# Estimation of Methane Gas Production in Turkey Using Machine Learning Methods
## 2 Materials and Methods
### Machine Learning
#### 2.2.1 Logistic Regression  
Logistic regression is used in classification problems to predict the outcomes of categorical dependent variables depending on one or more pre-indicator variables. It is a  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Output Variable** & **Input Variables** \\ \hline \multirow{8}{*}{Methane Gas Emissions (tons)} & \multirow{8}{*}{Agricultural Activities} & Number of Cattle (pcs) \\ \cline{3-3}  & & Red Meat Production (tons) \\ \cline{3-3}  & & Agricultural Area (hectare) \\ \cline{3-3}  & & Agricultural Greenhouse Gas Emissions (tons) \\ \cline{3-3}  & & Paddy Production (hectare) \\ \cline{3-3}  & & Share of Coal in Total Energy Production (\%) \\ \cline{3-3}  & Energy & Share of Natural Gas in Total Energy Production (\%) \\ \cline{3-3}  & & Industrial Production Index (2003 = 100) \\ \cline{3-3}  & Waste & Waste Greenhouse Gas Emission (tons) \\ \cline{3-3}  & & Population (Number of People) \\ \cline{3-3}  & & GDP (\$) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input/Output variables.  
\begin{table}
\begin{tabular}{l r r r r} \hline \hline  & **Average** & **Maximum** & **Minimum** & **SD** \\ \hline Methane gas & 48,868,492 & 63,988,980 & 40,945,943 & 7,131,161 \\ \hline Population & 68,251,387 & 83,614,362 & 53,921,758 & 9,039,080 \\ \hline GDP & 506,221,633,744 & 957,783,020,853 & 130,690,172,297 & 301,529,901,430 \\ \hline Coal share in electricity & 30.72 & 37.20 & 22.80 & 4.05 \\ \hline Natural gas share in electricity & 33.88 & 49.70 & 14.60 & 12.25 \\ \hline Industrial production index & 70.59 & 144.69 & 33.13 & 34.52 \\ \hline Number of cattle & 12,580,168 & 18,157,971 & 9,901,458 & 2,275,392 \\ \hline Agricultural area & 39,389,935 & 42,033,000 & 37,716,000 & 1,160,451 \\ \hline Agriculture greenhouse gas emission & 48,621,599 & 73,155,372 & 37,607,794 & 9,063,809 \\ \hline Waste greenhouse gas emission & 15,132,177 & 17,786,989 & 11,080,826 & 2,168,268 \\ \hline Cattle meat production & 614,882 & 1,341,445 & 303,120 & 313,293 \\ \hline Paddy production area & 82,125 & 126,419 & 40,400 & 29,149 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of independent variables.  
regression method that helps to make classifications and predictions. Logistic regression analysis is a method that calculates the estimated values of the dependent variable as probabilities and allows classification in accordance with probability rules [24].  
There are three basic methods in logistic regression: binary, ordinal, and nominal. Binary logistic regression is a logistic regression analysis with dependent variables containing two possible answers. Ordinal logistic regression is where the dependent variables have an ordinal scale and at least three categories. In nominal logistic regression, the dependent variable has a nominal scale and at least three categories [25].  
Odds ratio is used in logistic regression. Odds ratio (OR) is the ratio of the probability of success or occurrence \"P\" to the probability of failure or non-occurrence \"1-P\". Odds values take values within the range (0, +\(\infty\)). If two separate odds ratios are compared, the odds ratio is obtained. The odds ratio (OR) cannot be negative according to the formula and can be a value between 0 and infinity. When OR = 1, it can be said that the factor of interest (according to the reference) has no effect on increasing or decreasing the probability of the situation under investigation. When OR < 1, the factor of interest (according to the reference) has a decreasing effect on the probability of the situation under investigation. When OR > 1, the factor of interest (according to the reference) has an increasing effect on the probability of the investigated situation [26].