Factors Affecting the Visitation to National Parks Using Machine Learning Techniques: The Case of National Parks in Rwanda
The current study set out to identify factors affecting the number of visitors to national parks using machine learning techniques. The results of different linear regression and random forest models on both the train and test sets were compared using RMSE and R2 . Taken together, both random forest...
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| Format: | Article |
| Language: | English |
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AfricaJournals
2022-05-01
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| Series: | African Journal of Hospitality, Tourism and Leisure |
| Subjects: | |
| Online Access: | https://www.ajhtl.com/uploads/7/1/6/3/7163688/article_6_11_2_457-474.pdf |
| _version_ | 1818476940415205376 |
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| author | Musonera Abdou Edouard Musabanganji Herman Musahara |
| author_facet | Musonera Abdou Edouard Musabanganji Herman Musahara |
| author_sort | Musonera Abdou |
| collection | DOAJ |
| description | The current study set out to identify factors affecting the number of visitors to national parks using machine learning techniques. The results of different linear regression and random forest models on both the train and test sets were compared using RMSE and R2 . Taken together, both random forest and linear regression models were able to predict better on the train set, but all failed to make better predictions on the test set. Both linear regression and random forest models performed better using data from Akagera National Park than Volcanoes and Nyungwe National Parks. The most important features to explain the number of visits to national parks include price, parkspecific characteristics, and different months of the year whose features tend to vary from one park to another. This implies that forecasting future visits to different national parks will not only allow policy makers and the park management to make effective planning and efficient allocation of resources, but will also provide valuable
information to various people as they plan to visit various national parks.
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| first_indexed | 2024-12-10T09:31:45Z |
| format | Article |
| id | doaj.art-5fa52612bd854134b28b9a2eb084d65c |
| institution | Directory Open Access Journal |
| issn | 2223-814X |
| language | English |
| last_indexed | 2024-12-10T09:31:45Z |
| publishDate | 2022-05-01 |
| publisher | AfricaJournals |
| record_format | Article |
| series | African Journal of Hospitality, Tourism and Leisure |
| spelling | doaj.art-5fa52612bd854134b28b9a2eb084d65c2022-12-22T01:54:20ZengAfricaJournalsAfrican Journal of Hospitality, Tourism and Leisure2223-814X2022-05-01112457474https://doi.org/10.46222/ajhtl.19770720.236Factors Affecting the Visitation to National Parks Using Machine Learning Techniques: The Case of National Parks in RwandaMusonera Abdou0Edouard Musabanganji1Herman Musahara2African Centre of Excellence in Data Science, College of Business and Economics, University of RwandaSchool of Economics, College of Business and Economics University of RwandaSchool of Economics, College of Business and Economics University of RwandaThe current study set out to identify factors affecting the number of visitors to national parks using machine learning techniques. The results of different linear regression and random forest models on both the train and test sets were compared using RMSE and R2 . Taken together, both random forest and linear regression models were able to predict better on the train set, but all failed to make better predictions on the test set. Both linear regression and random forest models performed better using data from Akagera National Park than Volcanoes and Nyungwe National Parks. The most important features to explain the number of visits to national parks include price, parkspecific characteristics, and different months of the year whose features tend to vary from one park to another. This implies that forecasting future visits to different national parks will not only allow policy makers and the park management to make effective planning and efficient allocation of resources, but will also provide valuable information to various people as they plan to visit various national parks. https://www.ajhtl.com/uploads/7/1/6/3/7163688/article_6_11_2_457-474.pdfmachine learninggorillasakageranyungwerondom forest |
| spellingShingle | Musonera Abdou Edouard Musabanganji Herman Musahara Factors Affecting the Visitation to National Parks Using Machine Learning Techniques: The Case of National Parks in Rwanda African Journal of Hospitality, Tourism and Leisure machine learning gorillas akagera nyungwe rondom forest |
| title | Factors Affecting the Visitation to National Parks Using Machine Learning Techniques: The Case of National Parks in Rwanda |
| title_full | Factors Affecting the Visitation to National Parks Using Machine Learning Techniques: The Case of National Parks in Rwanda |
| title_fullStr | Factors Affecting the Visitation to National Parks Using Machine Learning Techniques: The Case of National Parks in Rwanda |
| title_full_unstemmed | Factors Affecting the Visitation to National Parks Using Machine Learning Techniques: The Case of National Parks in Rwanda |
| title_short | Factors Affecting the Visitation to National Parks Using Machine Learning Techniques: The Case of National Parks in Rwanda |
| title_sort | factors affecting the visitation to national parks using machine learning techniques the case of national parks in rwanda |
| topic | machine learning gorillas akagera nyungwe rondom forest |
| url | https://www.ajhtl.com/uploads/7/1/6/3/7163688/article_6_11_2_457-474.pdf |
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