Have You Ever Heard To Web Scraping Expert Use Business Information?

Have you ever heard of "data scraping?" Scaling of the use of information and data scraping technology made his fortune many a successful trader is not new technology. Sometimes website owners automated harvesting of your data can not be happy with sitting

Fortunately there is a modern solution to this problem. Proxy data scraping technology solves the problem by using proxy IP addresses. Scraping data each time you run the program, organized the evacuation of a website, the website thinks that it comes from a different IP address. For website owners, worldwide only a short period of increased traffic from the proxy data scraping sounds.

Now you might be asking yourself: "Can the technology proxy data scraping project?" Certainly better than the choice is dangerous and unreliable (but) free public proxy servers.

There are literally thousands of the world that is quite easy to free proxy servers are all on. But the trick is finding them. Many sites list hundreds of servers, but open to find, and the protocol perseverance, trial and error, works for one of the first lessons you something about server to server, or do not know what activities are going for. A public proxy requests or sensitive data transmitted through a bad idea.

A less risky scenario for proxy data for scraping a rotating proxy connection goes through many private IP addresses to hire.

Scrape data from the software-only website is the proven process of extracting data from the Web. Offer the best of the web software to extract data. We have the expertise and knowledge in web data extraction, image, display, email extract, eliminate services, data mining and web intervene to eliminate.

For example, many companies based on their own needs, in particular, helped to find the data.

Data collection

Generally, data, information, automated computer programs for processing by the appropriate structures transmission. Such formats and protocols are usually strictly structured, well-documented, easily decompose, and confusion to a minimum. Very often, these transmissions are not human readable.

Tractor unit that automatically Extractor is an email from a reliable source that the e-mail ID helps to remove. This is fundamentally different than web pages, HTML files, text files or other format, business services contacts duplicate email addresses without.

A web spider is a computer program that a methodical, automated or surf the World Wide Web in a systematic way. Especially the many sites in the search engines, up-to-date information, as a means to quickly use.

Proxy data scraping technology solves the problem by using proxy IP addresses. Every time your data scraping program is a production of a website, the website that comes from a different IP address. The owner of this website, proxy data from around the world in an increase in traffic looks exactly like scraping the short term.

Now you might be asking yourself, "my project where I can get the data scraping proxy technology?" "Do it yourself" solution, but unfortunately, there is no need to call. Consider hosting the proxy server you choose to rent, but this option is quite pricey, but definitely better than the alternative is incredibly dangerous (but) free public proxy server.

Source:http://www.articlesbase.com/outsourcing-articles/have-you-ever-heard-to-web-scraping-expert-use-business-information-6250856.html

Why Hand-Scraped Flooring?

So many types of flooring possibilities exist on the market, so why hand-scraped hardwood and why now? Trends for hardwoods come and go. In recent years, the demand for exotic species has grown, and even more closer to the present, requests for hand-scraped flooring are also increasing. As a result, nearly all species are available hand-scraped, but walnut, hickory, cherry, and oak are the most popular.

In the past, parquet was a popular style of flooring, and while seldom seen in the present, parquet was characterized by an angular style and contrasting woods. Not relying on color, hand-scraped flooring instead goes for texture. The wood is typically scraped by hand, creating a rustic and unique look for every plank. But rather than be exclusively rough, some hand-scraped products have a smoother sculpted look, such as hand-sculpted hardwood, and this flooring is often considered "classic."

Texture, as well, makes the flooring have additional visual and tactile dimensions. Those walking on the floor may just want to run their hands over the surface to feel the knots, scraping, and sculpted portions. However, tastes for hand-scraped flooring vary by region. According to top hardwood manufacturer Armstrong, the sculpted look is more requested in California, while a rustic appearance of knots, mineral streaks, and graining is more common in the Southwest. The Northeast, on the other hand, is just catching onto this trend.

There's no one look for hand-scraped flooring. Rather, hardwood is altered through scraping or brushing, finishing, or aging; a combination of such techniques may also be used.

Scraped or brushed hardwoods are sold under names "wire brushed," which has accented grain and no sapwood; "hand-sculpted," which indicates a smoother distressed appearance; and "hand hewn and rough sawn," which describes the roughest product available.

Aged hand-scraped products go by "time worn aged" or "antique." For both of these, the wood is aged, and then the appearance is accented through dark-colored staining, highlighting the grain, or contouring. A lower grade of hardwood is used for antique.

