PREDICTING STUDENT RESULTS BASED ON STUDY HOURS USING MACHINE LEARNING

  • Unique Paper ID: 166525
  • Volume: 11
  • Issue: 2
  • PageNo: 1006-1011
  • Abstract:
  • Data science and machine learning have shown to be extremely important and effective over time in a number of industries, including education. Computing systems are capable of learning from data and drawing conclusions thanks to machine learning, a subset of artificial intelligence. Assessment systems that forecast student achievement by assessing educational data with data mining and machine learning techniques have been introduced by recent developments in the education sector. Evaluation of student performance is an important educational metric that affects institution accreditation. Universities should use counselling to create performance improvement plans for underachievers in order to solve this. Forecasting academic achievement has emerged as a critical goal for numerous educational establishments. Helping at-risk students, making sure they stay in school, offering excellent learning materials, and improving the university's standing and reputation all depend on this. Small to medium-sized universities may find it difficult to accomplish this, particularly if they concentrate on graduate and postgraduate programs and have a dearth of student data available for research. This project's main goal is to show that it is feasible to train and model a tiny dataset and produce a prediction model with a reasonable level of accuracy. This study also looks at how visualization and clustering methods can be used to find important signs in a limited dataset. To find the most accurate model, many machine learning algorithms were trained with the best indicators. The findings showed that key indicators in tiny datasets can be successfully identified using clustering techniques

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 2
  • PageNo: 1006-1011

PREDICTING STUDENT RESULTS BASED ON STUDY HOURS USING MACHINE LEARNING

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