Earn Money From Price Comparison Through Web Scraping

Many individuals discover the pot of gold just within their reach. They have realized that there is money in the web. Cyber technology has blessed mankind with so many benefits that makes money very possible by just some clicks on the mouse and keyboard. Building a price comparison website is an effective way of helping clients find their desired products while you as the owner earn money at the same time.

Building price comparison websites

There is indeed much money in building price comparison websites but it is not an easy task especially for a novice in maintaining a website of one’s own. Since this entails some serious programming and ample familiarity with data feeds, you have to have a good working plan. In addition, what you are venturing into is greater than the usual blogs about just anything that you can think of. Furthermore, you are stepping into the vast field of electronic marketing, therefore you must be ready.

The first point of consideration is to identify which products or services are you going to include in your website. Choose a product or service that you and a majority of clients are mostly interested in. Suppose you want you to choose sports as your theme then you can include items and prices of sports gear, clothing such as uniforms, training videos, books, and other safety stuff. You need to do some research and even a survey to determine whether the goods and services you are promoting on your website are in demand and are what most people want to know. Moreover, it is on this stage that you may need the help of experts and veterans in the field of building to be assured that you are on the right track.

In addition, be willing to change in case your chosen category is not gaining readership or visitors. Then evaluate whether you need to expand or to be more specific in your description of the products and the comparison of the prices. Make your site prominent by search engine optimization (SEO) and make sure to acknowledge also that not too many people visit a site that is not free.

Helping visitors choose the best product/services

Good marketing strategy starts with knowing who your target audience are. There is indeed a need to do a lot of planning and research in order to understand your client’s needs and preferences. Moreover, knowing them thoroughly leads to achieving 100% consumer satisfaction. When you have provided everything they need to know about certain products, they would not need to seek elsewhere which will also gain you more regular visitors. Remember that your audience are members of communities and social networks such that there is a great possibility that they would spread the word around about the good services you are offering.

If there is a need to conduct a survey in addition to research, you should resort to it. In this manner you can discover what goods and services are not yet completely exhausted by the other websites or web creators. Ample knowledge about your potential visitors and consumers will surely make you effectively provide them with adequate statistics for their needs.

Your site will then look like a complete guidebook for them that will give them the best value for their money. Therefore, it must be thoroughly filled with product details, uses, options, and prices.

Making money as affiliate of eCommerce websites

Maintaining a price comparison website gives you less worry about getting paid or having your products bought and sold because income comes in through advertising and affiliate sales. Affiliate marketing is a way of earning money online by serving as a publisher for promotion of products, services or sites of businesses. The affiliate receives rewards from businesses for each visitor or client that comes to the business website or buys its product through the efforts of the advertising and promotion that is made by the affiliate. This is the online version of the concept of agent or referral fee sales channel. Aside from website owners, bloggers as well as members of community forums can also serve as affiliates. The affiliate earns money in three ways: through pay per link; pay per sale and pay per lead.

Trust in the reliability of the product - You should have a personal belief or confidence in the product you are promoting not only because it makes you sound more convincing, but also because you need to maintain your clients and establish credibility in your blog or website. In other words, don’t just pick any product. If you cannot use them personally, they should at least have several positive reviews and no negative ones.

Maintain credibility with readers and fellow bloggers - Befriend your readers and your co-bloggers by answering their queries sincerely and quickly. Your friendly attitude can win you their trust which is a very vital element of affiliate marketing.

Do reviews - In addition to publishing price comparison, you can gain more visitors by writing about the product and do proper SEO (Search Engine Optimization). So the expected happens, the more prominent the product becomes online, the higher will be your income.

Link with friends thru social media - Your friends have friends and their friends have also friends. Just think of how powerful your social media site can be when you post your link on your account on Facebook, Twitter or MySpace and others. Since trust is built on friendship, it is easy to get clients from among your friends and their friends.

Overall, you get all pertinent information about certain products through web data mining or web scraping. All you need to do is to be keen to the needs of your clients and use web content extraction efficiently.

Source: http://www.loginworks.com/earn-money-price-comparison-web-scraping/

Web Scraping – An Illegal Activity or Simple Data Collection?

