Data Engineering and Exploratory Data Analysis Techniques

Chandrasekar Vuppalapati ()
Additional contact information
Chandrasekar Vuppalapati: San Jose State University

Chapter Chapter 2 in Machine Learning and Artificial Intelligence for Agricultural Economics, 2021, pp 75-158 from Springer

Abstract: Abstract This chapter introduces data sources and data engineering attributes for handling agricultural datasets. The chapter starts with Knowledge Discovery in Databases (KDD) framework, the reference architecture for data mining applications, feature engineering techniques, and exploratory data analysis (EDA) process. Next, the chapter introduces feature reduction techniques such as principal component analysis (PCA). In agriculture data, most of the time, data for all important features may not be available and predictor class may not be fully populated. The chapter introduces imbalanced class techniques and demonstrates the workings. Finally, the chapter concludes with explainability of ML model and the need for democratizing the artificial intelligence.

Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-77485-1_2

Ordering information: This item can be ordered from
http://www.springer.com/9783030774851

DOI: 10.1007/978-3-030-77485-1_2

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().