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What is Data Preprocessing

Encyclopedia of Data Science and Machine Learning
The manipulation of raw gathered data to remove redundant, unreliable, or incorrect data points to make a dataset more suitable for machine learning tasks.
Published in Chapter:
Developing Machine Learning Skills With No-Code Machine Learning Tools
Emmanuel Djaba (Ghana Institute of Management and Public Administration, Ghana) and Joseph Budu (Ghana Institute of Management and Public Administration, Ghana)
Copyright: © 2023 |Pages: 15
DOI: 10.4018/978-1-7998-9220-5.ch097
No-code machine learning (ML) tools provide an avenue for individuals who lack advanced ML skills to develop ML applications. Extant literature indicates that by using such tools, individuals can acquire relevant ML skills. However, no explanation has been provided of how the use of no-code ML tools leads to the generation of these skills. Using the theory of technology affordances and constraints, this article undertakes a qualitative evaluation of publicly available no-code ML tools to explain how their usage can lead to the formation of relevant ML skills. Subsequently, the authors show that no-code ML tools generate familiarization affordances, utilization affordances, and administration affordances. Subsequently, they provide a conceptual framework and process model that depicts how these affordances lead to the generating of ML skills.
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Fuzzy Sequential Patterns for Quantitative Data Mining
It is one of the first steps of the KDD process. It refers to data preparation, where variable samples are selected, new variables are constructed, and existing ones are transformed, depending on the goal of the discovery process. During this step data are converted to the input format of the mining algorithm.
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Using Data Mining Techniques to Predict Obstetric Fistula in Tanzania: A Case of CCBRT
Is a data mining technique that involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors.
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US Medical Expense Analysis Through Frequency and Severity Bootstrapping and Regression Model
One important step in machine learning. Manipulating data before it is used to build a model for example, in order to enhance performance and more applicable to algorithms or models. It usually involves steps such as data cleaning, data integration, data reduction, and data transformation.
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Machine Learning in Text Analysis
A process for making data ready for analysis purpose by eliminating unwanted things from data.
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