Title: Support the creation of appropriate tourism offers by finding a model, using machine learning algorithms, to forecast spending by tourists

Authors: Ana Ktona; Etleva Muça; Denada Çollaku; Irena Shahini; Irena Boboli

Addresses: Natural Sciences Faculty, Informatics Department, Tirana University, 1001, Tirana, Albania ' Faculty of Economy and Agribusiness, Department of Economy and Rural Development Policies, Agricultural University of Tirana, Tirana, Albania ' Natural Sciences Faculty, Informatics Department, Tirana University, 1001, Tirana, Albania ' Sigal Uniqa Group Austria, First Branch Durres, Business Center 'Monun', 2001, Durrës, Albania ' Eqerem Ҫabej University, Gjirokastra, Albania

Abstract: Tourism in Albania is one of the potential pillars of economic development, offering real opportunities for GDP growth and employment. New technology development and the digital transformation of society have led to tourism in the upper social and economic dimensions. Technology can make an impact by improving tourist experiences through: integration of generation mobile, integration of IoT, data evaluation, reputation and promotion. This study presents the potential for using technology and computer science applications in finding models to forecast tourist expenditure. These models can be a support in creating appropriate tourism offers. Data has been collected from tourists in the City of Gjirokastra using a face-to-face questionnaire. Various machine learning algorithms have been applied to our data to determine the best model for forecasting tourist spending. The most appropriate model found is by applying a support vector machine for regression. The model we found can be used in forecasting the expenditure of a first-time visitor. Tourism agencies can use this information to create convenient and affordable offers to increase the number of tourists visiting the area.

Keywords: machine learning algorithms; forecasting spending; creating tourism offers; support vector machine for regression.

DOI: 10.1504/IJTMKT.2023.127333

International Journal of Technology Marketing, 2023 Vol.17 No.1, pp.30 - 47

Received: 06 Jan 2022
Accepted: 11 Apr 2022
Published online: 30 Nov 2022 *

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