Comparison of Machine Learning Algorithms in Restaurant Revenue Prediction
In this work we analyze the business attractiveness of cities from the franchise point of view. We select factors that theoretically influence attractiveness of the city in terms and show how to estimate them. We suggest some classical machine learning algorithms (ordinary least squares regression, elastic net, support vector regression, and random forest). We show results of empirical study and discuss what factors are the most important in the case.
In this paper, we address several aspects of applying classical machine learning algorithms to a regression problem. We compare the predictive power to validate our approach on a data about revenue of a large Russian restaurant chain. We pay special attention to solve two problems: data heterogeneity and a high number of correlated features. We describe methods for considering heterogeneity — observations weighting and estimating models on subsamples. We define a weighting function via Mahalanobis distance in the space of features and show its predictive properties on following methods: ordinary least squares regression, elastic net, support vector regression, and random forest.
The 25th International Conference on Computing in High Energy and Nuclear Physics (CHEP), organised by CERN, took place as a virtual event from 17–21 May 2021. The conference attracted 1144 registered participants from 46 different countries. There were 207 scientific presentations made over the 5 days of the conference. These were divided between 30 long talks and 2 keynotes, which were presented in plenary sessions; and 175 short talks, which were presented in parallel sessions.
There is a diverse variety of demographic data that can be analyzed with modern methods of data mining to achieve better results. On the one hand, the main chosen task is to compare different methods for the next event prediction and gender prediction, on the other hand, we pay special attention to interpretable patterns describing demographic behavior in the studied problems. There were considered interpretable methods as decision trees and their ensembles and semi- or non-interpretable methods, such as the SVM method with different customized kernels tailored for demographers' needs and neural networks, respectively. The best accuracy results were obtained with two-channel Convolutional Neural Networks.
In this study we develop a model for early box office receipts forecasting that, in addition to traditionally used regressors, uses several inputs that have never been used before, but appeared to be very useful predictors according to our variable importance analysis. New predictors account for the power of actors and directors, as well as for the intensity of competition at the time of movie release. Instead of Motion Picture of Association of America (MPAA) ratings commonly used in movie success prediction, textual information about the reasons for giving a movie its MPAA rating was formalized using word frequency and principal components analyses. The expert system is based on the Random forest algorithm, which outperformed a stepwise regression and a multilayer perceptron neural network. A regression tree-based diagnostic approach allowed us to detect the heterogeneity of model accuracy across segments of data and assess the applicability of the model to different movie types.
The article discusses the utility of automated revenue management systems, designed for the needs of civil aviation. The analysis of revenue management systems in Russian airlines and Russian carrier position in the international air transportation market are provided. The need to use the automated revenue management system to increase profits of the operator is identified. A strategy of selling tickets, taking into account overbooking as revenue management strategy, allowing both to increase employment of passenger seats and reduce the loss of the carrier from possible no-show passengers to register, is considered. Creation of the simulator to assess the effectiveness of the commercial activities of the airline is proposed.
The article discusses the need for a booking-limit strategy as effective methods of increasing the yield of airline, allowing to increase employment of passenger seats with a possible no-show passengers to the registration. The introduction of this strategy into practice is impossible without compliance with the rules of execution of contract of carriage entered into between the passenger and the carrier.