Data Mining

Data Mining

Data mining is the retrieving of hidden information from data using algorithms. Data mining helps to extract useful information from great masses of data, which can be used for making practical interpretations for business decision-making. It is basically a technical and mathematical process that involves the use of software and specially designed programs. Data mining is thus also known as Knowledge Discovery in Databases (KDD) since it involves searching for implicit information in large databases. The main kinds of data mining software are: clustering and segmentation software, statistical analysis software, text analysis, mining and information retrieval software and visualization software.

Data mining is gaining a lot of importance because of its vast applicability. It is being used increasingly in business applications for understanding and then predicting valuable information, like customer buying behavior and buying trends, profiles of customers, industry analysis, etc. It is basically an extension of some statistical methods like regression. However, the use of some advanced technologies makes it a decision making tool as well. Some advanced data mining tools can perform database integration, automated model scoring, exporting models to other applications, business templates, incorporating financial information, computing target columns, and more.

Some of the main applications of data mining are in direct marketing, e-commerce, customer relationship management, healthcare, the oil and gas industry, scientific tests, genetics, telecommunications, financial services and utilities. The different kinds of data are: text mining, web mining, social networks data mining, relational databases, pictorial data mining, audio data mining and video data mining.

Some of the most popular data mining tools are: decision trees, information gain, probability, probability density functions, Gaussians, maximum likelihood estimation, Gaussian Baves classification, cross-validation, neural networks, instance-based learning /case-based/ memory-based/non-parametric, regression algorithms, Bayesian networks, Gaussian mixture models, K-Means and hierarchical clustering, Markov models, support vector machines, game tree search and alpha-beta search algorithms, game theory, artificial intelligence, A-star heuristic search, HillClimbing, simulated annealing and genetic algorithms.

Some popular data mining software includes: Connexor Machines, Copernic Summarizer, Corpora, DocMINER, DolphinSearch, dtSearch, DS Dataset, Enkata, Entrieva, Files Search Assistant, FreeText Software Technologies, Intellexer, Insightful InFact, Inxight, ISYS:desktop, Klarity (part of Intology tools), Leximancer, Lextek Onix Toolkit, Lextek Profiling Engine, Megaputer Text Analyst, Monarch, Recommind MindServer, SAS Text Miner, SPSS LexiQuest, SPSS Text Mining for Clementine, Temis-Group, TeSSI®, Textalyser, TextPipe Pro, TextQuest, Readware, Quenza, VantagePoint, VisualText(TM), by TextAI, Wordstat. There is also free software and shareware such as INTEXT, S-EM (Spy-EM), and Vivisimo/Clusty.

Source : http://ezinearticles.com/?Data-Mining&id=196652

Data Mining - Retrieving Information From Data

Data Mining - Retrieving Information From Data

Data mining definition is the process of retrieving information from data. It has become very important now days because data that is processed is usually kept for future reference and mainly for security purposes in a company. Data transforms is processed into information and it is mostly used in different ways depending on what information one is extracting and from where the person is extracting the information.

It is commonly used in marketing, scientific information and research work, fraud detection and surveillance and many more and most of this work is done using a computer. This definition can come in different terms data snooping, data fishing and data dredging all this refer to data mining but it depends in which department one is. One must know data mining definition so that he can be in a position to make data.

The method of data mining has been there for so many centuries and it is used up to date. There were early methods which were used to identify data mining there are mainly two: regression analysis and bayes theorem. These methods are never used now days because a lot of people have advanced and technology has really changed the entire system.

With the coming up or with the introduction of computers and technology, it becomes very fast and easy to save information. Computers have made work easier and one can be able to expand more knowledge about data crawling and learn on how data is stored and processed through computer science.

Computer science is a course that sharpens one skill and expands more about data crawling and the definition of what data mining means. By studying computer science one can be in a position to know: clustering, support vector machines and decision trees there are some of the units that are found on computer science.

It's all about all this and this knowledge must be applied here. Government institutions, small scale business and supermarkets use data.

The main reason most companies use data mining is because data assist in the collection of information and observations that a company goes through in their daily activity. Such information is very vital in any companies profile and needs to be checked and updated for future reference just in case something happens.

Businesses which use data crawling focus mainly on return of investments, and they are able to know whether they are making a profit or a loss within a very short period. If the company or the business is making a profit they can be in a position to give customers an offer on the product in which they are selling so that the business can be a position to make more profit in an organization, this is very vital in human resource departments it helps in identifying the character traits of a person in terms of job performance.

Most people who use this method believe that is ethically neutral. The way it is being used nowadays raises a lot of questions about security and privacy of its members. Data mining needs good data preparation which can be in a position to uncover different types of information especially those that require privacy.

A very common way in this occurs is through data aggregation.

Data aggregation is when information is retrieved from different sources and is usually put together so that one can be in a position to be analyze one by one and this helps information to be very secure. So if one is collecting data it is vital for one to know the following:

    How will one use the data that he is collecting?
    Who will mine the data and use the data.
    Is the data very secure when am out can someone come and access it.
    How can one update the data when information is needed
    If the computer crashes do I have any backup somewhere.

It is important for one to be very careful with documents which deal with company's personal information so that information cannot easily be manipulated.

source : http://ezinearticles.com/?Data-Mining---Retrieving-Information-From-Data&id=5054887

One of the Main Differences Between Statistical Analysis and Data Mining

One of the Main Differences Between Statistical Analysis and Data Mining

Two methods of analyzing data that are common in both academic and commercial fields are statistical analysis and data mining. While statistical analysis has a long scientific history, data mining is a more recent method of data analysis that has arisen from Computer Science. In this article I want to give an introduction to these methods and outline what I believe is one of the main differences between the two fields of analysis.

