A Machine Learning Model to Predict Student Academics Course Interest
Kuldeep Pal, Chunnu Lal, Abhishant Sharma
Abstract
In the present study, we have put forth a machine learning classifier-based model for predicting if a student’s academic course interest is appropriate. Our demands are growing and have no boundaries as a result of the impending arrival of modern technologies. A lot of research is being done today in the area of data classification and prediction. The rate of progression has always increased and has been exponential. In the modern era, data processing is one of the most important and diverse fields of study, and it has a wide range of applications. We all understand that machine learning will be replaced by AI in the future. An important component of it is deep learning. Data classification in many classes is the most common type. Therefore, we trained a machine learning classifier using current data and a variety of recommended methods and algorithms based on various variables. Researchers are working to increase the accuracy of Students predictions based on abilities that are evaluated using a variety of criteria. In order to determine which model is ideal for categorizing the data, we looked through the numerous options. To determine the optimal model, we compare the various output parameters as well.