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Data Science & Analytics vocabulary · Data Collection and Cleaning

Normalize

Meaning in data science & analytics, with examples

/ˈnɔːrməlaɪz/verbCommon term

In data science & analytics, normalize means: To rescale numeric values to a common range, such as 0 to 1, or to adjust figures so that groups of different sizes can be compared fairly.

In Turkish: normalleştirmek, normalize etmek, ölçeklendirmek

How it is used

In modelling you normalize features so that no single one dominates; in reporting you normalize by population or user count ('per 1,000 users'). The British spelling is 'normalise' and the noun is normalization.

Example sentences

  1. We normalized the sales figures by store size, so small shops can be compared with large ones.

  2. Normalize the features before you run k-means.

Common collocations

  • normalize the data
  • normalize by population
  • normalized score
  • min-max normalization
  • feature engineeringThe work of creating new input variables from raw data to help a model learn, for example turning a date of birth into an age or counting a customer's purchases.
  • data wranglingThe work of turning messy raw data into a clean, well-structured form for analysis, including reshaping, merging, filtering and fixing formats.
  • clusteringA method that groups similar data points together without being told the groups in advance, for example to find natural customer segments.
  • schemaThe structure of a database or dataset: which tables exist, which columns they have, the data type of each column and how the tables relate to each other.

This word in other fields

The same English word can have a different meaning in another field:

More terms: Data Collection and Cleaning

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