Web scraping vs data mining
While these terms do share a lot of similarities, they are intrinsically contrasting. Web scraping refers to the extraction of data from websites. Generally, it can also involve formatting this data into a more comprehensible format, such as an Excel sheet. While most web scraping can be done manually, in most cases software tools are normally preferred due to their speed, accuracy, and convenience. The term web scraping can in most cases be used interchangeably with data harvesting, Collection is an agriculture term which means to gather ripe crops and store them from the fields which involve the act of collection and relocation. Thus data harvesting or web scraping can be described in simple words as the process of acquiring valuable or important data out of target websites and put them in your database in a structured format and form. Data mining is often misunderstood as a means to obtain the data. There are major differences between collecting the data and mining the data even though both of them involve the act of extraction and obtaining. Data mining is the process to discover fact-based patterns you generate from a large set of data. Rather than just getting the data and making sense of it, data mining is interdisciplinary, which integrates statistics, computer science, and machine learning.
Web scraping doesn’t involve the processing of any data. Data mining refers to the process of analyzing large datasets to uncover trends and valuable insights. It does not involve any data gathering or extraction. It may not always be web-based it can also be from other sources. When you access a web page, you can only view the data but cannot download it. Yes, you can manually copy and paste some of it but it is time-consuming and not viable. Web scraping automates this process and quickly extracts accurate and reliable data from web pages that you can use. You can scrape vast quantities of data and different kinds of data as well. It could be text, images, email ids, phone numbers, videos, etc.
Here’s how it works.
- Request-Response
The first simple step in any web scraping program is to request the intended website for the contents of a specific URL. In return, Remember, HTML is the file type used to display all the textual information on a webpage.
- Parse and Extract
In other terms, HTML is a markup language with a simplistic structure. When it comes to Parsing, it normally applies to any computer language. It is the process of using the code as a text and giving a structure in memory that the computer can comprehend and work with. HTML parsing is taking in HTML code and extracting important information like the title of the page, paragraphs in the page, headings in the page, links, bold text, etc.
- Download Data
The conclusion is to download and save the data in a CSV, JSON, or a database so that it can be retrieved and employed in any other program.
Process of data mining.
The data mining process is the discovery through large data sets of patterns, relationships, and insights that guide enterprises measuring and managing where they are and predicting where they will be in the future.
These are the essential steps of the data mining process.
- 1. Business understanding
In this phase:
Firstly, it is required to comprehend the business objectives clearly and find out what are the needs of the business. Next, assess the situation by acquiring the resources, assumptions, constraints, and other factors that should be considered. Finally, a good mining plan has to be established to achieve both business and data mining goals. The plan should be as detailed as possible.
- Data understanding
This data understanding phase begins with the data collection, this is collected from the available sources, to help get familiar with the data. Some important activities must be performed including data load and data integration to make the data collection successful.
Then, the data needs to be explored by tackling the data mining questions, which can be addressed using querying, reporting, and visualization.
- Data preparation
The preparation of data mostly consumes most time on the project. The outcome of this is the final data set. Once available these sources are identified, they can be selected, cleaned, constructed, and made into the desired form. The data exploration task at greater depth can be carried during this phase to get the patterns based on business understanding.
- Modeling
Firstly, the modeling technique should be selected and be used for the prepared data set.
Next, the test scenario must be generated to validate the quality and validity of the model.
Then, one or more models are created on the prepared data set.
Finally, models need to be assessed carefully involving stakeholders to make sure that created models are met with business initiatives.
- Evaluation
In this evaluation phase, the results have to be evaluated in the context of the objectives given in the first phase. In this phase, new requirements may come up due to the new trends that have been discovered in the model results or from other factors. Gaining understanding is an iterative process in data mining.
- Deployment
The knowledge gained or acquired through the data mining process needs to be presented in a manner that stakeholders can easily comprehend. Based on their business needs, the phase could be simple like creating a report or as complex as a repeatable data mining process across the organization. In this deployment phase, the plans for deployment, maintenance, and monitoring can be created for implementation and future supports. From the project point of view, the final report of the project needs to summary the project experiences and review the project to see what needs to improved created learned lessons.
These 6 steps describe the industry acceptable standard process for data mining, which is an open model describing common approaches being used by data experts. It is the most used analytics model.
Advantages of web scraping
Inexpensive – Web scraping services provide an essential service at a relatively low cost. It is paramount that data is collected back from websites and analyzed so that the internet functions regularly. Web scraping services do the job in a very efficient and pocket-friendly manner.
Easy to implement – Once a web scraping service initiates the proper mechanism to extract data, you are assured of getting data from a single page and the entire domain. This ultimately implies that with just an onetime cost, a lot of data can be collected.
Low maintenance and speed– One aspect that is often ignored when installing new services is the maintenance cost. Long term maintenance causes the project budget to go up. web scraping needs very little or no maintenance over time.
Accuracy – web scraping services are not only very fast, but they are accurate too. Simple errors in data extraction may cause major blunders later on. Accuracy of any type of data is thus very important. websites that are concerned with pricing data, sales prices, real estate numbers, or any kind of financial data, accuracy is extremely sensitive.
Disadvantages of web scrapping
Difficult to analyze – For anybody who is not an expert, the scraping processes are confusing to understand. Although this is not a major problem, some errors could be fixed faster if it was easier to understand for more software developers.
Time – It is very common for amateurs to take some time in the beginning since the software often has a learning curve. Most times web scraping services take time when becoming familiar with the core applications and need to shift to the scrapping language. This implies that such services can take a lot of time before they are up and running at full speed.
Advantages of data mining
Marketing / Retail
Data mining assists marketing companies to the model based on historical data and predicts who will respond to their new marketing campaigns i.e. direct mail, online marketing campaign…etc. Through these results, marketers will make an appropriate decision to selling profitable products to targeted customers.
Finance / Banking
Data mining grants financial institutions insight about loan information and credit reporting by credit reporters. By building a pattern from past customer’s data, the financial institution can determine good and bad loans. Besides, data mining helps banks on fraudulent credit card transactions to protect against the credit card’s owner.
Governments
Data mining helps the government by collecting and analyzing records of the financial transactions to model trends that can detect laundering or criminal activities, these data can also be used to come up with policies.
Disadvantages of data mining
Privacy Issues
The alarm about personal security online has been increasing enormously recently especially with the internet boom of social media and blogs. Because of these privacy issues, people are afraid that their private data is collected and used in a potentially unethical way. Businesses acquire information about their clients in many ways purposely for understanding their behavioral trends. However, businesses may not last forever, and the personal information of clients they own probably is sold to third parties.
Misuse of information/inaccurate information
Information is extracted through data mining intended for business purposes that can be misused. This information may be exploited by individuals or businesses to take advantage of vulnerable people.
Case study for web scraping
Companies offering products or services with specificity in a domain need to have data of similar products and services which appear in the market every day. Web software can be used to keep a regular watch on this data.
Case study for data mining
The company mined from Facebook and other social media platforms Kenyan to help President Uhuru Kenyatta win the disputed elections. Over two presidential election cycles, it presided over some of the most vicious campaigns Kenya has ever witnessed. Cambridge Analytica confirmed its hand in the election where they mined data from millions of Kenya to influence voter’s decisions showing a case of breach of privacy by data mining.
In conclusion, these two methods cannot be used in isolation there is a need for their correlation since web scraping is used for the collection and extraction while data mining is mainly data analysis.