## 3 Modeling and Results  
### Dataset Building  
For the fields of the original data, in order to operate normally and perform better in the Random Forest model, it is necessary to formulate dataset feature engineering rules under the premise of meeting the requirements of rapid evaluation (Table 3). When processing single text type fields, such as \"Royalty Type\", this article performed dummy variable processing based on attribute classification. For multitext type fields such as \"Deal Type\", we count based on the number of text per value. As to discrete type (no size meaning) fields, such as \"Owner Working Capital\", we performed one-hot encoding processing on them. When dealing with discrete type (size meaning) fields such as \"Country Risk Score Overall Current\", it performed value mapping processing on them.  
The following describes how to extract other special field features.  
1. [label=(0)]
2. Geologic Ore Body Type Zone  
For the field, Geologic Ore Body Type Zone is classified according to the gold ore genesis aggregation and dumb variable treatment (Table 4).  
\begin{table}
\begin{tabular}{c c} \hline
**Geologic Ore** & **Type** \\ \hline Saddle Reefs & \begin{tabular}{c} Orogenic \\ \end{tabular} \\ Mesothermal Lode Gold & \begin{tabular}{c} Orogenic \\ \end{tabular} \\ Vein Hosted & \begin{tabular}{c} Orogenic \\ \end{tabular} \\ Intrusive Related & \begin{tabular}{c} Intrusive Related \\ \end{tabular} \\ Disseminated & \begin{tabular}{c} Intrusive Related \\ \end{tabular} \\ Alkali Intrusion & \begin{tabular}{c} Intrusive Related \\ \end{tabular} \\ Granite Related & \
\begin{tabular}{c} Granite Related \\ \end{tabular} \\ Layered Mafic-Ultramafic Intrusion &
\begin{tabular}{c} Intrusive Related \\ \end{tabular} \\ Skarn (Metasomatic) & Skarn \\ Carbonate Replacement (incl Manto) & Skarn \\ IOCG Breccia Complex & IOCG \\ Iron Oxide Copper Gold (IOCG) & IOCG \\ Replacement & Replacement \\ Proterozoic Quartz Pebble Conglomerate & Placer \\ Paleoplacer (Buried) & Placer \\ Placer (Alluvial) & Placer \\ Placer (Beach) & Placer \\ \hline \end{tabular}
\end{table}
Table 4: Classification of gold mine types [41; 42].  
\begin{table}
\begin{tabular}{c c} \hline
\begin{tabular}{c} Attribute aggregation, addition, multiple overlay \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 3: Data processing rules.  
(2) Max (Interval (feet))  
Since the drilling footage data are not updated every year, if the data of a single year are taken, there will be many null values, so the five-year data of Interval (feet) from 2018 to 2022 is taken as the maximum value to generate a new field Max (Interval (feet)).  
(3) Development Stage label division  
According to the official interpretation of the Development Stage field (Table 5), gold mining projects are classified into \"already put into production\" status (Mine-Stage, Commissioning, Production, Operating, Satellite, Limited, Preproduction) and \"not put into production\" status (Late-Stage, Reserves Development, Advanced, Exploration, Prefeasibility/Scoping, Feasibility, Feasibility Started, Feasibility Complete, Construction Planncd, Construction Started, Expansion, Residual, Closed).  
Therefore, 4689 projects were selected to constitute the original dataset of global gold mining projects, of which 1330 projects were put into production, and 3359 projects were not put into production. According to the average time limit of 7 years from the feasibility stage to the production stage of the gold mine, we take the projects that have been put into production as positive samples and the projects that have been researched but not put into production before 2015 and 2015 as negative samples. Then, randomly combine the positive sample project data and negative sample project data to form a training dataset and a verification dataset, a total of 911 projects.