DATA ANALYSIS AND FORECASTING IN AGRICULTURAL ENTERPRISES

In economic-statistical modeling, the use of a linear regression model gives high results in the analysis of the relationship between economic indicators and production factors. The article analyzes the relationship between the volume of agricultural production and agricultural techniques using multiple regression models. As we know, it is important to get a better result for the decline of multiple regression. It is important to normalize the data set using feature-scaling techniques to get a good accuracy of the results. In this experiment, we also worked with scaling the dataset. First, we tried standard scalers algorithm and calculated the loss using the mean square error. In this way we achieved an accuracy of regression of mean square error 2.48, residual (between and ) sum of square is 6.16 and mean square error 1.03 terms. This work proves what can be predicted from multiple regressions and provides a guide to the impact of techniques on agricultural production.

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