Computational Statistics-Based Prediction Algorithms Using Machine Learning

Computational Statistics-Based Prediction Algorithms Using Machine Learning

Venkat Narayana Rao T., Manogna Thumukunta, Muralidhar Kurni, Saritha K.
Copyright: © 2021 |Pages: 17
DOI: 10.4018/978-1-7998-7701-1.ch004
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Abstract

Artificial intelligence and automation are believed by many to be the new age of industrial revolution. Machine learning is an artificial intelligence section that recognizes patterns from vast amounts of data and projects useful information. Prediction, as an application of machine learning, has been sought after by all kinds of industries. Predictive models with higher efficiencies have proven effective in reducing market risks, predicting natural disasters, indicating health risks, and predicting stock values. The quality of decision making through these algorithms has left a lasting impression on several businesses and is bound to alter how the world looks at analytics. This chapter includes an introduction to machine learning and prediction using machine learning. It also sheds light on its approach and its applications.
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Predictive Modeling

Predictive modeling is a concept that employs a Machine Learning algorithm that is capable of making predictions by learning from a training dataset. Predictive modeling is a process of predicting outcomes in the future by analyzing the results of the past (Kumar & Garg, 2018). A predictive model’s function is to identify that a data unit from a different source with similarity to existing patterns exhibits a specific behavior(Sodhi et al., 2019). The available data with known values and labels are used to train the training dataset model. The dataset with unknown values and labels that the model has to predict is known as the test dataset(Xin et al., 2018).

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