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http://hdl.handle.net/10603/515443
Title: | a recommendation model to resolve cavity to improve student outcomes |
Researcher: | Trivedi Het Tusharbhai |
Guide(s): | Dr. Ajaykumar M. Patel |
Keywords: | Computer Science Computer Science Interdisciplinary Applications Engineering and Technology |
University: | Ganpat University |
Completed Date: | 2023 |
Abstract: | In education, the most important par for any institute is their student s performance and their student s outcomes. Every institutes works very hard to increase their student s performance. The organization always arrange extra sessions for students, they also provides proper trainings for corporate world. All the faculties of the institute also make a big role in improving student s performance by providing proper guidance. newline newlineIn this research, I have work on how institutes can able to identify various major factors which directly or indirectly will effects student s performance. For that, educational data mining is the best way to identify proper results using different parameters and datasets. In educational data mining there are various of algorithms available which can be very much helpful to gain proper results from the datasets. There are various of algorithms are available in Data mining, Classification rules, regression rule, Association rule mining are most useful and will provides accurate and faster results from various datasets. In this research, I have worked with association rule mining algorithms. In association rule mining there are several of algorithms available like, apriori algorithm, FP Growth algorithm, Eclat algorithm. This different algorithms will use the support and confidence values of our datasets and on the basis of that it will find and provides the best rules based on our parameters and datasets. In this research, I have used Apriori algorithm and using that I have found best rules on the basis of my parameters and data sets. For my study, I have taken a set of 20000 students data using the questioner from various institutes and after finding those dataset I have applied it on Apriori algorithm and found some rules. After that I have used that existing apriori algorithm and made several changes and generates a new algorithm and apply same parameters and dataset on my new algorithm it will provides me same rules with better confidence level. newline newline |
Pagination: | 10661 kB |
URI: | http://hdl.handle.net/10603/515443 |
Appears in Departments: | Faculty of Computer Applications |
Files in This Item:
File | Description | Size | Format | |
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01_title_page.pdf | Attached File | 115.46 kB | Adobe PDF | View/Open |
02_certificate_by_research_guide.pdf | 71.73 kB | Adobe PDF | View/Open | |
05_declaration_by_candidate.pdf | 72.18 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 224.68 kB | Adobe PDF | View/Open | |
abstract.pdf | 219.09 kB | Adobe PDF | View/Open | |
acknowledgement.pdf | 196.86 kB | Adobe PDF | View/Open | |
annexure.pdf | 768.97 kB | Adobe PDF | View/Open | |
chapter 1.pdf | 624.07 kB | Adobe PDF | View/Open | |
chapter 2.pdf | 557.88 kB | Adobe PDF | View/Open | |
chapter 3.pdf | 749.32 kB | Adobe PDF | View/Open | |
chapter 4.pdf | 2.06 MB | Adobe PDF | View/Open | |
chapter 5.pdf | 809.8 kB | Adobe PDF | View/Open | |
conclusion.pdf | 335.35 kB | Adobe PDF | View/Open | |
list of abbreviations.pdf | 109.76 kB | Adobe PDF | View/Open | |
list of figures.pdf | 499.43 kB | Adobe PDF | View/Open | |
list of tables.pdf | 494.74 kB | Adobe PDF | View/Open | |
preface.pdf | 195.95 kB | Adobe PDF | View/Open |
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