Statistical analysis commonly involves an analyst formulating a hypothesis and then testing the validity of this hypothesis by running statistical tests on data that may have been collected for the purpose. For example, if an analyst was studying the relationship between income level and the ability to get a loan, the analyst may hypothesis that there will be a correlation between income level and the amount of credit someone may qualify for.

The analyst could then test this hypothesis with the use of a data set that contains a number of people along with their income levels and the credit available to them. A test could be run that indicates for example that there may be a high degree of confidence that there is indeed a correlation between income and available credit. The main point here is that the analyst has formulated a hypothesis and then used a statistical test along with a data set to provide evidence in support or against that hypothesis.

Data mining is another area of data analysis that has arisen more recently from computer science that has a number of differences to traditional statistical analysis. Firstly, many data mining techniques are designed to be applied to very large data sets, while statistical analysis techniques are often designed to form evidence in support or against a hypothesis from a more limited set of data.

Probably the mist significant difference here, however, is that data mining techniques are not used so much to form confidence in a hypothesis, but rather extract unknown relationships may be present in the data set. This is probably best illustrated with an example. Rather than in the above case where a statistician may form a hypothesis between income levels and an applicants ability to get a loan, in data mining, there is not typically an initial hypothesis. A data mining analyst may have a large data set on loans that have been given to people along with demographic information of these people such as their income level, their age, any existing debts they have and if they have ever defaulted on a loan before.

A data mining technique may then search through this large data set and extract a previously unknown relationship between income levels, peoples existing debt and their ability to get a loan.

While there are quite a few differences between statistical analysis and data mining, I believe this difference is at the heart of the issue. A lot of statistical analysis is about analyzing data to either form confidence for or against a stated hypothesis while data mining is often more about applying an algorithm to a data set to extract previously unforeseen relationships.

Source:http://ezinearticles.com/?One-of-the-Main-Differences-Between-Statistical-Analysis-and-Data-Mining&id=4578250

Data Extraction Services For Better Outputs in Your Business

Data Extraction Services For Better Outputs in Your Business

Data Extraction can be defined as the process of retrieving data from an unstructured source in order to process it further or store it. It is very useful for large organizations who deal with large amount of data on a daily basis that need to be processed into meaningful information and stored for later use. The data extraction is a systematic way to extract and structure data from scattered and semi-structured electronic documents, as found on the web and in various data warehouses.

In today's highly competitive business world, vital business information such as customer statistics, competitor's operational figures and inter-company sales figures play an important role in making strategic decisions. By signing on this service provider, you will be get access to critivcal data from various sources like websites, databases, images and documents.

It can help you take strategic business decisions that can shape your business' goals. Whether you need customer information, nuggets into your competitor's operations and figure out your organization's performance, it is highly critical to have data at your fingertips as and when you want it. Your company may be crippled with tons of data and it may prove a headache to control and convert the data into useful information. Data extraction services enable you get data quickly and in the right format.

Few areas where Data Extraction can help you are:

    Capturing financial data
    Generating better sales leads
    Conducting market research, survey and analysis
    Conducting product research and analysis
    Track, extract and harvest product pricing data
    Searching for specific job postings
    Duplicating an online database
    Acquiring real estate data
    Processing auction information
    Searching online newspapers for latest pricing information
    Extracting and summarize news stories from online news sources

Outsourcing companies provide custom made data extraction services to the client's requirements. The different types of data extraction services;

    Web extraction
    Database extraction

Outsourcing is the beneficial option for large organizations seeking to manage large information. Outsourcing this services helps businesses in managing their data effectively, which in turn enables business to experience an increase in profits. By outsourcing, you can certainly increase your competitive edge and save costs too!

This article is courtesy of Web Scraping Expert - an executive at Outsourcing Web Research offer high quality and time bound comprehensive range of data extraction services at affordable rates. For more info please visit us at: http://www.webscrapingexpert.com/ or directly send your requirements at: info@webscrapingexpert.com

Source:http://ezinearticles.com/?Data-Extraction-Services-For-Better-Outputs-in-Your-Business&id=2760257

Data Mining vs Screen-Scraping

Data Mining vs Screen-Scraping

Data mining isn't screen-scraping. I know that some people in the room may disagree with that statement, but they're actually two almost completely different concepts.

In a nutshell, you might state it this way: screen-scraping allows you to get information, where data mining allows you to analyze information. That's a pretty big simplification, so I'll elaborate a bit.

The term "screen-scraping" comes from the old mainframe terminal days where people worked on computers with green and black screens containing only text. Screen-scraping was used to extract characters from the screens so that they could be analyzed. Fast-forwarding to the web world of today, screen-scraping now most commonly refers to extracting information from web sites. That is, computer programs can "crawl" or "spider" through web sites, pulling out data. People often do this to build things like comparison shopping engines, archive web pages, or simply download text to a spreadsheet so that it can be filtered and analyzed.

Data mining, on the other hand, is defined by Wikipedia as the "practice of automatically searching large stores of data for patterns." In other words, you already have the data, and you're now analyzing it to learn useful things about it. Data mining often involves lots of complex algorithms based on statistical methods. It has nothing to do with how you got the data in the first place. In data mining you only care about analyzing what's already there.

The difficulty is that people who don't know the term "screen-scraping" will try Googling for anything that resembles it. We include a number of these terms on our web site to help such folks; for example, we created pages entitled Text Data Mining, Automated Data Collection, Web Site Data Extraction, and even Web Site Ripper (I suppose "scraping" is sort of like "ripping"). So it presents a bit of a problem-we don't necessarily want to perpetuate a misconception (i.e., screen-scraping = data mining), but we also have to use terminology that people will actually use.

Source: http://ezinearticles.com/?Data-Mining-vs-Screen-Scraping&id=146813

Collecting Data With Web Scrapers

Collecting Data With Web Scrapers

There is a large amount of data available only through websites. However, as many people have found out, trying to copy data into a usable database or spreadsheet directly out of a website can be a tiring process. Data entry from internet sources can quickly become cost prohibitive as the required hours add up. Clearly, an automated method for collating information from HTML-based sites can offer huge management cost savings.