Gone are the days when skillful extraction of information pertaining to real estate such as foreclosures, homes for sale, or mortgage records was considered difficult. Now, it is not only easy to extract data from real estate websites but also scrape real estate data on a consistent basis to add more value to your portal, or ensure that updated data is available to your visitors at all times. From downloading actual scanned documents in the form of PDF files to scraping websites for deeds or mortgages, smartly designer data extraction tools can do it all.

However, the one question that still manages to come to the front in the minds of those who scrape real estate listings and others are whether the act is illegal in nature or a simple way of collecting data.

Take a look.

Web Scraping—What is it?

Generally speaking, web scraping refers to programs that are designed to simulate human internet surfing and access websites on behalf of their users. These tools are effective in collecting large quantities of data that are otherwise difficult for end users to access. They process semi-structured or unstructured data pages of targeted websites and transform available data into a more structured format that can be extracted or manipulated by the user easily.

Quite similar to web indexing that is used by search engines, the end motivation of web scraping is much different. While web indexing makes search engines far more efficient, the latter is used for reasons like market research, change detection, data monitoring, or in some events, theft. But then, it is not always a bad thing. You just need to know if a website allows web scraping before proceeding with the act.

Fine Line between Stealing and Collecting Information

Web scraping rides an extremely fine line between the acts of collecting relevant information and stealing the same. The websites that have copyright disclosure statements in place to protect their website information are offended by outsiders raiding their data without due permission. In other words, it amounts to trespassing on their portal, which is unacceptable—both ethically and legally. So, it is very important for you to read all disclosure statements carefully and follow along in the right way. As web scraping cases may turn into felony offenses, it is best to guard against any kind of scrupulous activity and take permission before scraping data.

The Good News

However, all is not grey in data extraction processes. Reputed agencies are helping their clients scrape valuable data for gaining more value through legal means and carefully used tools. If you are looking for such services, then do get in touch with a reliable web scraping company of your choice and take your business to the next levels of success.

Source: https://3idatascraping.wordpress.com/2015/03/11/web-scraping-an-illegal-activity-or-simple-data-collection/

I Don’t Need No Stinking API: Web Scraping For Fun and Profit

If you’ve ever needed to pull data from a third party website, chances are you started by checking to see if they had an official API. But did you know that there’s a source of structured data that virtually every website on the internet supports automatically, by default?

scraper toolThat’s right, we’re talking about pulling our data straight out of HTML — otherwise known as web scraping. Here’s why web scraping is awesome:

Any content that can be viewed on a webpage can be scraped. Period.

If a website provides a way for a visitor’s browser to download content and render that content in a structured way, then almost by definition, that content can be accessed programmatically. In this article, I’ll show you how.

Over the past few years, I’ve scraped dozens of websites — from music blogs and fashion retailers to the USPTO and undocumented JSON endpoints I found by inspecting network traffic in my browser.

There are some tricks that site owners will use to thwart this type of access — which we’ll dive into later — but they almost all have simple work-arounds.

Why You Should Scrape

But first we’ll start with some great reasons why you should consider web scraping first, before you start looking for APIs or RSS feeds or other, more traditional forms of structured data.

Websites are More Important Than APIs

The biggest one is that site owners generally care way more about maintaining their public-facing visitor website than they do about their structured data feeds.

We’ve seen it very publicly with Twitter clamping down on their developer ecosystem, and I’ve seen it multiple times in my projects where APIs change or feeds move without warning.

Sometimes it’s deliberate, but most of the time these sorts of problems happen because no one at the organization really cares or maintains the structured data. If it goes offline or gets horribly mangled, no one really notices.

Whereas if the website goes down or is having issues, that’s a more of an in-your-face, drop-everything-until-this-is-fixed kind of problem, and gets dealt with quickly.

No Rate-Limiting

Another thing to think about is that the concept of rate-limiting is virtually non-existent for public websites.

Aside from the occasional captchas on sign up pages, most businesses generally don’t build a lot of defenses against automated access. I’ve scraped a single site for over 4 hours at a time and not seen any issues.

