# Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses
## 4 Results
### Selection of Significant Variables Using Supervised Learning Models  
The variables' importance rank of the 22 independent variables based on RF, DT and NB techniques are shown and compared in Figure 11. The present study takes advantage of various feature selection methods to pick only the important variables and design the prediction model according to selected variables. The core motive behind decreasing the number of variables (based on their level of importance and correlations) is to diminish the complexity and promote the applicability of our final model. Therefore, after implementing unsupervised clustering and identifying the correlation of the variables, we also compared the variables' importance based on three different tree-based supervised machine learning techniques. Table 3 presents the variable weights using outputs of RF, DT and NB. The mutually important variables were detected. For example, monthly income, age and private vehicle ownership were variables with high weights in all three methods.  
Furthermore, to make a more vivid conclusion from the three feature selection methods, the weight values of each variable were summarized and compared, as shown in Figure 12. Next, the amassed weight values were ranked from highest to lowest. According to Figure 12, there was a significant drop in weight values after the \"Gender\" variable. Therefore, we drew a line and deselected variables whose weights were below the line. The results of the selected most important variables based on three different ML techniques is summarized in Table 4.  
Further random forest modelling was performed using these 11 variables. Moreover, all these variables have a threshold of MeanDecreaseGini higher than 30.  
Figure 11: Importance score (weight) of variables based on three ML methods.  
Figure 12: Accumulated weights of variables.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **RF** & \multicolumn{2}{c}{**DT**} & \multicolumn{2}{c}{**NB**} \\ \hline
**No.** & **Attribute** & **Weight** & **Attribute** & **Weight** & **Attribute** & **Weight** \\ \hline
1 & Camp.mod/d & 0.1825 & Camp.mod/d & 0.14634 & Private vehicle & 0.059752 \\
2 & Smooth.Surf & 0.1409 & Age & 0.10431 & Greenery & 0.058748 \\
3 & Greenery & 0.1151 & Greenery & 0.09712 & Connectivity & 0.056134 \\
4 & Cam.tra.time/d & 0.0777 & Cam.tra.cost/d & 0.08648 & Gender & 0.04504 \\
5 & Cam.tra.cost/d & 0.0547 & Monthly income & 0.06964 & Monthly income & 0.041161 \\
6 & Travel mode & 0.0538 & Cam.tra.time/d & 0.06434 & Cam.tra.time/d & 0.040235 \\
7 & Age & 0.0534 & Travel mode & 0.0588 & Travel mode & 0.037877 \\
8 & Monthly income & 0.0509 & Connectivity & 0.05861 & Age & 0.037555 \\
9 & Gender & 0.0498 & Gender & 0.05055 & Camp.mod/d & 0.032029 \\
10 & Private vehicle & 0.0490 & Private vehicle & 0.04992 & Sep.lane & 0.025078 \\
11 & Camp.Hrs/d & 0.0477 & e-hailing & 0.04938 & Cam.tra.cost/d & 0.022726 \\
12 & on-road.Lane & 0.0469 & Camp.Hrs/d & 0.04726 & e-hailing & 0.021732 \\
13 & No-Lane & 0.0429 & Race & 0.0454 & on-road.Lane & 0.021263 \\
14 & Connectivity & 0.0415 & Sep.lane & 0.04247 & No-Lane & 0.020533 \\
15 & Race & 0.0376 & on-road.Lane & 0.04189 & Camp.Hrs/d & 0.019974 \\
16 & Education & 0.0374 & Position & 0.03798 & Capm.Tra/d & 0.016223 \\
17 & Position & 0.0366 & Smooth.Surf & 0.03751 & Status & 0.015691 \\
18 & Capm.Tra/d & 0.0351 & Education & 0.0374 & SMS Membership & 0.011859 \\
19 & Status & 0.0308 & Status & 0.03386 & Position & 0.010624 \\
20 & e-hailing & 0.0280 & Capm.Tra/d & 0.03175 & Race & 0.010495 \\
21 & SMS membership & 0.0268 & SMS Membership & 0.02767 & Smooth.Surf & 0.010309 \\
22 & Sep.lane & 0.0248 & No-Lane & 0.02676 & Education & 0.0082 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Importance score (weight) of variables based on three ML methods.