## 3 Methodology
### Factorial-Analysis-Based Feature Selection  
Feature selection proposes a simple and effective technique to eliminate redundant and irrelevant data. Removing the unrelated attributes improves accuracy, reduces the computation time, and facilitates an enhanced assuming of the learning model. Factor analysis is a technique used to reduce many variables to a few factors. This method  
Figure 1: The general system architecture of the proposed method. calculates all variables' most common variance levels and scores them. Factorial analysis has several possible approaches. In this paper, we used the most common method for the principal component analysis to extract factors from the dataset. PCA extracts the maximum variance and sets these values as the first factor [35]. The variance explained by the first factor is then calculated, after which the maximum variance of the second factor is calculated. This process ultimately moves to the last element. This model led to a determinant of 0.022, no multicollinearity, a KMO factor of 0.541 compatible sample sizes, and significance on the 0.001 level (alpha \(<\) 0.005), allowing rejection of the null hypothesis and therefore showing a correlation between the variables. Here, we utilized a factor analysis. Table 1 shows the descriptive statistics for all features, including the means, standard deviations, and the community value extraction method by PCA of the components used in the factor analysis. The number of cases was N = 69,831. Given that PCA is an iterative evaluation process, it starts with one as the initial estimate of the commonality (this is the total variability of all seven components for our dataset) and then continues the analysis until the initial collaboration is found. This means that the initial value on the diagonal of the correlation matrix is determined by the axial factoring of the main factor in terms of the square of the variable with the other attributes. These results are shown as the percentages of features of the values explained by the corresponding coefficient for the given variable. In other words, we find that the factor model defines about 83.2% of the variation in CH4. Likewise, 99.4% of the CavityPressure, 89.9% of the EtalonTemp, 88.0% of the WarmBoxTemp, 86.6% of the OutletValve, and 79.9% of the DasTemp are explained by our factor model. These results present the best outcome of explaining the variation in the CavityPressure, CavityTemp, DasTemp, EtalonTemp, WarmBoxTemp, OutletValve, WIND_N, and CH4 factors in the factor analysis. Hence, they appear as percentages of the features of the value explained by the coefficient for the variable. We demonstrate that the factor model explains approximately 83.2% of the variation in CH4. Likewise, 99.4% of the CavityPressure, 89.9% of the EtalonTemp, 88.0% of the WarmBoxTemp, 86.6% of the OutletValve, and 79.9% of the DasTemp are explained by our factor model. The results present the best outcome of explaining the variation in the CavityPressure, CavityTemp, DasTemp, EtalonTemp, WarmBoxTemp, OutletValve, WIND_N, and CH4 in the factor analysis. We sought to determine values that are close to the initial values. This model shows that most of the features of these variables are explained. In this case, the model is better for some variables than others. The model best describes CavityPressure, DasTemp, EtalonTemp, WarmBoxTemp, OutletValve, and CH4, with the outcomes not bad for other vari