How can you standardize your data during the cleaning process?

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Data mining is the process of extracting useful information from large and complex datasets. However, before you can apply any analytical techniques or algorithms, you need to prepare your data for optimal results. One of the most important steps in data preparation is data cleaning, which involves removing or correcting errors, inconsistencies, outliers, and missing values. In this article, you will learn how to standardize your data during the cleaning process, and why it is beneficial for your data mining goals.

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1 What is data standardization?

Data standardization is the process of transforming your data into a consistent and comparable format, so that you can easily compare, analyze, and integrate it with other sources. Data standardization can involve various operations, such as converting units, formats, scales, and encodings, as well as normalizing or scaling numerical values to a common range or distribution. Data standardization can help you improve the quality, accuracy, and reliability of your data, as well as reduce the complexity and redundancy of your data.

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2 Why standardize your data?

Standardizing your data can bring several advantages for data mining projects, such as improving the performance and accuracy of data mining algorithms that are sensitive to the scale or distribution of the data. It also enables data integration and comparison from different sources, making it easier to explore and visualize the data. Furthermore, standardizing your data simplifies its maintenance and management, reducing the amount of transformations and conversions needed in each step of the data mining process.

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3 How to standardize your data?

Standardizing your data for a data mining project involves various steps and techniques, depending on the type, structure, and characteristics of your data, as well as the objectives and requirements of your project. Identifying the variables and attributes that need to be standardized is the first step. This includes numerical, categorical, ordinal, or textual data and their units, formats, scales, and encodings. Then you must choose the appropriate standardization technique for each variable or attribute. Examples include unit conversion, format conversion, encoding conversion, normalization, or scaling. Once you have chosen a technique, you can use software tools and programming languages for data standardization such as Excel, SQL, Python, R, or SAS. Finally, you must verify and validate the results of your data standardization by checking the format, range, and distribution of your standardized data. You can use descriptive statistics, histograms, boxplots, scatterplots, or correlation matrices to do this.

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4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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MARIANA MOSCOSA

??—>??Directing Digital Transformation DX, IIoT, Automation and AI Strategies | Enabling Interconnected Technology Ecosystems for Manufacturing Execution and Resilient Supply Chains.
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I’ve seen common mistakes you should really avoid: 1) data being ingested and then raw data distributions being analyzed. 2) raw data analysis with no standardization, example: name conventions. 3) assume everyone will remember agreements on data cleaning and transformation in their minds.

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How can you standardize your data during the cleaning process?

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1 What is data standardization?

2 Why standardize your data?

3 How to standardize your data?

4 Here’s what else to consider

Data Mining

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How can you standardize your data during the cleaning process?

1

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1 What is data standardization?

2 Why standardize your data?

3 How to standardize your data?

4 Here’s what else to consider

Data Mining

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