CurFi: An automated tool to find the best regression analysis model using curve fitting
Abstract Regression analysis is a well known quantitative research method that primarily explores the relationship between one or more independent variables and a dependent variable. Conducting regression analysis manually on large datasets with multiple independent variables can be tedious. An auto...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-12-01
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| Series: | Engineering Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/eng2.12522 |
| _version_ | 1811320269498744832 |
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| author | Ayon Roy Tausif Al Zubayer Nafisa Tabassum Muhammad Nazrul Islam Md. Abdus Sattar |
| author_facet | Ayon Roy Tausif Al Zubayer Nafisa Tabassum Muhammad Nazrul Islam Md. Abdus Sattar |
| author_sort | Ayon Roy |
| collection | DOAJ |
| description | Abstract Regression analysis is a well known quantitative research method that primarily explores the relationship between one or more independent variables and a dependent variable. Conducting regression analysis manually on large datasets with multiple independent variables can be tedious. An automated system for regression analysis will be of great help for researchers as well as non‐expert users. Thus, the objective of this research is to design and develop an automated curve fitting system. As outcome, a curve fitting system named “CurFi” was developed that uses linear regression models to fit a curve to a dataset and to find out the best fit model. The system facilitates to upload a dataset, split the dataset into training set and test set, select relevant features and label from the dataset; and the system will return the best fit linear regression model after training is completed. The developed tool would be a great resource for the users having limited technical knowledge who will also be able to find the best fit regression model for a dataset using the developed “CurFi” system. |
| first_indexed | 2024-04-13T12:56:57Z |
| format | Article |
| id | doaj.art-bcdd8390d9804232b0084524a75b5d3a |
| institution | Directory Open Access Journal |
| issn | 2577-8196 |
| language | English |
| last_indexed | 2024-04-13T12:56:57Z |
| publishDate | 2022-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj.art-bcdd8390d9804232b0084524a75b5d3a2022-12-22T02:46:02ZengWileyEngineering Reports2577-81962022-12-01412n/an/a10.1002/eng2.12522CurFi: An automated tool to find the best regression analysis model using curve fittingAyon Roy0Tausif Al Zubayer1Nafisa Tabassum2Muhammad Nazrul Islam3Md. Abdus Sattar4Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka BangladeshDepartment of Computer Science and Engineering Military Institute of Science and Technology Dhaka BangladeshDepartment of Computer Science and Engineering Military Institute of Science and Technology Dhaka BangladeshDepartment of Computer Science and Engineering Military Institute of Science and Technology Dhaka BangladeshDepartment of Computer Science and Engineering Military Institute of Science and Technology Dhaka BangladeshAbstract Regression analysis is a well known quantitative research method that primarily explores the relationship between one or more independent variables and a dependent variable. Conducting regression analysis manually on large datasets with multiple independent variables can be tedious. An automated system for regression analysis will be of great help for researchers as well as non‐expert users. Thus, the objective of this research is to design and develop an automated curve fitting system. As outcome, a curve fitting system named “CurFi” was developed that uses linear regression models to fit a curve to a dataset and to find out the best fit model. The system facilitates to upload a dataset, split the dataset into training set and test set, select relevant features and label from the dataset; and the system will return the best fit linear regression model after training is completed. The developed tool would be a great resource for the users having limited technical knowledge who will also be able to find the best fit regression model for a dataset using the developed “CurFi” system.https://doi.org/10.1002/eng2.12522best fitcurve fittingleast squaresmachine learningregressionregression analysis |
| spellingShingle | Ayon Roy Tausif Al Zubayer Nafisa Tabassum Muhammad Nazrul Islam Md. Abdus Sattar CurFi: An automated tool to find the best regression analysis model using curve fitting Engineering Reports best fit curve fitting least squares machine learning regression regression analysis |
| title | CurFi: An automated tool to find the best regression analysis model using curve fitting |
| title_full | CurFi: An automated tool to find the best regression analysis model using curve fitting |
| title_fullStr | CurFi: An automated tool to find the best regression analysis model using curve fitting |
| title_full_unstemmed | CurFi: An automated tool to find the best regression analysis model using curve fitting |
| title_short | CurFi: An automated tool to find the best regression analysis model using curve fitting |
| title_sort | curfi an automated tool to find the best regression analysis model using curve fitting |
| topic | best fit curve fitting least squares machine learning regression regression analysis |
| url | https://doi.org/10.1002/eng2.12522 |
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