A darker stain tends to bring out the look of hand-scraped flooring. For woods that have specifically been stained, "French bleed" is the most common. Such a product has deeper beveled edges, and joints are emphasized with a darker color stain.

No matter the look for hand-scraped flooring, the hardwood is altered by hand, generally by a trained craftsman, such as an Amish woodworker. As a result, every plank looks unique. However, "hand-scraped" and "distressed" are often used interchangeably, but not all "distressed" products are altered by hand. Instead, the hardwood is distressed by machine, which presses a pattern into the surface of the wood.

Source:http://www.articlesbase.com/home-improvement-articles/why-hand-scraped-flooring-5488704.html

Scraping Fantasy Football Projections from the Web

In this post, I show how to download fantasy football projections from the web using R.  In prior posts, I showed how to scrape projections from ESPN, CBS, NFL.com, and FantasyPros.  In this post, I compile the R scripts for scraping projections from these sites, in addition to the following sites: Accuscore, Fantasy Football Nerd, FantasySharks, FFtoday, Footballguys, FOX Sports, WalterFootball, and Yahoo.

Why Scrape Projections?

Scraping projections from multiple sources on the web allows us to automate importing the projections with a simple script.  Automation makes importing more efficient so we don’t have to manually download the projections whenever they’re updated.  Once we import all of the projections, there’s a lot we can do with them, like:

•    Determine who has the most accurate projections
•    Calculate projections for your league
•    Calculate players’ risk levels
•    Calculate players’ value over replacement
•    Identify sleepers
•    Calculate the highest value you should bid on a player in an auction draft
•    Draft the best starting lineup
•    Win your auction draft
•    Win your snake draft

The R Scripts

To scrape the projections from the websites, I use the readHTMLTable function from the XML package in R.  Here’s an example of how to scrape projections from FantasyPros:

1 2 3 4 5 6 7 8    

#Load libraries

library("XML")

#Download fantasy football projections from FantasyPros.com

qb_fp <- readHTMLTable("http://www.fantasypros.com/nfl/projections/qb.php", stringsAsFactors = FALSE)$data

rb_fp <- readHTMLTable("http://www.fantasypros.com/nfl/projections/rb.php", stringsAsFactors = FALSE)$data

wr_fp <- readHTMLTable("http://www.fantasypros.com/nfl/projections/wr.php", stringsAsFactors = FALSE)$data

te_fp <- readHTMLTable("http://www.fantasypros.com/nfl/projections/te.php", stringsAsFactors = FALSE)$data

view raw FantasyPros projections hosted with ❤ by GitHub

The R Scripts for scraping the different sources are located below:

1.    Accuscore
2.    CBS - Jamey Eisenberg
3.    CBS – Dave Richard
4.    CBS – Average
5.    ESPN
6.    Fantasy Football Nerd
7.    FantasyPros
8.    FantasySharks
9.    FFtoday
10.    Footballguys – David Dodds
11.    Footballguys – Bob Henry
12.    Footballguys – Maurile Tremblay
13.    Footballguys – Jason Wood
14.    FOX Sports
15.    NFL.com
16.    WalterFootball
17.    Yahoo

Density Plot

Below is a density plot of the projections from the different sources:Calculate projections

Conclusion

Scraping projections from the web is fast, easy, and automated with R.  Once you’ve downloaded the projections, there’s so much you can do with the data to help you win your league!  Let me know in the comments if there are other sources you want included (please provide a link).

Source:http://fantasyfootballanalytics.net/2014/06/scraping-fantasy-football-projections.html

Extractions and Skin Care

As an esthetician or skin care professional, you may have heard some controversy over the matter of performing extractions during a routine facial service. What may seem like a relatively simple procedure can actually raise great controversy in the world of esthetics. Some estheticians regard extractions as a matter of providing a complete service while others see this as inflicting trauma to the skin. Learning more about both sides of the issue can help you as a professional in making an informed decision and explaining the issue to your clients.

What is an extraction?

As a basic review, an extraction is removing impurity (plug of dead skin or oil) from a pore or pimple. It is the removal of both blackheads and whiteheads from the skin. Extractions occur after the skin has been thoroughly cleansed, exfoliated and sometimes steamed to soften the area prior to extraction.