Web scrapers are programs that are able to aggregate information from the internet. They are capable of navigating the web, assessing the contents of a site, and then pulling data points and placing them into a structured, working database or spreadsheet. Many companies and services will use programs to web scrape, such as comparing prices, performing online research, or tracking changes to online content.

Let's take a look at how web scrapers can aid data collection and management for a variety of purposes.

Improving On Manual Entry Methods

Using a computer's copy and paste function or simply typing text from a site is extremely inefficient and costly. Web scrapers are able to navigate through a series of websites, make decisions on what is important data, and then copy the info into a structured database, spreadsheet, or other program. Software packages include the ability to record macros by having a user perform a routine once and then have the computer remember and automate those actions. Every user can effectively act as their own programmer to expand the capabilities to process websites. These applications can also interface with databases in order to automatically manage information as it is pulled from a website.

Aggregating Information

There are a number of instances where material stored in websites can be manipulated and stored. For example, a clothing company that is looking to bring their line of apparel to retailers can go online for the contact information of retailers in their area and then present that information to sales personnel to generate leads. Many businesses can perform market research on prices and product availability by analyzing online catalogues.

Data Management

Managing figures and numbers is best done through spreadsheets and databases; however, information on a website formatted with HTML is not readily accessible for such purposes. While websites are excellent for displaying facts and figures, they fall short when they need to be analyzed, sorted, or otherwise manipulated. Ultimately, web scrapers are able to take the output that is intended for display to a person and change it to numbers that can be used by a computer. Furthermore, by automating this process with software applications and macros, entry costs are severely reduced.

This type of data management is also effective at merging different information sources. If a company were to purchase research or statistical information, it could be scraped in order to format the information into a database. This is also highly effective at taking a legacy system's contents and incorporating them into today's systems.

Overall, a web scraper is a cost effective user tool for data manipulation and management.

source: http://ezinearticles.com/?Collecting-Data-With-Web-Scrapers&id=4223877

Making data on the web useful: scraping

Making data on the web useful: scraping

Introduction

Many times data is not easily accessible – although it does exist. As much as we wish everything was available in CSV or the format of our choice – most data is published in different forms on the web. What if you want to use the data to combine it with other datasets and explore it independently?

Scraping to the rescue!

Scraping describes the method to extract data hidden in documents – such as Web Pages and PDFs and make it useable for further processing. It is among the most useful skills if you set out to investigate data – and most of the time it’s not especially challenging. For the most simple ways of scraping you don’t even need to know how to write code.

This example relies heavily on Google Chrome for the first part. Some things work well with other browsers, however we will be using one specific browser extension only available on Chrome. If you can’t install Chrome, don’t worry the principles remain similar.
Code-free Scraping in 5 minutes using Google Spreadsheets & Google Chrome

Knowing the structure of a website is the first step towards extracting and using the data. Let’s get our data into a spreadsheet – so we can use it further. An easy way to do this is provided by a special formula in Google Spreadsheets.

Save yourselves hours of time in copy-paste agony with the ImportHTML command in Google Spreadsheets. It really is magic!
Recipes

In order to complete the next challenge, take a look in the Handbook at one of the following recipes:

    Extracting data from HTML tables.
    Scraping using the Scraper Extension for Chrome

http://i1.wp.com/farm9.staticflickr.com/8303/7850933084_b188c02992_o_d.jpg

Both methods are useful for:

    Extracting individual lists or tables from single webpages

The latter can do slightly more complex tasks, such as extracting nested information. Take a look at the recipe for more details.

Neither will work for:

    Extracting data spread across multiple webpages

Challenge

Task: Find a website with a table and scrape the information from it. Share your result on datahub.io (make sure to tag your dataset with schoolofdata.org)
Tip

Once you’ve got your table into the spreadsheet, you may want to move it around, or put it in another sheet. Right click the top left cell and select “paste special” – “paste values only”.
Scraping more than one webpage: Scraperwiki

Note: Before proceeding into full scraping mode, it’s helpful to understand the flesh and bones of what makes up a webpage. Read the Introduction to HTML recipe in the handbook.

Until now we’ve only scraped data from a single webpage. What if there are more? Or you want to scrape complex databases? You’ll need to learn how to program – at least a bit.

It’s beyond the scope of this course to teach how to scrape, our aim here is to help you understand whether it is worth investing your time to learn, and to point you at some useful resources to help you on your way!
Structure of a scraper

Scrapers are comprised of three core parts:

    A queue of pages to scrape
    An area for structured data to be stored, such as a database
    A downloader and parser that adds URLs to the queue and/or structured information to the database.

Fortunately for you there is a good website for programming scrapers: ScraperWiki.com

http://i1.wp.com/farm9.staticflickr.com/8112/8660176200_2dd5aa8d0b_z.jpg

ScraperWiki has two main functions: You can write scrapers – which are optionally run regularly and the data is available to everyone visiting – or you can request them to write scrapers for you. The latter costs some money – however it helps to contact the Scraperwiki community (Google Group) someone might get excited about your project and help you!.

If you are interested in writing scrapers with Scraperwiki, check out this sample scraper – scraping some data about Parliament. Click View source to see the details. Also check out the Scraperwiki documentation: https://scraperwiki.com/docs/python/
When should I make the investment to learn how to scrape?

A few reasons (non-exhaustive list!):

    If you regularly have to extract data where there are numerous tables in one page.
    If your information is spread across numerous pages.
    If you want to run the scraper regularly (e.g. if information is released every week or month).
    If you want things like email alerts if information on a particular webpage changes.