Unless you’re making concurrent requests, you probably won’t be viewed as a DDOS attack, you’ll just show up as a super-avid visitor in the logs, in case anyone’s looking.

Anonymous Access

There are also fewer ways for the website’s administrators to track your behavior, which can be useful if you want gather data more privately.

With APIs, you often have to register to get a key and then send along that key with every request. But with simple HTTP requests, you’re basically anonymous besides your IP address and cookies, which can be easily spoofed.

The Data’s Already in Your Face

Web scraping is also universally available, as I mentioned earlier. You don’t have to wait for a site to open up an API or even contact anyone at the organization. Just spend some time browsing the site until you find the data you need and figure out some basic access patterns — which we’ll talk about next.

Let’s Get to Scraping

So you’ve decided you want to dive in and start grabbing data like a true hacker. Awesome.

Just like reading API docs, it takes a bit of work up front to figure out how the data is structured and how you can access it. Unlike APIs however, there’s really no documentation so you have to be a little clever about it.

I’ll share some of the tips I’ve learned along the way.

Fetching the Data

So the first thing you’re going to need to do is fetch the data. You’ll need to start by finding your “endpoints” — the URL or URLs that return the data you need.

If you know you need your information organized in a certain way — or only need a specific subset of it — you can browse through the site using their navigation. Pay attention to the URLs and how they change as you click between sections and drill down into sub-sections.

The other option for getting started is to go straight to the site’s search functionality. Try typing in a few different terms and again, pay attention to the URL and how it changes depending on what you search for. You’ll probably see a GET parameter like q= that always changes based on you search term.

Try removing other unnecessary GET parameters from the URL, until you’re left with only the ones you need to load your data. Make sure that there’s always a beginning ? to start the query string and a & between each key/value pair.

Dealing with Pagination

At this point, you should be starting to see the data you want access to, but there’s usually some sort of pagination issue keeping you from seeing all of it at once. Most regular APIs do this as well, to keep single requests from slamming the database.

Usually, clicking to page 2 adds some sort of offset= parameter to the URL, which is usually either the page number or else the number of items displayed on the page. Try changing this to some really high number and see what response you get when you “fall off the end” of the data.

With this information, you can now iterate over every page of results, incrementing the offset parameter as necessary, until you hit that “end of data” condition.

The other thing you can try doing is changing the “Display X Per Page” which most pagination UIs now have. Again, look for a new GET parameter to be appended to the URL which indicates how many items are on the page.

Try setting this to some arbitrarily large number to see if the server will return all the information you need in a single request. Sometimes there’ll be some limits enforced server-side that you can’t get around by tampering with this, but it’s still worth a shot since it can cut down on the number of pages you must paginate through to get all the data you need.

AJAX Isn’t That Bad!

Sometimes people see web pages with URL fragments # and AJAX content loading and think a site can’t be scraped. On the contrary! If a site is using AJAX to load the data, that probably makes it even easier to pull the information you need.

The AJAX response is probably coming back in some nicely-structured way (probably JSON!) in order to be rendered on the page with Javscript.

All you have to do is pull up the network tab in Web Inspector or Firebug and look through the XHR requests for the ones that seem to be pulling in your data.

Once you find it, you can leave the crufty HTML behind and focus instead on this endpoint, which is essentially an undocumented API.

(Un)structured Data?

Now that you’ve figured out how to get the data you need from the server, the somewhat tricky part is getting the data you need out of the page’s markup.

Use CSS Hooks

In my experience, this is usually straightforward since most web designers litter the markup with tons of classes and ids to provide hooks for their CSS.

You can piggyback on these to jump to the parts of the markup that contain the data you need.

Just right click on a section of information you need and pull up the Web Inspector or Firebug to look at it. Zoom up and down through the DOM tree until you find the outermost <div> around the item you want.

This <div> should be the outer wrapper around a single item you want access to. It probably has some class attribute which you can use to easily pull out all of the other wrapper elements on the page. You can then iterate over these just as you would iterate over the items returned by an API response.

