Deep Learning for Student Placement Prediction

ANN-Based Student Placement Classification

This project leverages a Deep Learning Artificial Neural Network (ANN) to predict student placement outcomes based on academic and extracurricular attributes. Features like CGPA, number of internships, Projects, Soft skills, Certifications, Aptitude score, SSC&HSC marks and training details are used as input to train the model.

The life cycle includes:

• Data Preprocessing: Handling missing values, encoding categorical variables, and normalizing features.
• Model Building: Creating a multi-layered ANN using frameworks like Keras or TensorFlow.
• Evaluation: Accuracy, Confusion Matrix, Precision, Recall, and F1-Score to gauge performance.
• Model Saving: Saving the trained model using `.h5` format for deployment.
• Deployment: Integration into a web application for real-time prediction.
• Monitoring: Performance tracking, retraining with new data, handling data drift and feedback loops.

Challenges: Imbalanced data, overfitting, feature importance, and ensuring generalization on unseen student profiles.