Use of Supervised Learning Algorithms in Predictive Analytics

Abstract

Human life has been made easier and more comfortable thanks to technological advancements. Predictive analytics is a revolutionary technique that utilizes a significant amount of historical data to create predictions about the future. Its goal is to analyze specific data in order to forecast the future and identify the risks connected with a certain decision. Using data-driven predictive models, decisions that were the product of several mathematical computations can be made more quickly and accurately. Banking, education, healthcare, entertainment, and other industries employ technologies to create difficult decisions and forecast future trends. The goal of predictive analytics is to make accurate and cost-effective predictions. The data required for the analysis comes from a variety of sources and will be in a structured, semi-structured, or unstructured format. The classification of a large volume of data during the data analytics process is a tough challenge. The purpose of classification is to turn accessible data into knowledge that will be useful in future research. It is possible to learn from the training data set using machine learning, and the knowledge gathered this way can be applied to effective decision-making. Classification algorithms examine at the training data and use that knowledge to categorize the test data. To maximize their profitability, organizations acquire experts in critical decision-making. Using human intelligence to make key decisions is costly, dangerous, and time-consuming. As a result, predictive analytics is getting lots of attention these days. It makes the most out of available data in order to make better and more informed decisions. It can be used to discover different patterns and relationships in data in order to forecast future events. Data analysis delivers useful insights and reliably identifies potential hazards. The predictive model and attributes chosen for analysis determine the accuracy of the prediction. The use of an incorrect model and erroneous data can be catastrophic for an organization. Artificial intelligence, cloud computing, machine learning, and other emergent technologies are used to collect, store, and analyze data effectively. The quality of the data acquired and the models employed for analysis are both important factors in forecasting. To analyze the data and make predictions, many supervised learning approaches can be applied. The authors of this paper attempt to provide a thorough overview of the many supervised learning approaches prevalent in machine learning. They also attempt to investigate several application areas in which these strategies are employed to aid decision-making.