## 4 Machine Learning (ML) Algorithms
### Progress of Machine Learning in Pumps Application
Techniques such as support vector machines (SVM) and random forest (RF) were popular at first. Then came artificial neural network-based techniques (ANN), which performed well in both diagnostics and prognostics [33]. The benefits of ML include the ability to evaluate enormous amounts of data and to identify particular trends and patterns that people would not notice. For example, for an e-commerce site such as Amazon, knowing its users' browsing patterns and past purchases enables the site to offer them the most appropriate goods, discounts, and reminders. It takes advantage of its findings to show them relevant adverts. The user no longer has to supervise a project at every stage, due to the advent and robustness of ML. Giving machines the ability to learn enables them to make predictions and independently improve their algorithms. As ML algorithms mature, their accuracy and productivity continue to increase. They can consequently make quick and intelligent selections. Borrowed from the example of weather forecast model, the algorithms become faster while making more accurate predictions as the dataset expands. In dynamic or uncertain contexts, machine learning algorithms are adept at managing data that is multidimensional and multivariate. In the end, it is used in numerous applications. However, ML also has a few drawbacks, including the fact that AI tools require a sufficient quantity of high-quality training data before they can function well in operational mode. While smarter algorithms may aid in obtaining better results from smaller datasets, bad data cannot be improved by manipulation. Thus, access to large and high-quality data is a major facilitator and obstacle for the effective development of AI systems. Oil and gas fields produce infinite quantities of raw data. In the oil and gas business, there are identified challenges regarding the quality and accuracy of field data, as well as a general absence of huge volumes of labeled data. Training datasets must be meticulously gathered using a well-planned workflow and circumstance-specific multi-year procedure. To increase the value of the data that oil and gas firms own or have access to, they must restructure and modify their organizational structures and procedures [111]. Another major challenge is the ability to accurately interpret the results generated by the algorithms. The researcher must also carefully choose the algorithms for their purposes. Machine learning is autonomous but is also highly susceptible to errors. According to an average simulation, by 2030, over 70% of organizations may accept the use of at least one form of AI technology, but fewer than half may have fully absorbed the five categories [112]. The number of people will increase in the future. Monitoring the status of a machine is a procedure that cannot avoid failure but can forecast the potential of failure or a fault condition by measuring certain parameters. If an algorithm for machine learning can be developed, the system will be more efficient and it will be possible to detect faults at the ground level, thereby extending the pumping system's lifespan. Among the numerous machine learning techniques, classification and regression analysis are utilized in the majority of cases to find anomalies. When discrete systems are present, classification is typically employed, whereas regression is utilized when continuous functions are present. There are further machine learning algorithms that, with the aid of a predictive control model, can assess system anomalies. The predictive control hybrid model is seen as a new area of research, whereby researchers can predict anomalies to reduce the loss of energy, resources, and time, and make the system faultless.