ◦ Based on the information, you would obviously choose Hospital B because its mortality rate is 33 percent lower than Hospital A. ◦ But what if you know an additional piece of information before selecting the hospital, namely the condition of the patient prior to surgery? K Srikanth, Associate Professor, CSM Dept.,

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+Multiple Variable Summaries  Correlations ◦ Correlation measures the statistical relationship between variables, indicating how they change together, which is crucial for identifying patterns, selecting relevant features (like reducing redundancy), understanding data dependencies, and building more accurate models for tasks such as forecasting sales or risk assessment. ◦ It helps distinguish between strong/weak and positive/negative relationships, guiding data

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+Beautiful Soup 50 This may be due to python built -in HTML parser sometimes skips tags it doesn’t understand. XML parser Error By default, BeautifulSoup package parses the documents as HTML , however, it is very easy-to-use and handle ill-formed XML in a very elegant manner using beautifulsoup4. To parse the document as XML, you need to have lxml parser and you just need to pass the “xml” as the second argument to the Beautifulsoup constructor: soup = BeautifulSoup(markup, "lxml-xml") or

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+House. On the other hand, when the Redskins lost, the incumbent party lost the election. Interesting, it worked perfectly for 17 consecutive elections (1940 –2000). K Srikanth, Associate Professor, CSM Dept.,

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+year-olds did not write checks or submit credit card donations to the organization. Could it mean that donations were given in the name of a 1- year-old? K Srikanth, Associate Professor, CSM Dept.,

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+Beautiful Soup 14 Attributes (tag.attrs) A tag object can have any number of attributes. The tag <b class=”boldest”> has an attribute ‘class’ whose value is “boldest”. Anything that is NOT tag, is basically an attribute and must contain a value. You can access the attributes either through accessing the keys (like accessing “class” in above example) or directly accessing through “.attrs” >>> tutorialsP = BeautifulSoup("<div class='tutorialsP'></div>",'lxml') >>> tag2 = tutorialsP.div

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+>>> for element in last_a_tag.next_e lements: print(repr(element)) 'C' ';\n \nas per online survey.' '\n' <p class="prog">Programming Languages</p> 'Programming Languages' '\n'

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+◦ The values of correlation range from –1 to +1, where +1 indicates perfect positive correlation and –1 indicates perfect negative correlation. K Srikanth, Associate Professor, CSM Dept.,

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+What the Data Looks Like  Examples of categorical variables include State, Title_Code and target variables such as Responder.  Flag variables or binary variables are often used as names for categorical variables having only two values, like Gender (M,F), responses to questions (Yes and No), and dummy variables (1 and 0). K Srikanth, Associate Professor, CSM Dept.,

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+Beautiful Soup ii T able of Contents About the Tutorial ............................................................................................................................................ i Audience ........................................................................................................................................................... i

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+Navitime website since it aligns with the paper’s aim and is inclusive of a list of convenience stores and gas stations in Japan. Many other papers have used web scraping in different contexts. Thomas and Mathu r [6] utilized a Python -based approach, combining both BeautifulSoup and Scrapy. Scrapy is a web -crawling framework that uses an application programming interface (API) to extract data and allow developers to write crawlers. Here, BeautifulSoup is used to

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+rates, whereas men tended to apply to departments with higher overall acceptance rates. ◦ It was only after considering the interactions that the trend appeared. K Srikanth, Associate Professor, CSM Dept.,

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+What is Analytics?  Data-driven: Analytics uses data as the source of truth, often relying on computation rather than just intuition.  Pattern discovery: The goal is to uncover meaningful patterns or relationships within historic records of data.  Insight and decision support: The ultimate purpose is to gain insight and influence better decision-making — not just to generate numbers.  Historical basis: Because data comes from past events, analytics really looks backward to explain or predict outcomes.

