Machine-Learning-Case-Study-Regression

A machine learning project that predicts the chances of admission for students applying to graduate programs, with an optional decision-support system for scholarship recommendations.

Project Goals

• Predict admission probability using machine learning models.
• Rank features that impact admission decisions the most.
• Simulate decision-making for awarding scholarships.

Models Used

• Linear Regression
• Decision Tree Regressor
• Random Forest Regressor (with tuning)

Key Results

Model & R² Score (Test)

Linear Regression = 0.76 Decision Tree = 0.69 Random Forest = 0.83 Top 5 Feature Model = 0.81

Top Features: CGPA, GRE Score, TOEFL Score, Research, LOR Strength

Final Test

Predicted chance of admission Scholarship recommendation:

• Recommend if >85%
• Consider if >70%
• Do not recommend otherwise