## 4 Results
### Binary Classification for Predicting Tourism Class  
The proposed machine-learning models can be used to predict an individual class among the huge LBSN-based heterogeneous data, which are given in this section. In the current research, we used the proposed models to predict tourism venues with the help of supervised methods from our previously used dataset from Weibo. These venues are predicted based on the proximity to the information provided in the dataset, including the gender of the specific user, previously visited venues within a particular time of the day, day of the week, latitude/longitude, and venue names. The results presented in this section provide evidence of the efficiency and effectiveness of using machine learning for extracting useful traits and patterns in the behavior of users, more specifically, tourists. This information can be used to conduct useful research in predicting the preferences of tourists, as presented in our research. Figure 3 represents the ROC curve, also known as AUC. The ROC curve is a graphical representation of the performance of a binary classifier system as its discrimination threshold is varied. The ROC graph is a plot of the true positive rate (TPR) against the false positive rate (FPR) for all possible threshold values. The TPR is the proportion of actual positive samples that are correctly classified as positive, while the FPR is the proportion of actual negative samples that are incorrectly classified as positive. The ROC graph plots TPR against FPR as the discrimination threshold of the classifier is varied, and the resulting curve provides a visual representation of the trade-off between the TPR and FPR. In the ROC graph, the TPR is plotted on the y-axis and the FPR is plotted on the x-axis. A classifier with a perfect performance will have a TPR of 1.0 and an FPR of 0.0 and will be located at the top-left corner of the graph. A classifier with a poor performance will have a TPR that is close to 0.0 and an FPR that is close to 1.0 and will be located close to the bottom-right corner of the graph. AUC values greater than 0.9 represent excellent results; values from 0.8 to 0.9 are ranked as good, 0.7 to 0.8 are fair, and AUC values less than 0.6 are considered poor [57]. Figure 4 shows high values of the AUC for our models, and Figure 5 suggests a high accuracy in the prediction of tourism venues among all other types available in the LBSN dataset. Figure 6 shows a different aspect of the results in predicting the tourism venues. The deep-learning model attained maximum accuracy for predictions, representing a 99% prediction accuracy, followed by logistic regression with 91% and a generalized linear model and gradient-boosted trees showing 85% and 75% accuracy, respectively. There are multiple reasons contributing to the high accuracy of these models. For example, the supervised learning methods used in this analysis, which use labeled data for training and generally produce more accurate results as compared to unsupervised machine-learning models. Another reason may be the huge number of instances in the dataset as most of the check-ins may be recoded from similar venues, and also the names of the locations often include terms like schools, ports, parks etc., which helps in identifying the venue class