Modeling agricultural and methane emission data: a finite mixture regression approach

Abstract

In this paper, the method for unsupervised learning of finite mixture regression (FMR) models is presented for evaluation using agricultural and emissions data sets. The FMR models can be written as problems with incomplete data, and the expectation–maximization (EM) algorithm can be used to estimate unknown variables. The goals of this research are to find the best clustering model with different sets of training and test data and examine the relationship between crop production index and methane emissions in 22 countries from 1990 to 2019 using FMR. In this study also use machine learning process for a FMR model from real world data. According to the findings, the performance of the random training data (RDM) in time series is preferable to that of the fixed training data (FXM). In addition, both RDM and FXM are capable of classifying the 22 countries into two distinct groups and constructing the parameters for the regression model. However, selecting training and test data will result in a good prediction; it is dependent on the data collected. Picking the right training and test data is crucial for accurate predictions-it all comes down to having good data in the first place.