Today, machine learning is utilized in several industries, including tourism, hospitality, and the hotel industry. This project uses machine learning approaches such as classification to predict ...hotel customers’ loyalty and develop viable strategies for managing and structuring customer relationships. The research is conducted using the CRISP-DM technique, and the three chosen classification algorithms are random forest, logistic regression, and decision tree. This study investigated key characteristics of merchants’ customers’ behavior, interest, and preference using a real-world case study with a hotel booking dataset from the C3 Rewards and C3 Merchant systems. Following a comprehensive investigation of prospective preferences in the pre-processing phase, the best machine learning algorithms are identified and assessed for forecasting customer loyalty in the hotel business. The study's outcome was recorded and examined further before hotel operators utilized it as a reference. The chosen algorithms are developed utilizing Python programming language, and the analysis result is evaluated using the Confusion Matrix, specifically in terms of precision, recall, and F1-score. At the end of the experiment, the accuracy values generated by the logistic regression, decision tree, and random forest algorithms were 57.83%, 71.44%, and 69.91%, respectively. To overcome the limits of this study method, additional datasets or upgraded algorithms might be utilized better to understand each algorithm's benefits and limitations and achieve further advancement.Â
Vocational high schools are one of the educational stages impacted by Indonesia's low quality of education. Vocational High Schools play a crucial role in improving human resources. Graduates of ...Vocational High Schools can continue their education at universities or enter the workforce directly. Many students are found to have not yet considered their career path after graduation. At the same time, graduates are still expected to find mismatched employment with their expertise and skills. This research uses CRISP-DM, or Cross Industry Standard Process for Data Mining, to build machine learning models. The approach used is content-based filtering. This model recommends items similar to previously liked or selected items by the user. Item similarity can be calculated based on the features of the items being compared. After students receive job recommendations through intelligent job matching, they can use these recommendations as references when applying for jobs that align with their results. This process helps students direct their steps toward finding jobs that match their profiles, ultimately increasing their chances of success in the job market. These recommendations are crucial in guiding students toward career paths that align with their abilities and interests. The Intelligent Job Matching Model developed in this research provides recommendations for the job-matching process. This model benefits graduates by providing job recommendations aligned with their profiles and offers advantages to the job market. By implementing the Model of Intelligent Job Matching in the recruitment process, applicants with job qualifications can be matched effectively.
This article presents a model based on machine learning for the selection of the characteristics that most influence the low industrial yield of cane sugar production in Cuba. The set of data used in ...this work corresponds to a period of ten years of sugar harvests from 2010 to 2019. A process of understanding the business and of understanding and preparing the data is carried out. The accuracy of six rule learning algorithms is evaluated: CONJUNCTIVERULE, DECISIONTABLE, RIDOR, FURIA, PART and JRIP. The results obtained allow us to identify: R417, R379, R378, R419a, R410, R613, R1427 and R380, as the indicators that most influence low industrial performance.
Total household energy expenditures are a complex topic because so many behavioral, technological, environmental, and policy variables can affect expenditures. This study aimed to develop a ...high-performance ensemble learning (EL) model to classify total household energy expenditures. For this purpose, household consumption data from 11,521 households were examined using the Household Budget Survey 2019 data set that the Turkish Statistical Institute (TURKSTAT) published. In addition to the variables directly related to household energy expenditures, new variables were created within the framework of the literature and under the guidance of expert opinion. The prepared data were passed through data preprocessing, modeling, prediction, and performance evaluation stages using the open source RapidMiner software program. Classification performances of machine learning and EL methods were compared. Aside from k-nearest neighbor, decision tree, naive Bayes, random forest, gradient boosted trees, and DFNN classifiers, the study used bagging, boosting, voting, and stacking EL methods. The stacking EL method in the ALL model and bagging EL method in the deep feed forward neural network (DFNN) classifiers achieved the highest performance among EL methods. The accuracy value of the stacking and bagging methods was 0.984. The results indicate that EL methods can enhance individual machine learning methods significantly.
•A new approach is presented for prediction of total household energy expenditures.•Boosting, bagging, voting and stacking EL methods aside from ML methods were used.•The classification performances of EL and ML methods were compared.•The stacking and bagging EL method provided the highest performance.•The results were found to very highly accurately classification.
Nitrogen oxides (NOX) emissions that are caused by road traffic diesel engines affects public health. The existing instantaneous emissions models are often imprecise due to the lack of knowledge of ...highly non-linear processes behind real-world emissions and they do not include meteorological and driving volatility variables. This paper applied data mining techniques based on the Cross Industry Standard Process for Data Mining (CRISP-DM) method to a dataset of four diesel Euro 6 passenger cars tested in real-world driving conditions to: a) model stabilised hot NOX emissions based on kinematic (speed), internal engine (engine coolant temperature, engine load, engine speed, intake air temperature, manifold absolute pressure and mass air flow), meteorological (humidity) and driving volatility (acceleration and vehicular jerk); b) compare the performance of different machine learning (ML) techniques in predicting NOX emission rates, namely: Artificial Neural Networks (ANN), Random Forest (RF), and Gradient-Boosted Trees (GBT). The model that utilizes a set of detailed variables, particularly engine coolant temperature, engine load, engine speed, intake air temperature, humidity, acceleration and vehicular jerk, and using ANN technique was better able to deal with variability in emission data than models based on a single set of these variables. It was also found that models produced high Root Mean Square Error due to their inability in predicting high peaks in measured emission data. The presented models rely on fast inference times and can therefore be deployed for engine control units to inform drivers about their NOX emissions during driving.
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