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What is Missing Data

Chapter 69
Encyclopedia of Data Science and Machine Learning
data values that should be present in a dataset but are not. Most datasets are based on a tabular model, with a collection of homogeneous records (that is, records that contain values for the same, fixed collection of attributes). When a record is incomplete (it does not contain values for all attributes), we say that there is missing data. This situation creates a problem for many analyses and therefore must be addressed during Data Cleaning.
Published in Chapter:
Data Science in the Database: Using SQL for Data Preparation
Antonio Badia (University of Louisville, USA)
Copyright: © 2023 |Pages: 14
DOI: 10.4018/978-1-7998-9220-5.ch069
Abstract
An important part of the data lifecycle is preparing the data for analysis. This includes exploratory data analysis to find problems, data cleaning to solve problems related to 'dirty' data, and data transformation to achieve the format required by data analytics tools. Much data resides in databases, and it is beneficial for the data scientist to be able to carry out as much of this work as possible inside the database. This can be achieved using SQL. This article overviews some of the basic approaches to data exploration, cleaning and wrangling using SQL, including methods to deal with missing data, outliers, and bad values.
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More Results
Chapter 7
Classification with Incomplete Data
data are said to be missing when there is no information for one or more pattern on one or more features in a research study.
Published in Chapter: Classification with Incomplete Data; From: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
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Chapter 241
Weighting Imputation for Categorical Data
It refers that no data value is stored for the variable in the observation.
Published in Chapter: Weighting Imputation for Categorical Data; From: Encyclopedia of Business Analytics and Optimization
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