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College of computer studies career recommendation using decision tree algorithm / Johnmir R. Isaac, [and 4 others].

By:

Isaac, Johnmir R [author.]

Contributor(s):

Material type: Text

TextDescription: 110 leavesContent type:

text

Media type:

unmediated

Carrier type:

volume

Dissertation note: Undergraduate Theses (BSCS) - Computer Science Camarines Sur Polytechnic Colleges, 2022. Abstract: Anything can evoke a sense of empowerment in the contemporary world. This is evident in many facets of life, including romantic relationships, ordinary purchases. and career decisions. This generation appears to be constantly moving and inclined to making important decisions. This study used machine learning to recommend an ICT-related career for CCS third and fourth-year students to help them choose the right career in the future, and to lessen the job-skill mismatch in the Philippines. It gathered data from the students to serve as training data in recommending a career. The researchers collected a total of 70.75% of the total population of third and fourth-year students and graduates of the college. Decision tree algorithm was used to create a model to train and test the data in recommending an ICT-related career. After the model was created, the researchers utilized the Fl -Score, accuracy, recall, and precision to analyze the efficacy of the data model. The model achieved a 0.67 accuracy, 0.69 precision, 0.67 recall, and 0.65 Fl -score. The model showed that the model needs additional data to achieve a better performance in recommending a career. After evaluating the model, the results showed that decision support metrics were helpful in evaluating a recommender type of model.

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Item type	Current library	Shelving location	Call number	Status	Date due	Barcode
Unpublished Materials	Main Library	Undergraduate Thesis Section	UTH BSCS Is12c 2022 (Browse shelf(Opens below))	Not for loan		006080UM

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Undergraduate Theses (BSCS) - Computer Science Camarines Sur Polytechnic Colleges, 2022.

Includes bibliographical references.

Anything can evoke a sense of empowerment in the contemporary world. This is evident in many facets of life, including romantic relationships, ordinary purchases. and career decisions. This generation appears to be constantly moving and inclined to making important decisions. This study used machine learning to recommend an ICT-related career for CCS third and fourth-year students to help them choose the right career in the future, and to lessen the job-skill mismatch in the Philippines. It gathered data from the students to serve as training data in recommending a career. The researchers collected a total of 70.75% of the total population of third and fourth-year students and graduates of the college. Decision tree algorithm was used to create a model to train and test the data in recommending an ICT-related career. After the model was created, the researchers utilized the Fl -Score, accuracy, recall, and precision to analyze the efficacy of the data model. The model achieved a 0.67 accuracy, 0.69 precision, 0.67 recall, and 0.65 Fl -score. The model showed that the model needs additional data to achieve a better performance in recommending a career. After evaluating the model, the results showed that decision support metrics were helpful in evaluating a recommender type of model.

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