How Data Mining and Machine Learning Can Support a Business
Data mining is a procedure usually used by companies in turning raw data into useful information. By using software to find patterns in large batches of data, businesses can learn more about their customers, develop effective marketing strategies, increase their sales, and reduce costs. On the other hand, machine learning is a method of data analysis that automates analytical model building. Primarily, it is a branch of artificial intelligence as the systems can learn from data identify patterns, and make vital decisions with less human interventions. Hence, through using algorithms to build models that uncover connections, organizations can make crucial decisions without involving human beings.
Application of data mining in the business
Notably, technology is rapidly advancing, and this has led to the volumes of data which are contained in the electronic environment to increase .with the increase in the data amount globally, studies have been done on the use of big data. the massive amounts of data accumulated from different sources are always in liquidity; hence, big data solutions are a vital decision for companies to analyze the liquid data. (M. Bharati & Bharati Mahadev Ramageri, 2010) Data ming process is essential in business as it helps in processing data and finding effective results.arguably, data mining is being used in different fields in the industries to compete with the present data analysis environment. For the sectors to obtain smooth and quick evaluations of patterns and trends of the current markets to produce essential and fast market trend analysis, different mining techniques and tools are being used.for instance. Financial data is collected mostly from the banks and other various commercial sectors for data analysis,financial data needs a systematic method, and data mining plays a significant role in business data analysis. Data mining follows model development and evaluation, model building, refinement of data collection, and comprehension of data. Data mining is then used to predict profitability and trends and estimate the risk and predict credit card feuds in the banking field. The neural network, which is a technique of data mining, is utilized in price prediction, stock forecasting in the financial market. Identically, data mining is also used in the retail industry as it provides large amounts of data on services and consumption, transportation of goods and customer purchasing history, and sales. Retail data mining is critical in determining distribution policies, shopping patterns, and customer behaviors.
Application of machine learning in business
Machine learning in business applications helps in extracting relevant information from massive raw data sets. If it is appropriately implemented, machine learning can serve as a solution to various business challenges and also help in predicting sophisticated customer behaviors. Machines learning uses algorithms designed to process vast amounts of information and make decisions based on logic, which enables the devices to learn and complete tasks without further programming. Machines learning is applicable in various industries like the finance industry, healthcare industry,transport industry, and agriculture.
Machine learning is playing a vital role in the healthcare industry by improving the delivery system of healthcare services, reducing costs of handling patients’ data, developing new treatment processes and drugs, and remote monitoring. For example, machine learning is used ot enhance imaging analytics and pathology sing particular tools and algorithms.applcivations of machine learning a help radiologists to find out the subtleness in scans, and this enables them to detect and diagnose the health issues at an earky stage. For instance, these tools can be used im the identification of cancerous tumors in mammograms.apart from that; machine learning can be used in the discovery of drugs form the screening of a drugs compounds to the estimated rate of success based on the biological factors. therefore, machine learning is being utilized by the pharmaceutical companies in the discovery of drugs and the manufacturing processes. (Akhil, Samreen & Aluvalu, 2018)
Machine learning is arguably essential as it provides opportunities for high performance and comprehension of data-intensive procedures in the agricultural sector. Production of the machine learning model in a particular task is evaluated by the performance metric, which is enhanced with experience over time ( Nagageetha & Pateti, 2019). Significantly, the trained learning model can be used to predict data by using the knowledge gained in the training procedure (Habeeb, 2017). From the research carried out on machine learning on agriculture, it can be concluded that ML’s application is essential in the following categories. Primarily, yield production is one of the most critical topics in agriculture. It entails yield mapping, yield estimation matching the crop supply ith demand, and crop management to enhance productivity. Secondly, it helps in disease detection. Identically, one of the essential aspects to consider in agriculture is pest and disease control. The standard method used in the control of pests and diseases is through spraying the pesticides on the cropping area. This practice is usually expensive and has various adverse environmental effects. For instance, there can be contamination of the groundwater, adverse effects on the ecosystem, and negative effects on the healthy crop products. Application of machine learning is practical because the agrochemicals are used in the right place and time. Machine learning application is also essential in weed detection. Critically, necessarily, this is another critical problem in agriculture as the weeds are a danger to crop production. It is necessary to detect weeds accurately as they can be challenging to detect. The machine learning algorithms together with sensors can lead to accurate discovery and getting rid of weeds at low costs, without causing any environmental side effects (Konstantinos et al., 2018)
References
Habeeb, A. (2017). Introduction to Artificial Intelligence. Research Gate, 1-15.
Jabbar Akhil, Shirina Samreen & Rajanikanth Aluvalu. (2018). the Future of Health care: Machine Learning. International journal of engineering and technology, 23-25.
- Bharati & Bharati Mahadev Ramageri. (2010). Data mining techniques and applications. Indian Journal of Computer Science and Engineering, 301-305.
- Nagageetha & Nagaraja Kumar Pateti. (2019). Machine Learning Applications on Agricultural. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 110-122.
Ugochukwu O. Matthew, Musbahu Yunusa Makama, Aminu Abbas Gumel & Abdullahi Basher Abdullahi. (2019). Data Mining Applications In Banking Sector For Effective Service Delivery. Research Gate , 1-15.