###### Abstract
This modelling error happens in complex models when the higher the variance is, the lower the bias is. Machine learning can also be affected by overfitting. In machine learning overfitting refers to good performance on the training data, but poor generalization to other data. Nevertheless, in machine learning there exist several techniques to prevent overfitting: early stopping, training with more data, data augmentation, feature selection, regularization, etc. Machine learning models are able to obtain a high prediction accuracy with almost any type of data and perform classification efficiently [14]. An important advantage of machine learning models is that their hyperparameters can be highly tuned to improve the prediction. Furthermore, once the basic machine learning models are trained, the researcher can identify which is the best one for a particular dataset. In addition, advanced machine learning models like deep learning algorithms show very good results with unbalanced datasets and Big Data [14]. Deep learning is a promising class of machine learning models that has become a popular subject in the field of science. Deep learning has been used successfully for signal processing, pattern recognition, and statistical analysis. Many researchers have shown that neural networks outperform econometrics models in forecasting accuracy. In [15], tourism data was used to forecast the arrival of tourists from USA to Durban (South Africa). It was shown that neural networks perform better than exponential smoothing, ARIMA, multiple regression, and genetic regression models. In [16], the application of three time-series forecasting techniques, namely exponential smoothing, univariate ARIMA, and [PERSON]'s Model of Artificial Neural Networks (ANN), was investigated to predict travel demand (i.e., the number of arrivals) from different countries to Hong Kong. The results show that neural networks are the best method for forecasting visitor arrivals, especially those series without obvious patterns. We selected a machine learning algorithm to predict beach attendance due to the efficiency of these algorithms for classification purposes. Particularly, a deep learning algorithm (deep neural network) was selected due to the higher performance of neural networks for prediction compared to econometrics and its promising results in the field of science. The contributions of this paper are summarized as follows:  
* We propose to use IoT data and deep learning to estimate beach attendance. * A deep learning (fully connected deep neural network) algorithm was developed for beach attendance prediction. * Beach attendance is predicted based on the following attributes: the time of day, the day of the week, the season, and the weather conditions. We use these attributes as the seven input variables for training our proposed deep neural network (DNN) model. * The last attribute (number of beach visitors), the output variable, is taken as the ground-truth of the target attribute for model training. Images from cameras are analyzed to estimate the number of beach visitors using an image recognition software. This output is used to train the deep neural network during the training phase (using the training set); it is also used later to test the model (using the testing set) in terms of accuracy and mean absolute error (MAE). * Our proposed deep learning classifier outperforms other machine learning models (decision tree, \(k\)-nearest neighbors, and random forest) and can successfully differentiate between seven beach occupancy levels, with the Mean Absolute Error (MAE), accuracy, precision, recall and F1-score of 0.03, 92.7%, 92.9%, 92.7%, and 92.7%, respectively. To the best of our knowledge, our proposal is the first deep learning algorithm for beach attendance prediction. The experimental results prove its feasibility. The paper is structured as follows. Section 2 discusses the related work. Section 3 introduces the proposed deep neural network model and the dataset.