Deep Feed Forward Neural Network with Backpropagation for Student Placement Prediction

Problem Statement

Student placement prediction is an important application of Artificial Intelligence and Deep Learning. Educational institutions often need to identify whether a student is likely to be placed based on academic and skill-related factors. Traditional prediction methods rely on manual analysis, which may not accurately capture complex relationships among multiple features.

In this practical, a Deep Feed Forward Neural Network (DFFNN) is developed to predict the placement status of students using features such as CGPA, aptitude score, communication skills, technical skills, internship experience, and project performance. The neural network learns patterns from the training data using the Feed Forward process and improves its prediction accuracy through Backpropagation, which continuously updates the network weights by minimizing prediction errors.

Real-World Scenario

Imagine a university placement cell that receives placement data from hundreds of students every year. Instead of manually evaluating each student's chances of getting placed, the placement officer wants an intelligent system that can automatically analyze student profiles and predict whether a student is likely to be Placed or Not Placed.

The Deep Feed Forward Neural Network receives the student details as input, processes the information through multiple hidden layers, and predicts the placement outcome. If the prediction is incorrect, the Backpropagation algorithm adjusts the network's weights, allowing the model to learn from its mistakes and improve its future predictions.

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Objective

The objective of this practical is to build and train a Deep Feed Forward Neural Network for student placement prediction. The practical demonstrates how data flows through the network during the Feed Forward phase, how prediction errors are calculated using a loss function, and how Backpropagation updates the network weights to improve prediction accuracy over multiple training epochs.