A note here though: the DOM tree that is presented by the inspector isn’t always the same as the DOM tree represented by the HTML sent back by the website. It’s possible that the DOM you see in the inspector has been modified by Javascript — or sometime even the browser, if it’s in quirks mode.

Once you find the right node in the DOM tree, you should always view the source of the page (“right click” > “View Source”) to make sure the elements you need are actually showing up in the raw HTML.

This issue has caused me a number of head-scratchers.

Get a Good HTML Parsing Library

It is probably a horrible idea to try parsing the HTML of the page as a long string (although there are times I’ve needed to fall back on that). Spend some time doing research for a good HTML parsing library in your language of choice.

Most of the code I write is in Python, and I love BeautifulSoup for its error handling and super-simple API. I also love its motto:

    You didn’t write that awful page. You’re just trying to get some data out of it. Beautiful Soup is here to help. :)

You’re going to have a bad time if you try to use an XML parser since most websites out there don’t actually validate as properly formed XML (sorry XHTML!) and will give you a ton of errors.

A good library will read in the HTML that you pull in using some HTTP library (hat tip to the Requests library if you’re writing Python) and turn it into an object that you can traverse and iterate over to your heart’s content, similar to a JSON object.

Some Traps To Know About

I should mention that some websites explicitly prohibit the use of automated scraping, so it’s a good idea to read your target site’s Terms of Use to see if you’re going to make anyone upset by scraping.

For two-thirds of the website I’ve scraped, the above steps are all you need. Just fire off a request to your “endpoint” and parse the returned data.

But sometimes, you’ll find that the response you get when scraping isn’t what you saw when you visited the site yourself.

When In Doubt, Spoof Headers

Some websites require that your User Agent string is set to something they allow, or you need to set certain cookies or other headers in order to get a proper response.

Depending on the HTTP library you’re using to make requests, this is usually pretty straightforward. I just browse the site in my web browser and then grab all of the headers that my browser is automatically sending. Then I put those in a dictionary and send them along with my request.

Note that this might mean grabbing some login or other session cookie, which might identify you and make your scraping less anonymous. It’s up to you how serious of a risk that is.

Content Behind A Login

Sometimes you might need to create an account and login to access the information you need. If you have a good HTTP library that handles logins and automatically sending session cookies (did I mention how awesome Requests is?), then you just need your scraper login before it gets to work.

Note that this obviously makes you totally non-anonymous to the third party website so all of your scraping behavior is probably pretty easy to trace back to you if anyone on their side cared to look.

Rate Limiting

I’ve never actually run into this issue myself, although I did have to plan for it one time. I was using a web service that had a strict rate limit that I knew I’d exceed fairly quickly.

Since the third party service conducted rate-limiting based on IP address (stated in their docs), my solution was to put the code that hit their service into some client-side Javascript, and then send the results back to my server from each of the clients.

This way, the requests would appear to come from thousands of different places, since each client would presumably have their own unique IP address, and none of them would individually be going over the rate limit.

Depending on your application, this could work for you.

Poorly Formed Markup

Sadly, this is the one condition that there really is no cure for. If the markup doesn’t come close to validating, then the site is not only keeping you out, but also serving a degraded browsing experience to all of their visitors.

It’s worth digging into your HTML parsing library to see if there’s any setting for error tolerance. Sometimes this can help.

If not, you can always try falling back on treating the entire HTML document as a long string and do all of your parsing as string splitting or — God forbid — a giant regex.

Source: https://blog.hartleybrody.com/web-scraping/

Hand Scraped Flooring For a Natural and Unique Look

An option in hardwood flooring that is being increasingly adopted by those looking for something new, innovative and unique for their homes is hand scraped flooring. This type of wood flooring helps one achieve a distinct natural look on one's floor and also has a couple of advantages.

There are three types of scraping that you can get done on your wooden flooring: light, medium and hard. Preferably, if you have a light colored woodwork, then you should go for light scraping and if your floor has a darker shade, then you should opt for hard scraping. But, irrespective of the type of scraping you go for, you must ensure that the laborers doing the scraping are very skilled and impeccable in their job as hand scraping floors is an art that demands patience, time, talent and hard work.

