Student Performance Analyser using Supervised Learning Algorithms

EasyChair Preprint 5747

Abstract

In today’ academic environment, it’s essential to make tools that facilitate students learn during a casual or online environment. the primary step in victimization machine learning technology to boost these advances focuses on
predicting student performance supported the results achieved. one in each of these ways is that they are doing not provide competent leads to expecting underperforming students. Our work aims to double overlap. To beat this
limitation, we have a tendency first to check whether or not it is doable to predict underperforming students a lot accurately. Second, we developed numerous human explainable characteristics to live these factors to determine that factors lead to poor tutorial performance. These factors are supported student ratings at the University of Minnesota. Considering these factors, you
ought to analyze to spot numerous student stakeholders and perceive their importance.

Keyphrases: Decision Tree Algorithm, Random Forest Algorithm, Support Vector Machine, data preprocessing

@booklet{EasyChair:5747,
  author    = {Srricharan Bolisetti and Pranith Reddy Sankepally and Sai Rohith Reddy Kunta and Rohith Reddy Lakkireddy and Manoj Kumar Vemula},
  title     = {Student Performance Analyser using Supervised Learning Algorithms},
  howpublished = {EasyChair Preprint 5747},
  year      = {EasyChair, 2021}}