…And you don’t want to pay someone else to do it for you!
Summary:

In this course we’ve covered Web scraping and how to extract data from websites. The main function of scraping is to convert data that is semi-structured into structured data and make it easily useable for further processing. While this is a relatively simple task with a bit of programming – for single webpages it is also feasible without any programming at all. We’ve introduced =importHTML and the Scraper extension for your scraping needs.
Further Reading

    Scraping for Journalism: A Guide for Collecting Data: ProPublica Guides
    Scraping for Journalists (ebook): Paul Bradshaw
    Scrape the Web: Strategies for programming websites that don’t expect it : Talk from PyCon
    An Introduction to Compassionate Screen Scraping: Will Larson

Source: http://schoolofdata.org/handbook/courses/scraping/

Scrape amazon and price your product the right way – A use case

Scrape amazon and price your product the right way – A use case

So you built a product that you want to sell through Amazon.

How do you price your product? 

Amazon is the world’s largest online retailer. Millions of products are sold through amazon.  a lot of people make their living selling through Amazon. One of the biggest mistake people do in Amazon is that they price their product the wrong way. Sometimes they sell overpriced products, sometimes they sell the underpriced product. Both situations are toxic for the business.

We recently worked with a company that helps small businesses sell the products efficiently through amazon and other marketplaces. One of the key things they are doing is helping people with pricing their product the right way.

What I learned from them is that price is a relative term and a lot of people does not understand it. Pricing is a function of the positioning of  your product in the market.

We need to collect the data using  a technique called web scraping to understand how to position the product. You can get the  data in a CSV file which can be used for analysis.

1) What is the average price of a comparable product?

Understanding the pricing  strategy of your competitors products  is the first step in solving the problem. This can give you a range in which you can price your product. You can get the pricing data by scraping amazon

2) Is this a premium product?

People always pay a premium price for a premium product. What makes a product premium? – A product is considered premium only when the customer believe it is worth the price. Excellent marketing and branding are the ways to position your product as a premium product. You can get the relevant data by scraping amazon.

3) What are the problems with your competitor products?

Your competitor products might be having some defects. Or they might not be addressing a relevant problem. You have every chance of success If you are solving a problem that your competitor doesn’t. You can find these problems by analyzing the product reviews of your competitors. You can get review data by scraping amazon.

By analyzing data you can reach at a point where your profit margin looks healthy and pricing looks sensible. Buyers buy the value, not your product. Differentiate your product and position it as a superior product. Give people a reason to buy and that is the only way to succeed.

Source: http://blog.datahut.co/scrape-amazon-and-price-your-product-the-right-way-a-use-case/

Scrape amazon and price your product the right way – A use case

Scrape amazon and price your product the right way – A use case

So you built a product that you want to sell through Amazon.

How do you price your product? 

Amazon is the world’s largest online retailer. Millions of products are sold through amazon.  a lot of people make their living selling through Amazon. One of the biggest mistake people do in Amazon is that they price their product the wrong way. Sometimes they sell overpriced products, sometimes they sell the underpriced product. Both situations are toxic for the business.

We recently worked with a company that helps small businesses sell the products efficiently through amazon and other marketplaces. One of the key things they are doing is helping people with pricing their product the right way.

What I learned from them is that price is a relative term and a lot of people does not understand it. Pricing is a function of the positioning of  your product in the market.

We need to collect the data using  a technique called web scraping to understand how to position the product. You can get the  data in a CSV file which can be used for analysis.

1) What is the average price of a comparable product?

Understanding the pricing  strategy of your competitors products  is the first step in solving the problem. This can give you a range in which you can price your product. You can get the pricing data by scraping amazon

2) Is this a premium product?

People always pay a premium price for a premium product. What makes a product premium? – A product is considered premium only when the customer believe it is worth the price. Excellent marketing and branding are the ways to position your product as a premium product. You can get the relevant data by scraping amazon.

3) What are the problems with your competitor products?

Your competitor products might be having some defects. Or they might not be addressing a relevant problem. You have every chance of success If you are solving a problem that your competitor doesn’t. You can find these problems by analyzing the product reviews of your competitors. You can get review data by scraping amazon.

By analyzing data you can reach at a point where your profit margin looks healthy and pricing looks sensible. Buyers buy the value, not your product. Differentiate your product and position it as a superior product. Give people a reason to buy and that is the only way to succeed.

Source: http://blog.datahut.co/scrape-amazon-and-price-your-product-the-right-way-a-use-case/

Tapping The Mining Services Goldmine

Tapping The Mining Services Goldmine

In Australia, resources booms tend to come and go. In a recent speech, Reserve Bank Deputy Governor Ric Battellino identified five major booms over the last two hundred years - from the gold rush of the 1850s, to our current minerals and energy boom.

Many have argued that the current boom is different from anything we've experienced before, with the modernisation of the Chinese and Indian economies likely to keep demand high for decades. That's led some analysts to talk of a resources supercycle. And yet a supercycle is still a cycle.

By definition, cycles are uneven, with commodity prices ebbing and flowing in response to demand, economic conditions and market sentiment. And the share prices of resources companies tend to move with them.

Which raises the question: what's the best way for investors to tap into the potential of the mining boom, without the heart-stopping volatility that mining stocks sometimes deliver?
Invest in the store that sells the spade

Legend has it that the people who really profited from Australia's gold rush weren't the miners who flocked to the fields, but the store-owners who sold them their spades and pans. You can put the same principle to work today by investing in mining services and engineering companies.

Here are five reasons to consider giving mining services companies a place in your portfolio:

1. Growing demand

In November, the Australian Bureau of Agricultural and Resource Economics reported that mining and energy companies plan to invest a record $132.9bn in new projects, a 58% increase from the previous year. That includes 72 projects at an advanced stage of development, such as the $43bn Gorgon LNG project and the $20bn Olympic dam expansion. The mining services sector is poised to benefit from all of them.

