Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
3rd Edition
ISBN: 9781118729274
Author: Galit Shmueli, Peter C. Bruce, Nitin R. Patel
Publisher: WILEY
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Chapter 2, Problem 7P

A dataset has 1000 records and 50 variables with 5% of the values missing, spread randomly throughout the records and variables. An analyst decides to remove records with missing values. About how many records would you expect to be removed?

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In the ETL process, one important step to process when transforming the data is to work with null, n/a, and zero values in the dataset. If you have a field of quantitative data (e.g., number of years each individual in the table has held a full-time job), what would be the effect of the following? a. Transforming null and n/a values into blanks . b. Transforming null and n/a values into zeroes c. Deleting records that have null and n/a values from your dataset (Hint: Think about the impact on different aggregate functions, such as COUNT and AVERAGE.)

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