Data mining is a powerful and increasingly popular tool that uses machine learning to uncover patterns in data and help businesses stay competitive. Data scientists are trained to understand business objectives and select the correct techniques for data exploration and pre-processing. After formulating the business question, data mining methods are chosen and evaluated to determine their ability to fit the data set and answer the query. Results are then reported back to the business owner. Data mining is an essential part of modern business, allowing the organization to keep up with the competition and remain successful. With its growing popularity, the need for data scientists is rapidly increasing.
TopImplementation Of Data Mining And Challenges
With the need for problem analysis, it has created a position to be responsible for data analysis, namely Data Scientist. However, the Data Scientists are not solely from experts in digital technology, but they can be any person who owns the data. The medical technicians, who have information on the patients’ health and understand the context of medical industry, question asking and the nature of the data, can become a Data Scientist.
Many industries have employed data scientists who are not familiar with the industry or lack experiences in the data they are to analyze. Therefore, the data scientists have to consult the questioners, possibly from the management department, and the experts in order to obtain results consistent with the facts. Therefore, in the scientific work, the data can be applied in various industries as presented in the following examples.
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In the market, to determine the selling price, the consumers can see a model which consists of many sub-models. The prices vary depending on the car’s components. However, the consumers can still find similar pricings in all sub-models. For instance, Sub-model 1 costs 1,000,000 baht. The 2nd Sub-model is priced at 1,600,000 baht and the 3rd Sub-model is priced 1,7000,000 baht. It is obvious that the difference between the 2nd and 3rd Sub-models is only 100,000 baht. Such a price setting is caused by dividing customers into 3 groups to suit the number of Sub-models which is three. Data mining techniques are then used to analyze the mid-prices of each customer segment of each Sub-model. Unless the data scientists analyze the data using the techniques, general businesspeople may divide the pricing into 1,000,000 baht, 1,300,000 and 1,700,000 baht for Sub-data respectively. The clustering techniques are, therefore, used to determine the price of car sales to be able to set a suitable price for each target group. And the dealers also get the most profit.
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To forecast the condominium price in Bangkok (Sunkpho and Ramjan, 2020) using data analysis, it was found that variables affecting the prices of the condominium in Bangkok are the distance from the condo to Skytrain and MRT stations, the number of rooms, the number of floors and the age of the condominiums. Data scientists use deep learning techniques to analyze such variables and the prices, and found that the smaller distance from the condo to the sky train and subway stations, the higher the prices are. The more numbers of rooms are, the lower the prices will become. The more floors the condos have, the higher the prices are. And if the age of the condominium is less, its price is high. Therefore, the condominium real estate industries can consider such variables to determine the appropriate prices of the condominium projects.
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To categorize borrowers with the ability to pay debts, Banks have to classify their borrowers. To promote bank services, the borrowers are convinced to extend their loans with attractive offers such as a lower interest rate (Refinance). Data scientists can use data mining techniques to classify the customers by analyzing various variables. Since the banks have information about borrowers such as age, income, loan duration, default rate and the amount of additional loans that have already been approved, they can classify borrowers with the ability to repay their debts, so that they can offer marketing promotions. Banks can increase their interest income further.