Nowadays, many people tend to go for machine scraping, attracted by the lower investment involved in it. But such people are unable to achieve the requisite natural effect on their floors as machines create patterns on the floors that are easily detectable. These patterns do not emerge with hand scraping and the consequent look is as random and unique as it gets.

Though such scraped flooring is a costly option in flooring, it demands little maintenance. While with perfectly smooth surfaces, you will be always on the edge ensuring that there are no scratches, with hand scraped floors, you will not have to be concerned about this as any new scratches will only add to the already distressed appearance of the flooring.

Prefinished hand scraped wood flooring is also available in the market nowadays. These eliminate the need of any on-site scraping. But this option is of course unsuitable for those who have already got their floors installed. As it is, if you get on-site scraping done, you will have more control over things as you would be able to see the scraping as it develops and would be therefore in a position to exercise your preferences more.

Source: http://ezinearticles.com/?Hand-Scraped-Flooring-For-a-Natural-and-Unique-Look&id=4581623

Data Mining and Predictive Analysis

Data collection and curing is the core foundation of most businesses. Database building thus is an important function and activity where enterprises invest heavily. With information now available on the Internet and easily obtained, it raises the importance of having professionals who crawl data and offer web scraping services.

Once the data is accessed, though, it is important to filter out the relevant data based on the business need. Although Many DaaS provider convert the unstructured web data into meaningful structured data it is recommended to be internally equipped to use the data to its maximum.

This understanding has given rise to the field of Data Mining. Data Mining is designed to explore large amounts of data in search of consistent patterns and connections between the variables and validate the findings by applying the detected patterns to the new sets of the data. Once these connections are established and understood, the end goal is to be able to predict the possible outcomes using predictive analysis techniques.

Together, both Data Mining and predictive analysis aid in making marketing campaigns more efficient. While predictive analysis helps simulate and understand what may happen, data mining helps identify exciting data patterns and connections.

The process of Data Mining and Predictive analysis consists of 3 steps

Exploration

Once a database is compiled, it needs to be cleaned, analysed and potential connections need to be built. This process involves filtering the relevant data and identifying the possible predictors. Data Exploration also sets a premise for preliminary feature selection to manage number of variables. This data is then prepared for statistical analysis using a wide variety of graphical and statistical parameters. This helps identify the most relevant variables and setups the predictive models to be built.

Data mining process

Validation
Next comes building various models and choosing the most relevant ones. This decision is based on their possible predictive performance and of being able to produce stable results across all the samples. Simple as it sounds, to truly get the results, all possible models must be treated with data to simulate scenarios. The model with most stable statistical feature is validated.

Application

Once the relevant models are finalised, the same is applied to new data to understand and predict the estimated outcomes. Application of data models is an ongoing and complex process since every new dataset needs to be configured in the model.

Data Mining and predictive analysis essentially involves blending statistical methodology where the traditional statistics machine learning and complex algorithms. This greatly increases the need for efficient and skilled data handlers. This could include data analysts and scientists.

See how you can become data scientist here:

Data crunchers use data mining and predictive analysis actively to get an edge in the big data management. Database platforms like Hadoop assist in database management and large-scale distribution. But the costs involved in setting up data centres and big data management capacity are high. Budgets allocated within the enterprise are more project-focussed and analytics budgets are usually limited. Quite often, big data and analytics project fail to launch because of this problem! The other problem is that to run effective predictive models, data requires to be handled by scientists with experience. Finding and setting together a technologically-advanced team is a daunting task most enterprises face outside the tech domain.

Predictive Analysis model

A predictive analysis model is essentially predicting the all possible outcomes from a given set of data. Here are a few steps that can be taken to help build and identify the “ideal” predictive analysis model. These steps more or less mirror the usual statistical methodology of building a test model.

Defining an objective

This is the first and a critical step. Unless the objective is identified and defined there can be no concrete results since there wouldn’t be clarity to compare the final outcome to the expected result. It also helps understand the scope of the project.

Preparing the data

This is more to do with data mining. Historic data used for training the model is scattered across multiple platforms and sources. To compound the problem, data can be unstructured with possible duplicate accounts and missing values! Data quality determines the quality of the model, and thus it becomes imperative that data is healthy and relevant.

