Regression Model Approach to Predict Missing Values in the Excel Sheet Databases

The most important stage of data mining is pre-processing, where the authors 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. They 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 they can use the data mining algorithm to predict the value.

Provided by: International Journal of Computer Science & Engineering Technology (IJCSET) Topic: Data Management Date Added: Apr 2013 Format: PDF

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