Student Placement Prediction using Deep Learning Techniques

Improving student placement prediction is crucial for educational institutions aiming to guide their graduates towards successful careers. Traditional methods often fall short in managing the complexity and variety of student data, highlighting the need for a more sophisticated predictive approach. This research proposes a cutting-edge deep learning-based system that integrates Feedforward Neural Networks (FNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks. The objective is to develop an advanced predictive model capable of efficiently analyzing diverse student data sources. By integrating FNNs, RNNs, and LSTMs, the proposed system can interpret complex student information and generate valuable insights for educational institutions. Its accuracy in predicting placement probabilities and mapping out career trajectories is tailored to students’ abilities and career goals. Consequently, this model enables institutions to create customized placement strategies, enhancing students’ opportunities for success and improving their career outcomes.