Regression model approach to predict missing values in the Excel sheet databases
Journal Title: International Journal of Computer Science & Engineering Technology - Year 2012, Vol 3, Issue 4
Abstract
The most important stage of data mining is pre-processing, where we prepare the data for mining. Real-world data tends to be incomplete, noisy, and inconsistent and an important task when pre-processing the data is to fill in missing values, smooth out noise and correct inconsistencies. We can handle the missing values by ignoring data row, using global constant to fill miss missing value, using attribute mean to fill missing value, using attribute mean for all samples belonging to the same class, using most probable value to fill the missing value , and finally we can use the data mining algorithm to predict the value. We use Regression method for this prediction of missing values. This method is used to map a data item to a real valued prediction variable. All these operations can be done by using EXCEL sheet database also.
Authors and Affiliations
Z. Mahesh Kum , R. Manjula
Keywords
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