Machine Learning-Based System for Admission and Jobs Prediction in Engineering and Technology Sector

In the rapidly changing landscape of engineering, education and employment, this research paper presents a novel way to bridge the gap between data driven decision making and the intricacies of career paths and education. Conventional methods are often a generalized solution which fails to cater the individual aspirations and industrial trends. This study utilizes the advanced machine learning techniques specifically, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). The LSTM model is employed to enhance the understanding of intricate nonlinear patterns in the data. Unlike conventional methods, LSTM does not rely on a conventional activation function like ReLU, allowing it to capture more nuanced relationships within the data. This capability enables a more profound comprehension of both market trends and individual goals. Furthermore, the incorporation of GRU tackles the dynamic character of career pathways by enhancing the model's capacity to handle sequential input, including changing employment possibilities and skill needs. This study adds to the body of knowledge on career counseling while also offering a workable way to help students make educated choices about their futures. The integration of LSTM and GRU reflects a commitment to reducing confusion and enhancing the clarity of career choices, ultimately fostering a more confident and prepared workforce for the evolving demands of the professional landscape.