## 3 Methodology
### Exploratory Data Analysis  
The exploratory analysis could be described as the main process in the effort to create models, measure their performance and draw a conclusion regarding the research question of this work. The aim of this process is to explore all the important aspects that would provide a better understanding of the collected data and will support making decisions on the importance of each feature, testing various ML algorithms and observing the results to avoid overfitting and underfitting. Additionally, it is the most appropriate process to compare different standardization strategies, in other words secure the model from concept drift in future datasets. Python and the _Scikit-learn_ library [34] were used as the development environment for the experimentation process.  
The EDA process receives as input the data formulated at the end of the ETL process. The output of the EDA process takes the form of the information inferred by its sub-processes, which will enable the selection of the optimal feature set, the best performing algorithm and the most suitable optimizations. At this stage and before starting any data transformation, getting the quantile and the descriptive statistics of the engineered features, as shown in Tables 6 and 7 respectively, allows one to gain a better insight of the data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{8}{c}{**Quantile Statistics**} \\ \cline{2-10}  & **Min** & **5 th Perc.** & **Q1** & **Median** & **Q3** & **95 th Perc.** & **Max** & **Range** & **IQR** \\ \hline total\_gr.in\_gs & 1 & 4 & 16 & 24 & 31 & 48 & 60 & 59 & 15 \\ total\_success\_rounds\_in\_session & 1 & 1 & 2.5 & 7 & 11 & 12.1 & 14 & 13 & 8.5 \\ total\_win\_gr.points\_in\_gs & 5 & 11.6 & 43 & 97 & 167.5 & 323.3 & 361 & 356 & 124.5 \\ avg\_gr\_time\_in\_gs & 12.448 & 20.385 & 27.620 & 34.760 & 47.314 & 65.469 & 120.800 & 108.351 & 19.694 \\ avg\_gr\_time\_win\_gr\_in\_gs & 6 & 14.245 & 27.4 & 37 & 50.166 & 86 & 114.500 & 108.500 & 22.766 \\ rf\_decimal\_100 & 1.140 & 6.093 & 24.322 & 51.228 & 74.437 & 93.357 & 97.827 & 96.687 & 50.114 \\ puzzle\_lmp & 0.158 & 0.364 & 1.056 & 1.742 & 2.059 & 4.224 & 5.702 & 5.544 & 1.003 \\ mazzelmp & 0.069 & 0.277 & 0.555 & 0.936 & 1.179 & 2.219 & 2.289 & 2.219 & 0.624 \\ analikislimp & 0.194 & 0.292 & 0.740 & 0.779 & 1.480 & 1.519 & 1.588 & 1.363 & 0.740 \\ calcimp & 0.090 & 0.090 & 0.428 & 1.037 & 1.465 & 2.286 & 2.976 & 2.285 & 1.037 \\ naminglimp & 0.198 & 0.331 & 0.546 & 0.993 & 1.705 & 2.441 & 3.510 & 3.312 & 1.159 \\ soundImg & 0.106 & 0.156 & 0.424 & 0.848 & 1.590 & 2.932 & 6.734 & 6.628 & 1.166 \\ orientImg & 0.072 & 0.289 & 0.650 & 1.011 & 1.228 & 2.087 & 2.384 & 2.312 & 0.578 \\ langimp & 0.183 & 0.366 & 0.686 & 0.869 & 1.419 & 1.648 & 2.930 & 2.746 & 0.732 \\ logiclmp & 0.382 & 0.473 & 0.812 & 0.908 & 1.290 & 1.725 & 1.768 & 1.385 & 0.477 \\ memoryImg & 0.213 & 0.355 & 0.711 & 0.995 & 1.422 & 1.991 & 2.204 & 1.991 & 0.711 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantile statistics of the game-based engineered features.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Feature**} & **Data Type** & **Description** \\ \hline puzzlelmp & Real number \\ mazelmp & Real number \\ analikislimp & Real number \\ calclmp & Real number \\ namilmp & Real number \\ soumolmp & Real number \\ orientImg & Real number \\ langlmp & Real number \\ logclmp & Real number \\ memoryImg & Real number \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._