Educational Transformation through Machine Learning: Exploring Adaptability and Sentiment for Academic Excellence

This study examined how machine learning can change education by using different algorithms, such as Gradient Boosting, RCNN, and Random Forest. These are powerful tools that helped us look closely at how adaptable, effective, and insightful these methods can be in an educational setting. It is within this realization that the process of Gradient Boosting was able to handle the educational system quite well achieved 90% accuracy. The RCNN model achieved an accuracy of 74%, which was good enough in sensing and analyzing educational sentiments—important for giving us the insights into the learning environment. Through RCNN and Random Forest, a couple of ways of machine learning interacting with education were better understood. This study proves how these sophisticated methods can change education, making it more adaptive to the learner, more aware of the feelings of students and teachers, and capable of reaching high academic success