Web scraping vs. data mining
Web scraping doesn't involve the processing of any data. Data mining, on the other hand, refers to the process of analyzing large datasets to uncover trends and valuable insights.
While these terms do share many indistinguishable similarities, they are intrinsically contrasting. Web scraping alludes to the extraction of data from websites. Generally, it can also involve formatting this data into a more understandable format, such as an Excel sheet. While most web scraping may be done manually, in most cases, software tools generally are 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 essential data out of target websites and put them in your database in a structured format and form. Data mining is commonly misunderstood as a means to obtain the data. There are significant differences between data collection and mining the data even though both of them require the act of extraction and collecting. Data mining is the process to discover trends you create from a large set of data. Rather than just acquiring the data and making sense of it, data mining is interdisciplinary, which combines statistics, computer science, and machine learning.

Web scraping doesn't involve the processing of any data. Data mining, on the other hand, refers to the process of analyzing large datasets to uncover trends and valuable insights. No inclusion of any data gathering or extraction is involved. It may not always be web-based. It can also be from other sources. When you gain access to a web page, you can only view the data but cannot access the structured file or download it. Yes, you can copy and paste some of it, but it is time-consuming and not viable. Web scraping automates the process and quickly extracts correct and reliable data from web pages that you can use. You can scrape large quantities of data and different kinds of data as well. It could be text messages, images, email ids, phone numbers, and videos.

Steps involved in web scraping:

The foremost 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 words, HTML is a computer language with a simple structure. When it comes to Parsing, it usually integrates with 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 workaround. HTML parsing is taking in HTML code and extracting meaningful information like the text paragraphs headings, links and bold text.
Parse and Extract
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.
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 we should consider. Finally, we should establish a good mining plan to achieve both business and data mining goals. The project should be as detailed as possible.

Data understanding
This data understanding phase begins with the data collection, and this is collected from the available sources to help get acquainted with the data. Some essential activities must be performed, including data load and data integration, to make the data collection successful.

We should explore the data by challenging the data mining questions, which we raise by 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, we identify these sources; 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.
Firstly, the modeling technique should be selected and used for the prepared data set information. Next, we must create a test scenario to demonstrate the quality and validity of the established model. Then, one or more models are generated on the prepared data set. Finally, we need to assess the model carefully, involving stakeholders to make sure that created models are in line with business initiatives.
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 latest trends that have been shown in the model results or from other factors. Gaining understanding is an iterative process in data mining.
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 easy, like generating a report or as complex as an iterative data mining process across the organization. In this deployment phase, the plans for deployment, maintenance, and monitoring created for the implementation of future supports. From the project perspective, the final report of the program needs to summary the project experiences and review the plan to see what needs to improved created learned lessons.

These six steps describe the industry acceptable standard process for data mining, which is an open model describing conventional approaches used by data experts. It is the most used analytics model.
Advantages of web scraping
Web scraping services provide an essential service at a relatively low cost. It is paramount that data is acquired back from websites and analyzed so that the internet functions regularly. Web scraping services do the job in a very efficient and pocket-friendly method.
Easy to execute
Once a web scraping service initiates the proper mechanism to extract data, we get the assurance of getting data from a single page and the entire domain. It ultimately implies that with just a onetime cost, we can harvest a load of data.
Low costs and speed
One aspect that we often ignore 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 support over time.
Web scraping services are not only swift, but they are accurate too. Simple errors in data extraction may cause major blunders later on. Accuracy of any type of data is thus paramount. Websites that are concerned with pricing data, sales prices, real estate numbers, or any kind of financial data, accuracy is susceptible.
Disadvantages of web scrapping
Difficult to analyze
For amateurs, the scraping processes are confusing to understand. Although this is not a significant problem, some errors could be faster if it was easier to understand for more software developers.
It is prevalent 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. It implies that such services can make a massive amount of time before they are up and running at full speed.
Advantages of data mining
Marketing / Retail
Data mining helps marketing companies to model data 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.
Data mining helps the government by collecting and analyzing records of the financial transactions to model trends that can detect laundering or criminal activities. The government can use this information to come with policies of governance.
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.
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 behavioural trends. However, companies may not last forever, and the personal information of clients they own sold to third parties.
Misuse of information/inaccurate information
Information acquired 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 are in the market every day. They use web software 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 highly contentious elections. Over two presidential election cycles, it oversaw some of the most vicious campaigns Kenya has ever witnessed. Cambridge Analytica confirmed its hand in the presidential contest, 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, we cannot use these two methods in isolation; there is a need for their correlation since web scraping is used for the Collection and extraction, while data mining is mainly for data analysis and presentation.