Why Do It?

Extractions are considered a "must" by many estheticians when performing a routine facial because they want to leave their clients skin looking and feeling it's best. When done correctly, a simple extraction should be quick and relatively painless. As a trained esthetician it is important to know if your client has sensitive skin which would make them more prone to the damage that can be caused by extractions.

Why Not?

Extractions should only be performed by a trained esthetician and should not be done in excess. Extractions can cause broken capillaries or sin irritations that can lead to more (not less) breakouts. Extractions can also cause discomfort for your client when done incorrectly so you should seek their permission before performing any type of extraction during their facial. Remember your client has the right to know any product or procedure being performed on their skin and make an informed choice.

Who Decides?

As an esthetician it may be entirely up to you or it may be a procedure within your salon to do or not do extractions. It is important to check the guidelines of your employer and know their policies before performing any procedure. Remember to explain extractions and their benefits and possible complications to your client. Trust is an important part of any relationship and your client needs to know you are being open and honest with them. The last thing you want as a professional is a reputation for inflicting unnecessary and unwanted procedures or damage to your client's skin.

Bellanina Institute's owner and director, Nina Howard, is a multi-talented, forward-thinking entrepreneur who has built the Bellanina brand form the ground up to a successful million-dollar spa, spa training business, and skin care product line. Nina is a Licensed Esthetician with Para-Medical studies, Massage Therapist, Polarity Therapist, Skin Care Educator, Artist, and Professional Interior Designer.

Source:http://ezinearticles.com/?Extractions-and-Skin-Care&id=5271715

Benefits of Predictive Analytics and Data Mining Services

Predictive Analytics is the process of dealing with variety of data and apply various mathematical formulas to discover the best decision for a given situation. Predictive analytics gives your company a competitive edge and can be used to improve ROI substantially. It is the decision science that removes guesswork out of the decision-making process and applies proven scientific guidelines to find right solution in the shortest time possible.

Predictive analytics can be helpful in answering questions like:

•    Who are most likely to respond to your offer?
•    Who are most likely to ignore?
•    Who are most likely to discontinue your service?
•    How much a consumer will spend on your product?
•    Which transaction is a fraud?
•    Which insurance claim is a fraudulent?
•    What resource should I dedicate at a given time?

Benefits of Data mining include:

•    Better understanding of customer behavior propels better decision
•    Profitable customers can be spotted fast and served accordingly
•    Generate more business by reaching hidden markets
•    Target your Marketing message more effectively
•    Helps in minimizing risk and improves ROI.
•    Improve profitability by detecting abnormal patterns in sales, claims, transactions etc
•    Improved customer service and confidence
•    Significant reduction in Direct Marketing expenses

Basic steps of Predictive Analytics are as follows:

•    Spot the business problem or goal
•    Explore various data sources such as transaction history, user demography, catalog details, etc)
•    Extract different data patterns from the above data
•    Build a sample model based on data & problem
•    Classify data, find valuable factors, generate new variables
•    Construct a Predictive model using sample
•    Validate and Deploy this Model

Standard techniques used for it are:

•    Decision Tree
•    Multi-purpose Scaling
•    Linear Regressions
•    Logistic Regressions
•    Factor Analytics
•    Genetic Algorithms
•    Cluster Analytics
•    Product Association

Should you have any queries regarding Data Mining or Predictive Analytics applications, please feel free to contact us. We would be pleased to answer each of your queries in detail.

Source:http://ezinearticles.com/?Benefits-of-Predictive-Analytics-and-Data-Mining-Services&id=4766989

Do blog scraping sites violate the blog owner's copyright?

I noticed that my blog has been posted on one of these website scraping sites. This is the kind of site that has no original content, but just repeats or scrapes content others have written and does it to get some small amount of ad income from ads on the scraping site. In essence the scraping site is taking advantage of the content of the originating site in order to make a few dollars from people who go to the site looking for something else. Some of these websites prey on misspelling. If you accidentally misspell the name of an original site, you just may end up with one of these patently commercial scraping sites.

Google defines scraping as follows:

•    Sites that copy and republish content from other sites without adding any original content or value
•    Sites that copy content from other sites, modify it slightly (for example, by substituting synonyms or using automated techniques), and republish it
•    Sites that reproduce content feeds from other sites without providing some type of unique organization or benefit to the user

My question, as set out in the title to this post, is whether or not scraping is a violation of copyright. It turns out that the answer is likely very complicated.  You have to look at the definition of a scraping site very carefully. Let me give you some hypotheticals to show what I mean.

