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Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed, [1] and is often an important step in the data mining process. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values , amongst other issues.
Preprocessing can refer to the following topics in computer science: Preprocessor , a program that processes its input data to produce output that is used as input to another program like a compiler Data pre-processing , used in machine learning and data mining to make input data easier to work with
Data preparation is the act of manipulating (or pre-processing) raw data (which may come from disparate data sources) into a form that can readily and accurately be analysed, e.g. for business purposes. [1]
A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before data mining. The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data.
Most preprocessors are specific to a particular data processing task (e.g., compiling the C language). A preprocessor may be promoted as being general purpose, meaning that it is not aimed at a specific usage or programming language, and is intended to be used for a wide variety of text processing tasks.
Data processing is the collection and manipulation of digital data to produce meaningful information. [1] Data processing is a form of information processing , which is the modification (processing) of information in any manner detectable by an observer.
Don't rely on bloviating pundits to tell you who'll prevail on Hollywood's big night. The Huffington Post crunched the stats on every Oscar nominee of the past 30 years to produce a scientific metric for predicting the winners at the 2013 Academy Awards.
Feature scaling is a method used to normalize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step.