Statisfy : An Automated Statistics Tools and Machine Learning Modeler Web Application / John Carlos R. Opleda, John Chris G. Torres, Kebin Bryan D. Aban.

By:

Opleda, John Carlos R

Contributor(s):

Material type: Text

TextManila : Technological University of the Philippines, 2022Description: xiii, 96 pages : illustrations ; 28 cm. + 1 CD-ROM (4 3/4 in.)Content type:

text

Media type:

unmediated

Carrier type:

volume

Subject(s):

LOC classification:

BTH QA 76 O65 2022

Dissertation note: College of Science -- Bachelor of Science in Computer Science, Technological University of the Philippines, 2022. Abstract: This study focused on providing statistics and machine learning tools for researchers that can aid them in conducting studies. The researchers of this study found that there are researchers that find it hard to conduct research due to the lack of knowledge about statistics and difficulty in creating machine learning models. The main objective of this study is to develop a web application that can perform statistical analysis and automate machine learning generation, training, and testing, The system can recommend appropriate statistical methods and machine learning algorithms based on the variables selected and the purpose of analysis. The system can also generate reports of the data analysis in PDF format as well as downloadable machine learning models. The evaluation criteria were based on ISO 25010 and the system is tested in terms of functional suitability, performance efficiency, usability, reliability, and maintainability. The acceptability of the system was rated highly acceptable with a mean rating of 3.65.--Author's Abstract.

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Holdings
Item type	Current library	Shelving location	Call number	Copy number	Status	Notes	Date due	Barcode
Bachelor's Thesis COS	TUP Manila Library	Thesis Section-2nd floor	BTH QA 76 O65 2022 (Browse shelf(Opens below))	c.1	Not for loan	For Room Use Only		BTH0003430

Thesis (Undergraduate)

College of Science -- Bachelor of Science in Computer Science, Technological University of the Philippines, 2022.

Includes bibliographical references.

This study focused on providing statistics and machine learning tools for researchers that can aid them in conducting studies. The researchers of this study found that there are researchers that find it hard to conduct research due to the lack of knowledge about statistics and difficulty in creating machine learning models. The main objective of this study is to develop a web application that can perform statistical analysis and automate machine learning generation, training, and testing, The system can recommend appropriate statistical methods and machine learning algorithms based on the variables selected and the purpose of analysis. The system can also generate reports of the data analysis in PDF format as well as downloadable machine learning models. The evaluation criteria were based on ISO 25010 and the system is tested in terms of functional suitability, performance efficiency, usability, reliability, and maintainability. The acceptability of the system was rated highly acceptable with a mean rating of 3.65.--Author's Abstract.

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