ACADEMIC ANALYSIS, PROGRESSION AND PREDICTION

Computer Science and Engineering

Academic progression prediction and analysis is the process of using data and statistical methods to forecast and understand patterns in students' academic progress. This can involve predicting students' grades or likelihood of success in a particular program or course, identifying factors that may impact their academic progression, and analysing trends and patterns in student performance over time. This app is being built with engineering colleges in mind currently focused singularly for AMC Engineering College. We believe our application will prove to play profound role in uniting AMC students and teachers under a single application where students can keep track of their attendance, progress and predict their potential marks for the upcoming semesters with the help of our machine learning algorithms. Machine learning (ML) is a type of artificial intelligence that involves training algorithms on data in order to make predictions or decisions. In the context of predicting student marks, ML algorithms can be used to analyse data on students' past academic performance and other relevant factors, and use that data to build a model that can predict their grades in a particular course or program. Academic progression prediction and analysis is the process of using data and statistical methods to forecast and understand patterns in students' academic progress. This can involve predicting students' grades or likelihood of success in a particular program or course, identifying factors that may impact their academic progression, and analysing trends and patterns in student performance over time. This app is being built with engineering colleges in mind currently focused singularly for AMC Engineering College. We believe our application will prove to play profound role in uniting AMC students and teachers under a single application where students can keep track of their attendance, progress and predict their potential marks for the upcoming semesters with the help of our machine learning algorithms. One of the benefits of using ML for predicting student marks is that it can help identify patterns and trends in the data that may not be immediately obvious to humans. This can provide valuable insights into the factors that are most important for academic success, and help educators and policymakers allocate resources more efficiently.

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