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A machine learning framework achieved test R² values from 0.942 to 0.963 for predicting missing Sonic logs and 0.927 to 0.930 for Gamma Ray logs. This dataset from the University of Kansas supports a study on using K-Nearest Neighbors regression to address gaps in geophysical well data. The workflow involves correlation-guided feature selection and min–max normalization on data from five wells.
Primary data file is a 2.1 MB DOCX document, which may require extraction of the underlying dataset.