## 4 Analysis of Results  
We will show experimental result in this section mainly in forms of tables and graphs. Table 6 shows selected features according to learning model and the corresponding prediction accuracy for each location category. Prediction accuracy is represented as 100% minus SMAPE. Random Forest and Stacking uses the same set of feature since feature selection was done with Random Forest. The abbreviations for each machine learning algorithm is as follows:  
* **RF**: Random Forest
* **XGB**: XGBoost
* **STK**: Stacking  
From the results, of course, selected features from Random Forest and XGBoost are overlapped but not in total. This is due to the purpose of learning between these two models. From the view of big data, feature selection can reduce noise and reduces the effect of overfitting with increased accuracy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Location Category** & **Learning Algorithm** & **Selected Features** & **Accuracy (\%) (100-SMAPE)** \\ \hline \multirow{3}{*}{Foreign Institutions} & RF & O, C, A & 3.07 \\ \cline{2-4}  & STK & & 1.77 \\ \cline{2-4}  & XGB & O, C, Salary & 2.72 \\ \hline \multirow{3}{*}{Retail Business} & RF & & 50.89 \\ \cline{2-4}  & STK & O, A, N & 58.50 \\ \cline{2-4}  & XGB & O, A, Salary, journey, SNS1, Hobby & 43.76 \\ \hline \multirow{3}{*}{Service industry} & RF & O, C, A, & 58.16 \\ \cline{2-4}  & STK & Edu, Salary & 57.19 \\ \cline{2-4}  & XGB & Edu & 61.50 \\ \hline \multirow{3}{*}{Restaurant} & RF & O, C, E, A, & 75.60 \\ \cline{2-4}  & STK & Religion, Hobby & 84.19 \\ \cline{2-4}  & XGB & C, E, N, Religion, C, time & 76.15 \\ \hline \multirow{3}{*}{Pub} & RF & O, A, Edu, & 63.62 \\ \cline{2-4}  & STK & Salary, SNS2 & 66.43 \\ \cline{2-4}  & XGB & O, E, A, Edu, Transport, Journey, SNS2 & 58.84 \\ \hline \multirow{3}{*}{Beverage store} & RF & E, N, Religion, & 69.59 \\ \cline{2-4}  & STK & Salary, SNS2, Hobby & 78.75 \\ \cline{2-4}  & XGB & E, A, N, Hobby & 52.30 \\ \hline \multirow{3}{*}{Theater and Concert Hall} & RF & E, N, Religion & 63.62 \\ \cline{2-4}  & STK & Salary & 57.25 \\ \cline{2-4}  & XGB & E, N, Religion, Salary, Transport & 69.39 \\ \hline \multirow{3}{*}{Institutions of Education} & RF & O, C, E, A, N, & 75.79 \\ \cline{2-4}  & STK & Religion & 80.90 \\ \cline{2-4}  & XGB & E, N, Journey, SNS2 & 67.14 \\ \hline \multirow{3}{*}{Hospital} & RF & C, E, A, & 10.44 \\ \cline{2-4}  & STK & Edu, Salary & 14.48 \\ \cline{2-4}  & XGB & Edu, Journey & 15.15 \\ \cline{2-4}  & RF & & 41.18 \\ \cline{2-4}  & STK & O, A, Journey & 45.35 \\ \cline{2-4}  & XGB & O, A, Journey & 43.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of Feature Selection. In addition, Figure 3 shows that Stacking with feature selection resulted in high prediction accuracy. Maybe aggregation of five different models of Stacking reduces noise of data. In case of foreign institutions and hospital which has very small number of visit, five different models may lead to low accuracy in level 1 of Stacking and the aggregated results lead to low accuracy. It is notable that several of BFF were always included in selected features. It is proven that personality is highly related with visiting places. Table 7 shows the results numerically: hyperparameter optimization and prediction accuracy. In Table 7, hyperparameter value of RF are chosen as n_estimators, max_depth, bootstrap, respectively and in case of XGB, n_estimators, max_depth, learning_rate, booster are shown. It is notable that feature selection leads to accuracy decrease however prediction accuracy can be increased with hyperparameter optimization. For most of Random Forest optimization, bootstrap is used for the most of location categories. Bootstrap is useful for smaller number of input data. In addition, different method of optimization for the same location categories leads to similar value of max_depth or n_estimators. Interestingly, different hyperparameters can lead to similar accuracy.