ML Algorithms and Their Approach on COVID-19 Data Analysis

Title

ML Algorithms and Their Approach on COVID-19 Data Analysis

Creator

Ashok K.; Reddy P.A.; Kumar K.P.

Description

This chapter begins with characterizing Supervised Learning and Unsupervised learning and investigates Machine Learning algorithms in every one of the sub domains of Regression, Classification, Clustering, and so forth. It also talks about the engineering of calculations like Linear Regression, Logistic Regression, K-Means, K Nearest Neighbors, Hierarchical, DB Scan, Decision Tree, Random Forest Regression, and Random Forest classifier. Utilization of every algorithm to investigate the dataset will be displayed by carrying out it on renowned dataset model, and output of each piece of code is displayed with their preview. This section likewise takes care of the issue of predicting the future number of COVID-19 cases and the precision behind each model or algorithm is shown and investigated utilizing different measurements dependent on situation or issue articulation, for example, either issue is on forecast or order. This chapter does not focus on the solution of COVID-19 data analysis or expectation, rather it will be followed and will task different models dependent on need with conclusive target being clear comprehension of the Machine Learning algorithms and its execution in Python. 2023 Scrivener Publishing LLC.

Source

Data Engineering and Data Science: Concepts and Applications, pp. 335-349.

Date

2023-01-01

Publisher

wiley

Subject

classification; covid analysis; covid data visualization; Machine learning; regression; supervised learning; un-supervised learning

Coverage

Ashok K., Department of CS, Kyungpook National University, Daegu, South Korea; Reddy P.A., Department of CSE, Christ University, Karnataka, Bangalore, India; Kumar K.P., Department of CSE, Christ University, Karnataka, Bangalore, India

Rights

Restricted Access

Relation

ISBN: 978-111984199-9; 978-111984187-6

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1002/9781119841999.ch14

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174105993&doi=10.1002%2f9781119841999.ch14&partnerID=40&md5=474ddaa51fcf6f604ec1d41c321c3799