Authors - Sayyora Qulmatova Abstract - This study uses machine learning models such as Multi-Linear Regression (MLR) and Holt-Winters Exponential Smoothing to model and forecast agricultural production indicators in Uzbekistan. The dataset consists of key agricultural indicators such as gross agricultural output, milk production, egg production, honey production, vegetables, fruits, and livestock products (in live weight). To improve model performance and ensure comparability, various data preprocessing methods such as StandardScaler, MinMaxScaler, RobustScaler, and Normalizer were used. The forecasting accuracy of each model was evaluated using standard error metrics such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percent- age error (MAPE). Empirical results show that the MLR model provides stable and interpretable forecasts, especially when combined with appropriate scaling methods. The Holt-Winters model exhibits strong performance for time series with consistent trends, but shows limitations when applied to variable data. The MLP model effectively captures nonlinear relationships and complex time patterns, although its performance is sensitive to data preprocessing, with MinMaxScaler generally yielding superior results. Overall, the results show that no model is universally optimal; instead, the choice of forecasting technique should be based on the characteristics of the data. The proposed modeling framework contributes to increasing the accuracy and reliability of agricultural forecasts and can support evidence-based policy planning and decision-making in the agricultural sector of Uzbekistan.
Open Zoom