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AuToGo: Web based application with implementation of machine learning for intelligent decision support system in car rental services / Barunbdia, Dale Austin C.; Eusebio, Riene Heninz L.; Garcia, Renz Jan Mari M. 6

By: Barunbdia, Dale Austin C.; Eusebio, Riene Heninz L.; Garcia, Renz Jan Mari M. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; January 2024.46Edition: Description: 28 cm. vii, 96 ppContent type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

Contents:

Action note: In: Summary: ABSTRACT: This research introduces AuToGo, an innovative web-based application designed to enhance th efficiency and intelligence of decision-making processes in car rental services through the implementation of machine learning algorithms. The study addresses the challenges faced by the car rental industry in optimizing fleet management, pricing strategies, and customer satisfaction. AuToGo leverages advanced machine learning techniques to analyse historical data, customer preferences, and real-time market trends, providing intelligent decision support for key aspects of car rental operations. The application aims to improve fleet utilization, optimize pricing models, and enhance customer experiences by offering personalized recommendations. The research details the development and integration of machine learning models into AuToGo, evaluates its performance through case studies, and discusses the potential impact on the car rental industry's operational efficiency and competitiveness. AuToGo represents a significant advancement in leveraging data-driven insights for intelligent decision-making within the can rentalsector, showcasing the potential for transformative enhancements in service quality and businee profitability. To verify that the produced system meets the defined requirements, a complete specification and evaluation of software product quality was carried out using the ISO 25010:2011 software quality model. This survey examined each component of the ISO:25010:2011, with an overall percentage of Very Good evaluations at 87.9%, indicating that consumers are quite satisfied. Additionally, 11.7% of the evaluations were given na Good rating, indicating that the system received positive feedback in some areas. Other editions:

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Item type	Current location	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	PLM	PLM Filipiniana Section	Filipiniana-Thesis	T T58.5.B37 2024 (Browse shelf)	Available		FT7789

Total holds: 0

Undergraduate Thesis: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2024. 56

ABSTRACT: This research introduces AuToGo, an innovative web-based application designed to enhance th efficiency and intelligence of decision-making processes in car rental services through the implementation of machine learning algorithms. The study addresses the challenges faced by the car rental industry in optimizing fleet management, pricing strategies, and customer satisfaction. AuToGo leverages advanced machine learning techniques to analyse historical data, customer preferences, and real-time market trends, providing intelligent decision support for key aspects of car rental operations. The application aims to improve fleet utilization, optimize pricing models, and enhance customer experiences by offering personalized recommendations. The research details the development and integration of machine learning models into AuToGo, evaluates its performance through case studies, and discusses the potential impact on the car rental industry's operational efficiency and competitiveness. AuToGo represents a significant advancement in leveraging data-driven insights for intelligent decision-making within the can rentalsector, showcasing the potential for transformative enhancements in service quality and businee profitability. To verify that the produced system meets the defined requirements, a complete specification and evaluation of software product quality was carried out using the ISO 25010:2011 software quality model. This survey examined each component of the ISO:25010:2011, with an overall percentage of Very Good evaluations at 87.9%, indicating that consumers are quite satisfied. Additionally, 11.7% of the evaluations were given na Good rating, indicating that the system received positive feedback in some areas.

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