Feature Engineering for Various Data Types in Data Science

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Author(s): Nilesh Kumar Sahu (Birla Institute of Technology, Mesra, India), Manorama Patnaik (Birla Institute of Technology, Mesra, India)and Itu Snigdh (Birla Institute of Technology, Mesra, India)
Copyright: 2021
Pages: 16
Source title: Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science
Source Author(s)/Editor(s): Mrutyunjaya Panda (Utkal University, India)and Harekrishna Misra (Institute of Rural Management, Anand, India)
DOI: 10.4018/978-1-7998-6659-6.ch001

Keywords: Data Mining and Databases / Engineering Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

The precision of any machine learning algorithm depends on the data set, its suitability, and its volume. Therefore, data and its characteristics have currently become the predominant components of any predictive or precision-based domain like machine learning. Feature engineering refers to the process of changing and preparing this input data so that it is ready for training machine learning models. Several features such as categorical, numerical, mixed, date, and time are to be considered for feature extraction in feature engineering. Datasets containing characteristics such as cardinality, missing data, and rare labels for categorical features, distribution, outliers, and magnitude are currently considered as features. This chapter discusses various data types and their techniques for applying to feature engineering. This chapter also focuses on the implementation of various data techniques for feature extraction.

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Feature Engineering for Various Data Types in Data Science

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