The sector also stands to benefit from Australia's worsening skills shortage, with more companies looking to contractors to provide essential services in remote locations.

2. Less volatility

Resource stocks tend to fluctuate with commodity prices, which are subject to international economic forces and market sentiment beyond the control of any individual company. As a result, they are among the most volatile companies on the Australian sharemarket. But mining services stocks, while still exposed to the commodities cycle, tend to be more stable.

3. More predictable cash flow

One reason for the comparative volatility of commodity companies is that their cash flow can be very variable. In the development phase, they need to make significant capital expenditure, often leading to negative cash flows. And while they enjoy healthy revenues in the production phase, that revenue may diminish as a resource is exhausted, unless they make further investments in exploration and development.
In contrast, mining services companies require comparatively little capital investment, with more predictable cash flows over the long-term.

4. Higher dividends

Predictable cash flows and lower capital expenditures often allow services companies to pay out more of their earnings as dividends, making them more appealing for income-oriented investors.

5. No need to pick winners

Many miners are highly leveraged to demand for a single commodity, whether it's gold, coal, copper or iron ore. Some are reliant on a single mine or field. Whereas services companies generally have a more diversified customer base.

Source: http://ezinearticles.com/?Tapping-The-Mining-Services-Goldmine&id=5924837

How Web Scraping Affects your Revenue Growth

How Web Scraping Affects your Revenue Growth

Web scraping is an indispensable resource when it comes to gaining an edge in the competition with the help of business intelligence. As more and more data gets created on the world wide web, the complexity of extracting it intensifies. Web scraping is a technology that demands an extensive tech stack, high end resources and technically skilled labour. Given this resource hungry nature, many businesses prefer outsourcing it to doing the scraping in-house. Here is a brief walk-through of web scraping so that you can get a grip on the whole process and understand how it could affect your revenue growth as a business.

Business intelligence

The competition among online businesses is at its peak. This has more to do with the ready availability of insightful data. When data acquisition at this scale wasn’t possible in the past, businesses made hit-or-miss decisions upon instincts. Now that every activity can be recorded, extracted as data and analysed to arrive at the best business decisions, companies are making the most of it to boost their revenue. This includes monitoring the activity of competitors on social media, price intelligence, sentiment analysis, gathering data for market research and much more. The use cases of web scraping in business is almost infinite. Business intelligence is extremely helpful for the survival of companies in a market that fluctuates often. Implementing a business intelligence strategy powered by web scraping can definitely give a boost to your revenue growth.
Cost centres involved in in-house Web Scraping

Web scraping, despite being a robust solution for extracting data from the web, is not going to be an easy path if your company is not technically rich already. It involves setting up resources like a tech stack and servers that can run the web crawler by a technically skilled team. Following are the primary cost centres involved in the web scraping process.

1. High end servers

Web scraping is a resource intensive process. Considering the importance of uptime here, the crawlers cannot be run on average performance machines. To have the optimum uptime and avoid crashes, the crawler has to be run on high performing servers located in different parts of the world. The quality of servers is crucial to the consistency of the process. Not to mention, these high end servers makeup for a significant amount of the cost involved in web scraping.

2. Technically skilled labour

Scanning through the source code to identify appropriate tags that hold the required data points and creating a program that can automatically fetch these data points from similar pages’ at large scale requires deep programming skills. It goes without saying that employing skilled people would incur cost that could take a hit on your revenue. Ideally, you will need a team of at least 10 to run a web scraping setup in-house.       

3. An extensive tech stack

Although most of the software being used for web scraping are open source, you will find yourself investing in paid software to make certain things easier or faster. Dealing with open source software might not be as user friendly as the paid ones. In any case, having a tech stack with a lot of options is a necessary aspect of web scraping that would incur additional cost.   

4. Maintenance

Building and running the web scraping setup is only half of the story. Since websites undergo changes often, there is a possibility of the crawler setup breaking from time to time. To avoid or solve this at the earliest, a monitoring system that involves both machines and humans is necessary. Monitoring and maintenance contribute to a considerable cost in the web scraping process.
Data as a service

If data for business is your requirement, a better way to acquire it would be to depend on a company that can deliver it via the data as a service route. Web scraping companies have already set up high-end resources required to run the web crawlers that you can utilize to avail web scraping at a much lower cost than what you would incur by doing it on your own. With this, you can also save yourself from the complications and maintenance headache associated with web scraping. Moreover, with a web scraping service, you can enjoy a much higher return on investment owing to the lowered cost of data acquisition. You can use our ROI calculator to compare between the cost of going with an in-house web scraping setup and a hosted solution.

Source: https://www.promptcloud.com/blog/web-scraping-affects-revenue-growth

Scraping Yelp Business Data With Python Scraping Script

Scraping Yelp Business Data With Python Scraping Script

Yelp is a great source of business contact information with details like address, postal code, contact information; website addresses etc. that other site like Google Maps just does not. Yelp also provides reviews about the particular business. The yelp business database can be useful for telemarketing, email marketing and lead generation.

Are you looking for yelp business details database? Are you looking for scraping data from yelp website/business directory? Are you looking for yelp screen scraping software? Are you looking for scraping the business contact information from the online Yelp? Then you are at the right place.

Here I am going to discuss how to scrape yelp data for lead generation and email marketing. I have made a simple and straight forward yelp data scraping script in python that can scrape data from yelp website. You can use this yelp scraper script absolutely free.

