IMPROVING REGRESSION ANALYSIS WITH BOX-COX TRANSFORMATION IN A BAYESIAN FRAMEWORK

. Prediction and forecasting of the Big data is an emerging transformation to build a best linear Model. Regression analysis is widely used for forecasting time series datasets, but non-significant parameters can lead to unreliable predictions. To improve models, the Bayesian technique is used, incorporating prior knowledge, and investigating their superiority. This powerful statistical methodology is essential for accurate crop yield forecasting. The present article proposes to predict and forecast the rice crop production in India using Bayesian linear regression methodology, and the results were compared with simple linear regression. A Box-Cox transformation was also included in the procedure to increase forecast accuracy. The method is obviously perfect for predicting future values. In addition, To evaluate model accuracy, we used squared R, MSE, RMSE, MAE, MAPE, and Theil's U. After applying the Box-Cox transformation, the findings of the result analysis are more accurate and easier to predict. Finally, Comparing simple Linear regression and Bayesian regression models, it was found that the Bayesian framework yielded superior results compared to classical approaches.