A Machine Learning Framework for Price Estimation in Air Force Acquisition
Name
07312026_Cost_estimation_for_DoW_Acquisitions__FINAL_w_PA_edits_.pdf
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8.54 MB
Format
Adobe PDF
Checksum (MD5)
c8a75e94ccb8737a37f41fced2abb16e
Author(s)
Kefallinos, Paola
O'brien, Cuyler
Date Issued
August 21, 2026
Abstract
Price estimation in the Air Force is often bottlenecked by data that is proprietary, incomplete, or unavailable at the time planning begins, leaving pricing teams reactive and acquisition timelines at risk. This work investigates whether machine learning models trained on public contracting data can produce estimates accurate enough to support acquisition planning. The data pipeline processed Air Force contracts spanning 2010-2025 sourced from USASpending.gov, applying data cleaning and feature engineering methods that reduced columns by 84% and rows by 53%. The modeling pipeline evaluated two regression models (XGBoost and Random Forest), tuned hyperparameters, and identified t he o ptimal f eature s et through permutation importance and elbow analysis, resulting in a 34% reduction of the encoded feature space. The optimally configured XGBoost model trained on the reduced feature set achieved a test R 2 of 0.7068, mean absolute error of 0.3137, and root mean squared error of 0.4302 on the log scale, and a median absolute percent error of 51.3% on the dollar scale. While these results fall short of operational adoption thresholds, they demonstrate the viability of a public contracting data pipeline to support a machine learning price estimation engine, laying the groundwork for the next generation of acquisition automation. With continued investment, this work can be extended with AI integrations to enrich model inputs and produce interactive outputs, generating more accurate estimates, reducing programmatic risk, and compressing acquisition timelines.
Subjects
price estimation
contract award value
machine learning
XGBoost
USASpending
acquisition planning
Department of the Air Force
MIT Department
Lincoln Laboratory
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