Data Sampling

Once mined, Data is essentially split into 2 parts. One set is for training that is used to build the model and the second is the ‘test’ set that is used to verify the accuracy of the final output. This also helps identify and filter the noise component.

Model Building

Sampling cam equally result in a single algorithm or parallel & connected algorithms. In such a case the data goes through multiple testing and a decision is based on the final output.

Execution

Once a model gets finalised, the other teams in the organization need to be involved to build a deployable model and understand its impact on the overall business.

The possibilities with Data mining & Predictive analysis are huge. It also gives a huge room for learning and experimenting. There are several tools available in the industry to aid through all the steps of data mining and predictive analysis. The combination of human expertise and intellect along with the help of the available tools and the overall cooperation within the multiple channels within the organization essentially ensures a stronger grip on the ability to build a solid predictive model.

When used together, predictive analytics and data mining help marketing professionals anticipate and get ready for customer needs, rather than just reacting to them.

Source: https://www.promptcloud.com/blog/data-mining-and-predictive-analysis/

rvest: easy web scraping with R

rvest is new package that makes it easy to scrape (or harvest) data from html web pages, inspired by libraries like beautiful soup. It is designed to work with magrittr so that you can express complex operations as elegant pipelines composed of simple, easily understood pieces. Install it with:

install.packages("rvest")

rvest in action

To see rvest in action, imagine we’d like to scrape some information about The Lego Movie from IMDB. We start by downloading and parsing the file with html():

library(rvest)

lego_movie <- html("http://www.imdb.com/title/tt1490017/")

To extract the rating, we start with selectorgadget to figure out which css selector matches the data we want: strong span. (If you haven’t heard of selectorgadget, make sure to read vignette("selectorgadget") – it’s the easiest way to determine which selector extracts the data that you’re interested in.) We use html_node() to find the first node that matches that selector, extract its contents with html_text(), and convert it to numeric with as.numeric():

lego_movie %>%

  html_node("strong span") %>%

  html_text() %>%

  as.numeric()

#> [1] 7.9

We use a similar process to extract the cast, using html_nodes() to find all nodes that match the selector:

lego_movie %>%

  html_nodes("#titleCast .itemprop span") %>%

  html_text()

#>  [1] "Will Arnett"     "Elizabeth Banks" "Craig Berry"   

#>  [4] "Alison Brie"     "David Burrows"   "Anthony Daniels"

#>  [7] "Charlie Day"     "Amanda Farinos"  "Keith Ferguson"

#> [10] "Will Ferrell"    "Will Forte"      "Dave Franco"   

#> [13] "Morgan Freeman"  "Todd Hansen"     "Jonah Hill"

The titles and authors of recent message board postings are stored in a the third table on the page. We can use html_node() and [[ to find it, then coerce it to a data frame with html_table():

lego_movie %>%

  html_nodes("table") %>%

  .[[3]] %>%

  html_table()

#>                                              X 1            NA

#> 1 this movie is very very deep and philosophical   mrdoctor524

#> 2 This got an 8.0 and Wizard of Oz got an 8.1...  marr-justinm

#> 3                         Discouraging Building?       Laestig

#> 4                              LEGO - the plural      neil-476

#> 5                                 Academy Awards   browncoatjw

#> 6                    what was the funniest part? actionjacksin

Other important functions

•    If you prefer, you can use xpath selectors instead of css: html_nodes(doc, xpath = "//table//td")).

•    Extract the tag names with html_tag(), text with html_text(), a single attribute with html_attr() or all attributes with html_attrs().

•    Detect and repair text encoding problems with guess_encoding() and repair_encoding().

•    Navigate around a website as if you’re in a browser with html_session(), jump_to(), follow_link(), back(), and forward(). Extract, modify and submit forms with html_form(), set_values() and submit_form(). (This is still a work in progress, so I’d love your feedback.)

To see these functions in action, check out package demos with demo(package = "rvest").

Source: http://blog.rstudio.org/2014/11/24/rvest-easy-web-scraping-with-r/