  We present a new method to predict the line-of-sight column density (NH)
values of active galactic nuclei (AGN) based on mid-infrared (MIR), soft, and
hard X-ray data. We developed a multiple linear regression machine learning
algorithm trained with WISE colors, Swift-BAT count rates, soft X-ray hardness
ratios, and an MIR-soft X-ray flux ratio. Our algorithm was trained off 451 AGN
from the Swift-BAT sample with known NH and has the ability to accurately
predict NH values for AGN of all levels of obscuration, as evidenced by its
Spearman correlation coefficient value of 0.86 and its 75% classification
accuracy. This is significant as few other methods can be reliably applied to
AGN with Log(NH <) 22.5. It was determined that the two soft X-ray hardness
ratios and the MIR-soft X-ray flux ratio were the largest contributors towards
accurate NH determination. This algorithm will contribute significantly to
finding Compton-thick (CT-) AGN (NH >= 10^24 cm^-2), thus enabling us to
determine the true intrinsic fraction of CT-AGN in the local universe and their
contribution to the Cosmic X-ray Background.
