Recommendation system using deep learning to predict suitable academic path for higher secondary students

It is critical to predict students' success in topics related to high study, along with deep learning as well as its connection to educational information. Recommending student performance aids in course selection and the creation of appropriate future study plans for students. It assists teachers and supervisors in monitoring pupils in order to give assistance and combining training programmes to obtain the best outcomes, in addition to recommending student performance. One of the benefits of student recommendation will be that it eliminates authorized alerting indicators while also restricting students from being ejected due to inefficiencies. Recommendation helps students by assisting them in selecting courses and study schedules that are suited for their ability. The proposed approach made suggestions using a deep neural network by obtaining relevant information as characteristic and giving weights to it. Feed forwarding and back propagation information have been used to modify the frequency of nodes and hidden layers, and the neural network is constructed automatically utilizing many modified hidden layers. The training phase was often employed to train the system utilizing labelled information from the datasets, whereas the testing phase is being utilized to assess it. With precision, the suggested technique was developed utilizing Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Presented has demonstrated its performance relevance by producing best recommendation outcomes in MAE (0.593) and RMSE (0.785).