Research Article
Career Guidance System Using Decision Tree, Random Forest, and Naïve Bayes Algorithm
Issue:
Volume 13, Issue 2, April 2025
Pages:
35-42
Received:
8 October 2024
Accepted:
30 October 2024
Published:
18 March 2025
DOI:
10.11648/j.ijsts.20251302.11
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Abstract: Students often struggle with identifying the right options that align with their interests, abilities, and aspirations. Most students lack the required knowledge to make the right decisions. After receiving a degree, the path to career specialization always seems unclear for most students. But, if a student can manage to get it right by choosing the right path for their career, they will experience significant economic and psychological benefits. Choosing the right career path is a critical decision that can significantly impact an individual's future. Providing effective career guidance is therefore essential, especially for students who often face challenges in aligning their interests, skills, and aspirations with suitable career options. This study addresses this need by developing and evaluating a comprehensive Career Guidance System utilizing three machine learning algorithms: Decision Tree, Random Forest, and Naive Bayes. The system was built using an iterative approach, incorporating a user-friendly web page and an interactive chatbot to enhance the career guidance experience. Developed and deployed using Python and the powerful Django framework, the system leverages cutting-edge technologies to deliver personalized recommendations tailored to each student's unique profile. To evaluate the system's performance, key metrics such as accuracy, precision, recall, and F1 score were employed. Notably, the Random Forest classifier outperformed the other algorithms, achieving the highest accuracy. This superior performance highlights the algorithm's ability to capture complex relationships between student interests, passions, and career choices, making it an ideal choice for career guidance applications. The Career Guidance System developed in this study holds significant potential for revolutionizing the career counseling process. The choice of algorithms used in this study was chosen given the specific needs of the project, especially considering specific concerns of scalability and accuracy. in the advancement of computer science and its applications in career counseling. The findings demonstrate the system's overall efficiency and effectiveness, paving the way for its wider adoption and further refinement to support students in making informed and fulfilling career choices.
Abstract: Students often struggle with identifying the right options that align with their interests, abilities, and aspirations. Most students lack the required knowledge to make the right decisions. After receiving a degree, the path to career specialization always seems unclear for most students. But, if a student can manage to get it right by choosing th...
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Research Article
Research and Design of Carbon Market Swing Option Products: A Study Based on China Emission Allowance as Underlying Assets
Issue:
Volume 13, Issue 2, April 2025
Pages:
43-53
Received:
12 February 2025
Accepted:
28 February 2025
Published:
18 March 2025
DOI:
10.11648/j.ijsts.20251302.12
Downloads:
Views:
Abstract: Under the policy framework highlighted by the 20th National Congress of the Communist Party of China, which advocates for ‘actively and steadily promoting carbon peak and carbon neutrality’, this study investigates the pricing mechanism of China Emission Allowance (CEA) swing options in the carbon market, aligned with the practical needs of the ‘Interim Regulations on the Management of Carbon Emission Trading’. In line with the policy outlined by the 20th National Congress of the Communist Party of China, which emphasizes the "active and steady progress toward carbon peaking and carbon neutrality," this paper delves into the pricing mechanism of CEA swing options in the carbon market. This article mainly introduces the CEA swing option product, which is an innovative financial instrument that aims to provide flexible risk management means for high-carbon emission industries such as electricity, petrochemicals and manufacturing. This paper analyses the descriptive statistical results of the characteristics and data of the CEA market, based on the Market regional conversion model, and adjusts it in combination with the stochastic fluctuation model, so as to determine the CEA value model. At the same time, this article also emphasises the importance of implementing a flexible product management mechanism, including risk tips, regulatory measures, and rolling issuance and adjustment strategies for products to ensure that products can effectively serve the needs of the target market. The research results of this article play an important role in improving the liquidity of the carbon market and helping relevant enterprises avoid the risks caused by fluctuations in the price of carbon emissions. By introducing swing options, it can not only provide more flexible risk management tools for high-carbon emission enterprises, but also promote the healthy development of the carbon emission trading market and encourage more market entities to participate in energy conservation and emission reduction actions.
Abstract: Under the policy framework highlighted by the 20th National Congress of the Communist Party of China, which advocates for ‘actively and steadily promoting carbon peak and carbon neutrality’, this study investigates the pricing mechanism of China Emission Allowance (CEA) swing options in the carbon market, aligned with the practical needs of the ‘In...
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