I have used urllib, BeautifulSoup packages. Urllib package to make http request and parsed the HTML using BeautifulSoup, used Threads to make the scraping faster.
Yelp Scraping Python Script

import urllib
from bs4 import BeautifulSoup
import re
from threading import Thread

#List of yelp urls to scrape
url=['http://www.yelp.com/biz/liman-fisch-restaurant-hamburg','http://www.yelp.com/biz/casa-franco-caramba-hamburg','http://www.yelp.com/biz/o-ren-ishii-hamburg','http://www.yelp.com/biz/gastwerk-hotel-hamburg-hamburg-2','http://www.yelp.com/biz/superbude-hamburg-2','http://www.yelp.com/biz/hotel-hafen-hamburg-hamburg','http://www.yelp.com/biz/hamburg-marriott-hotel-hamburg','http://www.yelp.com/biz/yoho-hamburg']

i=0
#function that will do actual scraping job
def scrape(ur):

          html = urllib.urlopen(ur).read()
          soup = BeautifulSoup(html)

      title = soup.find('h1',itemprop="name")
          saddress = soup.find('span',itemprop="streetAddress")
          postalcode = soup.find('span',itemprop="postalCode")
          print title.text
          print saddress.text
          print postalcode.text
          print "-------------------"

threadlist = []

#making threads
while i<len(url):
          t = Thread(target=scrape,args=(url[i],))
          t.start()
          threadlist.append(t)
          i=i+1

for b in threadlist:
          b.join()

Recently I had worked for one German company and did yelp scraping project for them and delivered data as per their requirement. If you looking for scraping data from business directories like yelp then send me your requirement and I will get back to you with sample.

Source: http://webdata-scraping.com/scraping-yelp-business-data-python-scraping-script/

Web Scraping – A trending technique in data science!!!

Web Scraping – A trending technique in data science!!!

Web scraping as a market segment is trending to be an emerging technique in data science to become an integral part of many businesses – sometimes whole companies are formed based on web scraping. Web scraping and extraction of relevant data gives businesses an insight into market trends, competition, potential customers, business performance etc.  Now question is that “what is actually web scraping and where is it used???” Let us explore web scraping, web data extraction, web mining/data mining or screen scraping in details.

What is Web Scraping?

Web Data Scraping is a great technique of extracting unstructured data from the websites and transforming that data into structured data that can be stored and analyzed in a database. Web Scraping is also known as web data extraction, web data scraping, web harvesting or screen scraping.

What you can see on the web that can be extracted. Extracting targeted information from websites assists you to take effective decisions in your business.

Web scraping is a form of data mining. The overall goal of the web scraping process is to extract information from a websites and transform it into an understandable structure like spreadsheets, database or csv. Data like item pricing, stock pricing, different reports, market pricing, product details, business leads can be gathered via web scraping efforts.

There are countless uses and potential scenarios, either business oriented or non-profit. Public institutions, companies and organizations, entrepreneurs, professionals etc. generate an enormous amount of information/data every day.

Uses of Web Scraping:

The following are some of the uses of web scraping:

•     Collect data from real estate listing
•     Collecting retailer sites data on daily basis
•     Extracting offers and discounts from a website.
•     Scraping job posting.
•     Price monitoring with competitors.
•     Gathering leads from online business directories – directory scraping
•     Keywords research
•     Gathering targeted emails for email marketing – email scraping
•     And many more.

There are various techniques used for data gathering as listed below:

•     Human copy-and-paste – takes lot of time to finish when data is huge
•     Programming the Custom Web Scraper as per the needs.
•     Using Web Scraping Softwares available in market.

Are you in search of web data scraping expert or specialist. Then you are at right place. We are the team of web scraping experts who could easily extract data from website and further structure the unstructured useful data to uncover patterns, and help businesses for decision making that helps in increasing sales, cover a wide customer base and ultimately it leads to business towards growth and success.

We have got expertise in all the web scraping techniques, scraping data from ajax enabled complex websites, bypassing CAPTCHAs, forming anonymous http request etc in providing web scraping services.

Source: http://webdata-scraping.com/web-scraping-trending-technique-in-data-science/

How to Use Microsoft Excel as a Web Scraping Tool

How to Use Microsoft Excel as a Web Scraping Tool

Microsoft Excel is undoubtedly one of the most powerful tools to manage information in a structured form. The immense popularity of Excel is not without reasons. It is like the Swiss army knife of data with its great features and capabilities. Here is how Excel can be used as a basic web scraping tool to extract web data directly into a worksheet. We will be using Excel web queries to make this happen.

Web queries is a feature of Excel which is basically used to fetch data on a web page into the Excel worksheet easily. It can automatically find tables on the webpage and would let you pick the particular table you need data from. Web queries can also be handy in situations where an ODBC connection is impossible to maintain apart from just extracting data from web pages. Let’s see how web queries work and how you can scrape HTML tables off the web using them.
Getting started

We’ll start with a simple Web query to scrape data from the Yahoo! Finance page. This page is particularly easier to scrape and hence is a good fit for learning the method. The page is also pretty straightforward and doesn’t have important information in the form of links or images. Here is the URL we will be using for the tutorial:

http://finance.yahoo.com/q/hp?s=GOOG

To create a new Web query:

1. Select the cell in which you want the data to appear.
2. Click on Data-> From Web
3. The New Web query box will pop up as shown below.

4. Enter the web page URL you need to extract data from in the Address bar and hit the Go button.
5. Click on the yellow-black buttons next to the table you need to extract data from.

6. After selecting the required tables, click on the Import button and you’re done. Excel will now start downloading the content of the selected tables into your worksheet.

Once you have the data scraped into your Excel worksheet, you can do a host of things like creating charts, sorting, formatting etc. to better understand or present the data in a simpler way.
Customizing the query

Once you have created a web query, you have the option to customize it according to your requirements. To do this, access Web query properties by right clicking on a cell with the extracted data. The page you were querying appears again, click on the Options button to the right of the address bar. A new pop up box will be displayed where you can customize how the web query interacts with the target page. The options here lets you change some of the basic things related to web pages like the formatting and redirections.

Apart from this, you can also alter the data range options by right clicking on a random cell with the query results and selecting Data range properties. The data range properties dialog box will pop up where you can make the required changes. You might want to rename the data range to something you can easily recognize like ‘Stock Prices’.