Let's suppose that I write a blog and put a link in my blog post to your blog. Does that link violate your copyright? I can't imagine that anyone would think that there was problem with linking to another website on the Web. In this case, there is no content from the originating site, just a link.

But let's carry the hypothetical a little further. What if I put a link to your site and quote some of your content? Does this violate copyright law? If you are acquainted with any of the terminology of copyright law; think fair use. The issue here is whether or not the "quoted" material is a substantial reproduction of the entire original content? I would have the opinion that duplicating an entire blog post either with or without attribution would be a violation of the originator's copyright.

So is the scraping website protected by the "fair use" doctrine? Does the fact that the motivation for listing the original websites is to make money have anything to do with how you would decide if there was or was not a violation of the originator's copyright? By the way, the copyright does not make a distinction between a commercial and non-commercial use of the original constituting or not constituting a violation of copyright. The fact that the reproducing (scraping) party does not make money from the reproduction is not a factor in the issue of violation, although it may ultimately be an issue as to the amount of damages assessed.

Does the fact that the actions of the scraper annoy me, make any difference? I would answer, not in the least. Whether or not you are annoyed by the violation of the copyright makes no difference as to whether or not there is a violation. Likewise, you have no independent claims for your wounded feelings because of the copied content. Copyright is a statutory action (i.e. based on statutory law) and unless the cause of action is recognized by the law, there is no cause of action. Now, in an outrageous case, you may have  some kind of tort (personal injury) claim, but that is way outside of my hypothetical situation.

So what is the answer? Does scraping violate the originator's copyright? If only a small portion of the blog is copied (scraped) then I would have to have the opinion that it is not. Essentially, no matter what the motivation of the scrapper, there is not enough content copied to violate the fair use doctrine. Now, that is my opinion. Your's might differ. That is what makes lawsuits.

Do I think there are other reasons why scraping websites are objectionable? Certainly, but those reasons have nothing to do with copyright and they are probably the subject of another different blog post. So, if you are reading this from scraping website, bear in mind that there may be a serious problem with that type of website.

Source:http://genealogysstar.blogspot.in/2013/05/do-blog-scraping-sites-violate-blog.html

Local ScraperWiki Library

It quite annoyed me that you can only use the scraperwiki library on a ScraperWiki instance; most of it could work fine elsewhere. So I’ve pulled it out (well, for Python at least) so you can use it offline.

How to use
pip install scraperwiki_local
A dump truck dumping its payload

You can then import scraperwiki in scripts run on your local computer. The scraperwiki.sqlite component is powered by DumpTruck, which you can optionally install independently of scraperwiki_local.

pip install dumptruck
Differences

DumpTruck works a bit differently from (and better than) the hosted ScraperWiki library, but the change shouldn’t break much existing code. To give you an idea of the ways they differ, here are two examples:

Complex cell values
What happens if you do this?
import scraperwiki
shopping_list = ['carrots', 'orange juice', 'chainsaw']
scraperwiki.sqlite.save([], {'shopping_list': shopping_list})
On a ScraperWiki server, shopping_list is converted to its unicode representation, which looks like this:
[u'carrots', u'orange juice', u'chainsaw']
In the local version, it is encoded to JSON, so it looks like this:
["carrots","orange juice","chainsaw"]


And if it can’t be encoded to JSON, you get an error. And when you retrieve it, it comes back as a list rather than as a string.

Case-insensitive column names
SQL is less sensitive to case than Python. The following code works fine in both versions of the library.

In [1]: shopping_list = ['carrots', 'orange juice', 'chainsaw']
In [2]: scraperwiki.sqlite.save([], {'shopping_list': shopping_list})
In [3]: scraperwiki.sqlite.save([], {'sHOpPiNg_liST': shopping_list})
In [4]: scraperwiki.sqlite.select('* from swdata')

Out[4]: [{u'shopping_list': [u'carrots', u'orange juice', u'chainsaw']}, {u'shopping_list': [u'carrots', u'orange juice', u'chainsaw']}]

Note that the key in the returned data is ‘shopping_list’ and not ‘sHOpPiNg_liST’; the database uses the first one that was sent. Now let’s retrieve the individual cell values.

