Machine Learning

University Admission Prediction Using Machine Learning

The University Admission Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts student admission chances using the Graduate Admission Dataset. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Logistic Regression and Random Forest with feature engineering, achieving 88.5% accuracy and 94.7% ROC-AUC.

The system utilizes the Graduate Admission Dataset containing 500 student records with features including GRE scores, TOEFL scores, University Rating, SOP, LOR, CGPA, and Research experience. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time admission prediction, enabling students and universities to make data-driven decisions.

• Multi-Model Admission Prediction
• Random Forest (88.5% Accuracy)
• Logistic Regression (84.2% Accuracy)
• Feature Engineering & EDA
• Interactive Visualizations
• Real-time Admission Predictions
• Model Performance Comparison
• Feature Importance Analysis
• Application Strategy Insights
• Flask Web Application