Auto refresh

Auto-refresh is a feature of web queries worth mentioning, and one which makes our Excel web scraper truly powerful. You can make the extracted data to be auto-refreshing so that your Excel worksheet will update the data whenever the source website changes. You can set how often you need the data to be updated from the source web page in data range options menu. The auto refresh feature can be enabled by ticking the box beside ‘Refresh every’ and setting your preferred time interval for updating the data.
Web scraping at scale

Although extracting data using Excel can be a great way to scrape html tables from the web, it is nowhere close to a real web scraping solution. This can prove to be useful if you are collecting data for your college research paper or you are a hobbyist looking for a cheap way to get your hands on some data. If data for business is your need, you will definitely have to depend on a web scraping provider with expertise in dealing with web scraping at scale. Outsourcing the complicated process that web scraping will also give you more room to deal with other things that need extra attention such as marketing your business.

Source: https://www.promptcloud.com/blog/how-to-use-excel-to-scrape-websites

Why Healthcare Companies should look towards Web Scraping

Why Healthcare Companies should look towards Web Scraping

The internet is a massive storehouse of information which is available in the form of text, media and other formats. To be competitive in this modern world, most businesses need access to this storehouse of information. But, all this information is not freely accessible as several websites do not allow you to save the data. This is where the process of Web Scraping comes in handy.

Web scraping is not new—it has been widely used by financial organizations, for detecting fraud; by marketers, for marketing and cross-selling; and by manufacturers for maintenance scheduling and quality control. Web scraping has endless uses for business and personal users. Every business or individual can have his or her own particular need for collecting data. You might want to access data belonging to a particular category from several websites. The different websites belonging to the particular category display information in non-uniform formats. Even if you are surfing a single website, you may not be able to access all the data at one place.

The data may be distributed across multiple pages under various heads. In a market that is vast and evolving rapidly, strategic decision-making demands accurate and thorough data to be analyzed, and on a periodic basis. The process of web scraping can help you mine data from several websites and store it in a single place so that it becomes convenient for you to a alyze the data and deliver results.

In the context of healthcare, web scraping is gaining foothold gradually but qualitatively. Several factors have led to the use of web scraping in healthcare. The voluminous amount of data produced by healthcare industry is too complex to be analyzed by traditional techniques. Web scraping along with data extraction can improve decision-making by determining trends and patterns in huge amounts of intricate data. Such intensive analyses are becoming progressively vital owing to financial pressures that have increased the need for healthcare organizations to arrive at conclusions based on the analysis of financial and clinical data. Furthermore, increasing cases of medical insurance fraud and abuse are encouraging healthcare insurers to resort to web scraping and data extraction techniques.

Healthcare is no longer a sector relying solely on person to person interaction. Healthcare has gone digital in its own way and different stakeholders of this industry such as doctors, nurses, patients and pharmacists are upping their ante technologically to remain in sync with the changing times. In the existing setup, where all choices are data-centric, web scraping in healthcare can impact lives, educate people, and create awareness. As people no more depend only on doctors and pharmacists, web scraping in healthcare can improve lives by offering rational solutions.

To be successful in the healthcare sector, it is important to come up with ways to gather and present information in innovative and informative ways to patients and customers. Web scraping offers a plethora of solutions for the healthcare industry. With web scraping and data extraction solutions, healthcare companies can monitor and gather information as well as track how their healthcare product is being received, used and implemented in different locales. It offers a safer and comprehensive access to data allowing healthcare experts to take the right decisions which ultimately lead to better clinical experience for the patients.

Web scraping not only gives healthcare professionals access to enterprise-wide information but also simplifies the process of data conversion for predictive analysis and reports. Analyzing user reviews in terms of precautions and symptoms for diseases that are incurable till date and are still undergoing medical research for effective treatments, can mitigate the fear in people. Data analysis can be based on data available with patients and is one way of creating awareness among people.

Hence, web scraping can increase the significance of data collection and help doctors make sense of the raw data. With web scraping and data extraction techniques, healthcare insurers can reduce the attempts of frauds, healthcare organizations can focus on better customer relationship management decisions, doctors can identify effective cure and best practices, and patients can get more affordable and better healthcare services.

Web scraping applications in healthcare can have remarkable utility and potential. However, the triumph of web scraping and data extraction techniques in healthcare sector depends on the accessibility to clean healthcare data. For this, it is imperative that the healthcare industry think about how data can be better recorded, stored, primed, and scraped. For instance, healthcare sector can consider standardizing clinical vocabulary and allow sharing of data across organizations to heighten the benefits from healthcare web scraping practices.

Healthcare sector is one of the top sectors where data is multiplying exponentially with time and requires a planned and structured storage of data. Continuous web scraping and data extraction is necessary to gain useful insights for renewing health insurance policies periodically as well as offer affordable and better public health solutions. Web scraping and data extraction together can process the mammoth mounds of healthcare data and transform it into information useful for decision making.

To reduce the gap between various components of healthcare sector-patients, doctors, pharmacies and hospitals, healthcare organizations and websites will have to tap the technology to collect data in all formats and present in a usable form. The healthcare sector needs to overcome the lag in implementing effective web scraping and data extraction techniques as well as intensify their pace of technology adoption. Web scraping can contribute enormously to the healthcare industry and facilitate organizations to methodically collect data and process it to identify inadequacies and best practices that improve patient care and reduce costs.

Source: https://www.promptcloud.com/blog/why-health-care-companies-should-use-web-scraping

How Web Data Extraction Services Will Save Your Time and Money by Automatic Data Collection

How Web Data Extraction Services Will Save Your Time and Money by Automatic Data Collection

Data scrape is the process of extracting data from web by using software program from proven website only. Extracted data any one can use for any purposes as per the desires in various industries as the web having every important data of the world. We provide best of the web data extracting software. We have the expertise and one of kind knowledge in web data extraction, image scrapping, screen scrapping, email extract services, data mining, web grabbing.