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+[] >>> >>> soup.find('h2') If soup.find_all() method can’t find anything, it returns empty list wherea s find() returns None. find_parents() and find_parent() Unlike the find_all() and find() methods which traverse the tree, looking at tag’s descendents, find_parents() and find_parents methods() do the opposite, they traverse the tree upwards and look at a tag’s (or a string’s) parents. Syntax find_parents(name, attrs, string, limit, **kwargs) find_parent(name, attrs, string, **kwargs)

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+Feature Creation ( Feature Engineering)  NumericalVariable Scaling ◦ Z-Score Standardization: Centers the data around a mean of 0 with a standard deviation of 1.  Formula: (x - mean) / standard deviation  Example: This is preferred when the data contains outliers or follows a Gaussian distribution, as it preserves the shape of the original distribution while making variables comparable. K Srikanth, Associate Professor, CSM Dept.,

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+html_doc = response.read() soup = BeautifulSoup(html_doc, 'html.parser') # Read the web page for that URL and parse it table = soup.find('table',id='team_batting') # Look for the table with the team_batting id if table != None: for tfoot in table.tfoot: # Look for the table footer td = tfoot.find_all('td') # Find all of the td elements and save in a list

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+which is useful in the context of the study. Despite Python being an obvious option, the researcher contended that it would be difficult to implement and that future studies can adopt it as the root of their web scraping endeavor. The results could be of importance to those who want to develop more secure websites against malicious users as well as researchers who wish to gather data without facing security breaches. Another study conducted by Chaulagain, Pandey et al.,

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+Data Visualization, T wo or Higher Dimensions  Scatterplots ◦ Anscombe’s Quartet:  Anscombe’s Quartet is a set of four small datasets created by statistician Francis Anscombe in 1973. It is famously used to demonstrate the importance of data visualization in exploratory data analysis.  Each of the four datasets consists of 11 (x, y) points. Despite appearing completely different when plotted, they share nearly identical summary statistics.  After seeing these measures and values for all four of

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+Single Variable Summaries  The Uniform Distribution: ◦ The uniform distribution assumes that a variable has a fixed,finite range. ◦ Figure 3-2 shows a conceptualization of a uniform distribution. The mean value is in the center of the range of the variable. ◦ In uniform distributions, the mean can also be computed by simply computing one-half of the difference between the maximum minus the minimum of the values of the variable. K Srikanth, Associate Professor, CSM Dept.,

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+distributions have the same disproportionate effect. Other algorithms, such as decision trees, are unaffected by skew. K Srikanth, Associate Professor, CSM Dept.,

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+extract the year from the text in the th element, and then find any cells that are bolded via the strong tag. Then, we look inside the strong tag , and see if it has a href attribute inside (which is used for links). If it does, then we can extract that url as well as identify the team name (the contents of the cell text). Once we have that, we call the team page function and add the returned value to a list.

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+>>> soup.a <a class="prog" href=" [URL] id="link1">Java</a> To get all the tag’s attribute, you can use find_all() method: >>> soup.find_all("a") [<a class="prog" href=" [URL] id="link1">Java</a>, <a class="prog" href=" [URL] id="link2">C</a>, <a class="prog" href=" [URL] id="link3">Python</a>, <a class="prog"

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+1 Paper 119-2022 Simmering Data: Using Beautiful Soup and Python to Scrape Data from Web Pages Joe Matise, NORC at the University of Chicago ABSTRACT Ever look at a table on a web page and wished you had it in a data set? You probably took one look at the source of the web page and then decided it wasn’t worth the hassle. SAS has some tools to help, but oftentimes tables have too much complexity to parse without hours of work. Instead, parse the web page

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+all in the upper range of Band11, whereas clover and alfalfa are at the lower range of Band11. ◦ However, the plot shows that for Band9, soy is larger than wheat and rye. K Srikanth, Associate Professor, CSM Dept.,

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+What is Predictive Analytics?  Y ouneed algorithms to sift through all of the potential combinations of inputs in the data—the patterns—and identify which ones are the most interesting.  The analyst can then focus on these patterns, undoubtedly a much smaller number of inputs to examine.  A predictive model identifies six of the 50 input variables as the most significant contributors to accurate models. K Srikanth, Associate Professor, CSM Dept.,

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+Single Variable Summaries  Rank Ordered Statistics ◦ Quartiles are a special case of the general rank- ordered measure called quantiles. Other commonly used quantiles are listed in Table 3-5. K Srikanth, Associate Professor, CSM Dept.,

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+Single Variable Summaries  Applying simple statistics in Data Understanding ◦ Y ouhave already seen this in FISTDATE: The 0 for the minimum value, the recoding of missing to the value 0, distorts the mean value. AGE is likewise distorted because of the 1-year-olds in the data. These are all values that need to be investigated and possibly corrected before modeling begins. K Srikanth, Associate Professor, CSM Dept.,