Data Preparation

Definition of Data Preparation

Data preparation: Data preparation is the process of getting data ready for analysis. This often includes cleaning up the data, removing outliers, and transforming it into a form that is suitable for the analysis that will be performed.

Why is a Data Preparation process used? What is it good for?

Data preparation is a process used in data science and machine learning that helps get data into the right format for analysis or machine learning. It is essential to ensure the best possible results as it reduces potential errors, increases accuracy, and enables better predictive models.

Data preparation involves a variety of tasks such as collecting data from multiple sources, cleansing the data, reorganizing it into a useable form for analysis, validating its accuracy and completeness, dealing with missing information, normalizing values and more. This process can be time consuming but necessary to obtain the best possible results from subsequent analysis.

Data preparation is also critical for machine learning as it helps prepare data for building predictive models. It ensures that all data points are represented accurately and fully so that algorithms can use them properly when making predictions. Data preparation also helps reduce noise in the dataset by removing any irrelevant observations or variables that may lead to over-fitting of models. Additionally, it allows datasets to be divided into training sets (used to build and train models) and test sets (used to validate how well machines have learned).

Overall, data preparation is a key step in any analytics or machine learning project as it enables accurate prediction models built on robust datasets. By ensuring quality input data, organizations can ensure their insights are reliable and up-to-date which will improve decision making processes significantly.

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