  To advance the development of materials through data-driven scientific
methods, appropriate methods for building machine learning (ML)-ready feature
tables from measured and computed data must be established. In materials
development, X-ray diffraction (XRD) is an effective technique for analysing
crystal structures and other microstructural features that have information
that can explain material properties. Therefore, the fully automated extraction
of peak features from XRD data without the bias of an analyst is a significant
challenge. This study aimed to establish an efficient and robust approach for
constructing peak feature tables that follow ML standards (ML-ready) from XRD
data. We challenge peak feature extraction in the situation where only the peak
function profile is known a priori, without knowledge of the measurement
material or crystal structure factor. We utilized Bayesian estimation to
extract peak features from XRD data and subsequently performed Bayesian
regression analysis with feature selection to predict the material property.
The proposed method focused only on the tops of peaks within localized regions
of interest (ROIs) and extracted peak features quickly and accurately. This
process facilitated the rapid extracting of major peak features from the XRD
data and the construction of an ML-ready feature table. We then applied
Bayesian linear regression to the maximum energy product $(BH)_{max}$, using
the extracted peak features as the explanatory variable. The outcomes yielded
reasonable and robust regression results. Thus, the findings of this study
indicated that \textit{004} peak height and area were important features for
predicting $(BH)_{max}$.
