## 3 Modeling and Results
### Results and Accuracy Verification  
After data cleaning and feature engineering of the original data, the miceforest method was used to use 30 rounds of imputation iterations for the missing data. After the graphical diagnosis, it can be seen that the mean of the four variables with missing values gradually converges after multiple imputations (Figure 5). In the distribution line plot, it can be observed that the original data after imputation fit well with the distribution of the imputed data (Figure 6). By randomly splitting the dataset into training and validation sets in a 7:3 ratio, 638 (70%) samples were randomly selected from the dataset of the gold mining project as the training set, and the remaining 273 (30%) samples were used as the validation set to  
Figure 4: Technical route. Figure 5: Convergence results during variable interpolation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Development Stage** & **Description** \\ \hline \multirow{5}{*}{Already put into production} & Mine-Stage & Project that has made a decision to move forward with production. \\  & Commissioning & The mine is commissioned and production has started. \\  & Production & Commercial production has been achieved. \\  & Operating & The mine is fully operational. \\  & Satellite & A satellite of a central processing complex. \\  & Limited & Some ore and/or commodity is being produced. Closure. \\  & Preproduction & Ago-ahead decision has been made and the project is being readied for production. \\ \hline \multirow{5}{*}{Not put into production} & Late-Stage & Project with a defined resource that has not yet reached a production decision. \\  & Reserves Development & An initial reserve/resource has been calculated. \\  & Advanced & Drilling is being completed to add additional reserves/resources. \\  & Exploration & Project is in the exploration phase. \\  & Prefeasibility/Scoping & Usually an in-house assessment that includes mining and proccessing methods, \\  & & capital costs, NPV, IRR. \\  & Feasibility & Bankable feasibility study is underway to determine economic viability. \\  & Feasibility Started & Feasibility report has commenced. \\  & Feasibility Complete & Feasibility report is complete. \\  & Construction Planned & Construction is planned for the property \\  & Construction Started & Construction has begun at the property \\  & Expansion & Operator is engaged in an active capital expansion of the facilities. \\  & Residual & The operator has stopped mining ore and is leaching the residual ore. \\  & Closed & Operation has stopped, in many cases because the ore has been exhausted. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: S&P Capital IQ official explanation of the Development Stage. construct the Random Forest model. After modeling and ten-fold cross-validation of the model, the model training accuracy was 95.92%, and the model test accuracy was 93.80%. While the model training accuracy was 97.02%, and the model inspection accuracy was 95.99% after training modeling on the miceforest imputation dataset. Compared with the original data, the model accuracy of the filled dataset is improved by 2.19%. The true positive rate of the model has also increased from 96.07% to 97.80%. The ROC (Receiver Operating Characteristic Curve) is used to evaluate model accuracy. Obviously, we can see that the model using imputed data has higher accuracy (Figure 7) than the model using original missing value data (Figure 8). Figure 8: Missing values are filled in case of Random Forest modeling ROC. This blue dotted line represents a random guess line, representing the performance level of the model in random prediction situations. Figure 6: Data distribution and raw data distribution after missing values are filled, the red line represents the original data, while the black line depicts the imputed value for each dataset. Figure 7: Random Forest modeling ROC without filling with missing values.