  This research employs Gaussian Process Regression (GPR) with an ensemble
kernel, integrating Exponential Squared, Revised Mat\'ern, and Rational
Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization
was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21
for Revised Mat\'ern, and 0.13 for Rational Quadratic. The ensemble kernel
demonstrated superior performance in predictive accuracy, achieving an \( R^2
\) score near 1.0, and significantly lower values in Mean Squared Error (MSE),
Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). These findings
highlight the efficacy of ensemble kernels in GPR for predictive analytics in
complex pharmaceutical sales datasets.