In [5]: data = scraperwiki.sqlite.select('* from swdata')
In [6]: print([row['shopping_list'] for row in data])
Out[6]: [[u'carrots', u'orange juice', u'chainsaw'], [u'carrots', u'orange juice', u'chainsaw']]

The code above works in both versions of the library, but the code below only works in the local version; it raises a KeyError on the hosted version.

In [7]: print(data[0]['Shopping_List'])
Out[7]: [u'carrots', u'orange juice', u'chainsaw']

Here’s why. In the hosted version, scraperwiki.sqlite.select returns a list of ordinary dictionaries. In the local version, scraperwiki.sqlite.select returns a list of special dictionaries that have case-insensitive keys.

Develop locally

Here’s a start at developing ScraperWiki scripts locally, with whatever coding environment you are used to. For a lot of things, the local library will do the same thing as the hosted. For another lot of things, there will be differences and the differences won’t matter.

If you want to develop locally (just Python for now), you can use the local library and then move your script to a ScraperWiki script when you’ve finished developing it (perhaps using Thom Neale’s ScraperWiki scraper). Or you could just run it somewhere else, like your own computer or web server. Enjoy!

Source:https://blog.scraperwiki.com/2012/06/local-scraperwiki-library/

A quick guide on web scraping: Why and how

Web scraping, which is the collection and cleaning of online data, is the first step in any
data-driven project. Here’s a short video that explains what scraping is, and how to create
automated scraping jobs using a digital tool.

This is a 15-minute video created by an instructor at Ohio State University. In the first six
minutes, the instructor talks about why we need web scraping; he then shows how to use a
scraping tool, OutWit Hub, to collect data scattered in a large database.

FYI: read reviews by Reporters’ Lab of OutWit Hub and other web scraping tools.

Source: http://www.mulinblog.com/quick-guide-web-scraping/

Scraping and Analyzing Angel List Syndicates: Kimono Labs + Silk

Because we use Silk to tell stories and visualize data, we are always looking for interesting ways to pull data into a Silk. Right now that means getting data into the CSV format. Fortunately, a wave of new and powerful visual webscraping tools and services have emerged. These make it very simple for anyone (no technical skills required) to scrape data from a website and export that data into a CSV which we can quickly upload into a Silk.

Cool New Scraping Tools

One of the tools we love in this new space is Kimono Labs. Backed by Y Combinator, Kimono combines a visual scraping editor with the ability to do very granular code-inspector level editing to scraping paths. Saved scrapes can be turned into APIs and exported as JSON. Kimono also lets you save time-series versioning of scrapes.

Data from angel-list-syndicates.silk.co
Like many startups, we watch the goings on at AngelList very closely. Syndicates are of particular interest. Basically, these are DIY venture capital pools that allow a qualified investor to serve as a syndicate leader and aggregate small investments from other qualified investors who are members of AngelList. The idea of the syndicates is to democratize the VC process and make it easier and less risky for individuals to participate.

We used Kimono to scrape information on the Top 25 Syndicates ranked by dollars backing each round. Kimono makes it very easy to visually designate which parts of a page to scrape and how many rows there are on a page. (Here you can see me highlighting the minimum dollar investment). We downloaded the information as a CSV and did a quick scrub to get it ready for upload to Silk. The process took no more than 15 minutes.

We could tell by eyeballing the numbers beforehand that a serious Power Law was in effect. And the actual data analysis on Silk bore this out. We chose to use a pie chart to show distribution. Three syndicates control nearly two-thirds of all the committed capital by Angel.co members in the syndicate program. One of the top three - Tim Ferriss - has no background as a venture capitalist or building technology companies but is rapidly becoming a force in startup investing. The person with the largest committed syndicate pool, Gil Penachina, is someone who is a quiet mover and shaker in Silicon Valley but he clearly packs a huge punch.

The largest syndicate in terms of likely commitments of deals per year is Foundry Group Angels, a group led by Brad Feld (@bfeld). While they put in less per deal, they are planning to back over 50 deals per year - a huge number. Trailing far behind those three was media impresario and Launch conference mogul Jason Calacanis, who is one of the most visible people in the startup space.

Source: http://blog.silk.co/post/83501793279/scraping-and-analyzing-angel-list-syndicates