# Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
## 4 Results and Discussion
The study results were made easy to understand using data visualisation techniques. Data visualisation helps to render the features in the dataset more visible and make the dataset more understandable. This study explored the relationship between the predicted and actual data by employing scatter and line plots. A scatter plot is used to explore how two values change in relation to each other across two dimensions. With this diagram, although the reason for the relationship cannot be understood, the presence and strength of a relationship between two variables can be  
Figure 4: MSE analysis. Figure 5: MAE analysis. determined, facilitating classification into categories such as strong positive, weak positive, strong negative, weak negative, or no relationship [62]. Figure 7 shows the scatter plot curves for the ANN, RF regression, SVR, and time series methods. Figure 6: MAPE analysis. Figure 7: Scatter plots of methods. Figure 7 shows scatter plot graphs comparing the actual average time spent on the internet with the predicted values generated by the machine learning models. On these plots, the \(x\)-axis represents the actual values, while the \(y\)-axis represents the predicted values. Each point on the graph corresponds to a specific data sample. Scatter plots serve as an excellent tool for the analysis of the relationship between variables, as well as detecting patterns, highlighting outliers, and assessing the data distribution. The figures indicate a strong positive correlation between the predicted and actual values; as one variable increases, so does the other, resulting in points that largely cluster around the line. A line chart serves as an effective tool in illustrating temporal trends. In this type of visualisation, the dimension, such as time, is invariably plotted along the \(x\)-axis, while the calculated values occupy the \(y\)-axis. To construct a line on the chart, the dataset must include a minimum of two data points. In contrast, a dataset containing only a single value is depicted merely as a point. They are graphs used to compare the mean values obtained from continuous variables of different categorical variables [63]. Figure 8 shows the line plots for the ANN, RF regression, SVR, time series methods. Figure 8 shows the comparative analysis between the actual and predicted values concerning the average time spent on the internet, evaluated using several machine learning models. The \(x\)-axis of these line plots signifies the time interval, while the \(y\)-axis represents the normalised average time spent online. In these visual representations, the red lines denote the actual measurements, whereas the blue lines depict the predicted estimates. Figure 8: Line plots of methods. The figures collectively suggest that the methodologies employed exhibit a high degree of predictive accuracy. Among these, it is evident that the ANN offers the most precise alignment with the actual data, followed sequentially by SVR, RF, and the time series. This observation underscores that the ANN approach demonstrates a superior fit and predictive capabilities compared to its counterparts. In 2014, the average daily duration spent on the internet amounted to 277 min, increasing to 417 min by 2024. This study utilised machine learning models to estimate the average time devoted to internet use. According to the results of this model, it is calculated that it will reach 507 min in 2030 and 603 min in 2035. The results showed that the artificial neural network (ANN) model provided the highest accuracy rate and was superior to the other models in predicting internet usage times. The ANN model was characterised by low error rates due to its ability to work with complex data structures. Although random forest (RF), support vector regression (SVR), and time series also provided high accuracy rates, the superiority of the ANN in terms of the accuracy and error metrics made a significant difference. These findings suggest that the ANN is a model that should be particularly favoured in the prediction of time series data.