Who can use Data Scraping Services?

Data scraping and extraction services can be used by any organization, company, or any firm who would like to have a data from particular industry, data of targeted customer, particular company, or anything which is available on net like data of email id, website name, search term or anything which is available on web. Most of time a marketing company like to use data scraping and data extraction services to do marketing for a particular product in certain industry and to reach the targeted customer for example if X company like to contact a restaurant of California city, so our software can extract the data of restaurant of California city and a marketing company can use this data to market their restaurant kind of product. MLM and Network marketing company also use data extraction and data scrapping services to to find a new customer by extracting data of certain prospective customer and can contact customer by telephone, sending a postcard, email marketing, and this way they build their huge network and build large group for their own product and company.

We helped many companies to find particular data as per their need for example.

Web Data Extraction

Web pages are built using text-based mark-up languages (HTML and XHTML), and frequently contain a wealth of useful data in text form. However, most web pages are designed for human end-users and not for ease of automated use. Because of this, tool kits that scrape web content were created. A web scraper is an API to extract data from a web site. We help you to create a kind of API which helps you to scrape data as per your need. We provide quality and affordable web Data Extraction application

Data Collection

Normally, data transfer between programs is accomplished using info structures suited for automated processing by computers, not people. Such interchange formats and protocols are typically rigidly structured, well-documented, easily parsed, and keep ambiguity to a minimum. Very often, these transmissions are not human-readable at all. That's why the key element that distinguishes data scraping from regular parsing is that the output being scraped was intended for display to an end-user.

Email Extractor

A tool which helps you to extract the email ids from any reliable sources automatically that is called a email extractor. It basically services the function of collecting business contacts from various web pages, HTML files, text files or any other format without duplicates email ids.

Screen scrapping

Screen scraping referred to the practice of reading text information from a computer display terminal's screen and collecting visual data from a source, instead of parsing data as in web scraping.

Data Mining Services

Data Mining Services is the process of extracting patterns from information. Datamining is becoming an increasingly important tool to transform the data into information. Any format including MS excels, CSV, HTML and many such formats according to your requirements.

Web spider

A Web spider is a computer program that browses the World Wide Web in a methodical, automated manner or in an orderly fashion. Many sites, in particular search engines, use spidering as a means of providing up-to-date data.

Web Grabber

Web grabber is just a other name of the data scraping or data extraction.

Web Bot

Web Bot is software program that is claimed to be able to predict future events by tracking keywords entered on the Internet. Web bot software is the best program to pull out articles, blog, relevant website content and many such website related data We have worked with many clients for data extracting, data scrapping and data mining they are really happy with our services we provide very quality services and make your work data work very easy and automatic.

Source: http://ezinearticles.com/?How-Web-Data-Extraction-Services-Will-Save-Your-Time-and-Money-by-Automatic-Data-Collection&id=5159023

Web Scraping Best Practices

Web Scraping Best Practices

Extracting data from the World Wide Web has several challenges as more webmasters are working day and night to lower cases of scraping and crawling of their data in order to survive in the competitive world. There are various other problems you may face when web scraping and most of them can be avoided by adapting and implementing certain web scraping best practices as discussed in this article.

Have knowledge of the scraping tools

Acquiring adequate knowledge of hurdles that may be encountered during web scraping, you will be able to have a smooth web scraping experience and be on the safe side of the law. Conduct a thorough research on the types of tools you will use for scraping and crawling. Firsthand knowledge on these tools will help you find the data you need without being blocked.

Proper proxy software that acts as the middle party works well when you know how to work around HTTP and HTML protocols. Use tools that can change crawling patterns, URLs and data retrieved even when you are crawling on one domain. This will help you abide to the rules and regulations that come with web scraping activities and escaping any legal issues.
Conduct your scraping activities during off-peak hours

You may opt to extract data during times that less people have access for instance over the weekends, during late night hours, public holidays among others. Visiting a website on several instances to retrieve the same type of data is a waste of bandwidth. It is always advisable to download the entire site content to your computer and thereafter you can access it whenever need arises.
Hide your scrapping activities

There is a thin line between ethical and unethical crawling hence you should completely evade being on the top user list of a particular website. Cover up your track as best as you can by making use of proxy IPs to avoid any legal problems. You may also use multiple IP addresses or VPN services to conceal your scrapping activities and lower chances of landing on a website’s blacklist.

Website owners today are very protective of their data and any other information existing under their unique url. Be keen when going through the terms and conditions indicated by websites as they may consider crawling as an infringement of their privacy. Simple etiquette goes a long way. Your web scraping efforts will be fruitful if the site owner supports the idea of sharing data.
Keep record of your activities

Web scraping involves large amount of data.Due to this you may not always remember each and every piece of information you have acquired, gathering statistics will help you monitor your activities.
Load data in phases

Web scraping demands a lot of patience from you when using the crawlers to get needed information. Take the process in a slow manner by loading data one piece at a time. Several parallel request to the same domain can crush the entire site or retrace the scrapping attempts back to your local machine.

Loading data small bits will save you the hustle of scrapping afresh in case that your activity has been interrupted because you will have already stored part of the data required. You can reduce the loading data on an individual domain through various techniques such as caching pages that you have scrapped to escape redundancy occurrences. Use auto throttling mechanisms to increase the amount of traffic to the website and pause for breaks between requests to prevent getting banned.
Conclusion

Through these few mentioned web scraping best practices you will be able to work around website and gather the data required as per clients’ request without major hurdles along the way. The ultimate goal of every web scraper is to be able to access vital information and at the same time remain on the good side of the law.

Source: http://nocodewebscraping.com/web-scraping-best-practices/