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Data Preprocessing Definition

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Data Preprocessing Definition

Data Preprocessing Definition – Businesses use data to set company plans, establish business goals, and comprehend business objectives. However, neither directly collected nor Internet-obtained data can be directly analyzed or processed using a computer. However, there is a procedure called data preparation that converts raw data into a more comprehensible format.

This procedure can be found in every organization that utilizes big volumes of data. It will simplify the process of data mining, which is the collection and analysis of data in order to extract useful information.

Data Preprocessing Definition

The Meaning Of Data Preprocessing

Data preparation is the transformation of raw data into a more comprehensible format. This procedure is required to rectify flaws in raw data, which is frequently insufficient and has an irregular format.

During preprocessing, data are validated and imputed. Validation seeks to evaluate the completeness and precision of the filtered data. Imputation, meantime, tries to repair errors and input missing numbers manually or automatically via a business process automation (BPA) tool.

Data quality has a direct effect on the success of any data analysis-based endeavor. Data preparation plays a part in machine learning by ensuring that massive data is prepared and the information it contains can be comprehended by the company’s algorithm so that it can generate more accurate results.

The Advantages of Data Preprocessing

Given the above information, it is clear that data preparation plays a crucial role in database-driven projects. It is also possible to say that data preparation delivers a variety of benefits for projects and businesses, including:

  • Streamlining the process of data mining
  • Make data more readable.
  • Reduces data representation burden
  • Reduce significantly the length of data mining
  • Simplify the data analysis process in machine learning

Phases of Data Preprocessing Work

The data processing work process is organized into four distinct stages for best performance: data cleaning, data integration, data transformation, and data reduction.

Data Cleaning

In the data cleaning step, the raw data will be cleaned by multiple procedures, including the addition of missing values, smoothing of noisy data, and resolution of detected inconsistencies. Data can also be cleaned and arranged using segments of similar size and then smoothing them (binning), with a linear or multiple regression function (regression), or by grouping them into groups of comparable data (regression) (grouping).

Data Integration

Integration of data is the process of combining data from multiple sources into a single data set (dataset). Data with different formats must be transformed to the same format prior to merging. The overall objective of this data integration method is to unify and streamline data through the following steps such as verify that all data have the same format and properties, eliminates unnecessary properties from each data source and detect contradictory data values.

Transformation of Data

The data will be normalized and standardized at this point. Data normalization was performed to guarantee that no redundant data existed, whilst data generalization was performed to homogenize the data. Data transformation enables the modification of data structures, data formats, and data values to create a dataset that is suited for the mining process or the intended algorithm. There are at least five possible steps in the data transformation process, including Agregation, Normalization, Feature Selection, Discreditization, Hierarchy Generation Concept.

Data Compression

The final necessary step is data reduction or the reduction of data quantity. It is feared that data mining’s reliance on voluminous amounts of information may reduce its accuracy. Therefore, it is necessary to minimize the data sample while ensuring that the process does not alter the outcomes of the data analysis. When decreasing data, there are three strategies that can be used: dimensionality reduction, numerosity reduction, and data compression. The three strategies are adaptable based on factors such as whether the data being processed is large, medium, or compressed and poses a threat.


These are the Data Preprocessing Definition, the topic of data preparation, which is an essential step that aids the data analysis procedure. This procedure will select data from multiple sources and standardize its format to create a data set.

In this approach, firms can obtain more precise outcomes, which can then be utilized to determine business plans, define business goals, and comprehend corporate objectives.