| 000 -LEADER |
|---|
| fixed length control field |
a |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20250108104416.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
250108b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
AIKTC-KRRC |
| Transcribing agency |
AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
24997 |
| Author |
Patil, Kamlesh V. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Study on regression based machine learning models to predict the student performance |
| 250 ## - EDITION STATEMENT |
|---|
| Volume, Issue number |
Vol.38(2), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Sangli |
| Name of publisher, distributor, etc. |
K.E. Society's Rajarambapu Institute of Technology |
| Year |
2024 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Pagination |
177-186p. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
This article discusses the use of three regression models (Linear Regression, Decision Tree Regression, and Random Forest Regression) to study the performance of high school students in India across three subjects: Physics, Chemistry, and Mathematics. The study identifies various factors that affect student performance, such as access to good internet connectivity, parental educational background, and lunch quality. The data was obtained from an educational firm and analyzed based on principles and methods that aid decision-making processes. The results showed that all three regression models produced accurate and plausible results, with an overall accuracy of approximately 95%. The study's primary objective was to provide a clear and concise comparative analysis of various Machine Learning techniques and their impact on the dataset and the predictive attributes analyzed. The findings from this study underscore the importance of considering various factors when analyzing student performance and highlight the effectiveness of Machine Learning techniques in this domain. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
4642 |
| Topical term or geographic name entry element |
Humanities and Applied Sciences |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
24998 |
| Co-Author |
Yesugade, Kiran D. |
| 773 0# - HOST ITEM ENTRY |
|---|
| Title |
Journal of engineering education transformations (JEET) |
| International Standard Serial Number |
2349-2473 |
| Place, publisher, and date of publication |
Sangli Rajarambapu Institute Of Technology |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| URL |
https://journaleet.in/articles/a-study-on-regression-based-machine-learning-models-to-predict-the-student-performance |
| Link text |
Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Articles Abstract Database |
