University Admission Prediction: Leveraging Machine Learning to Forecast Admission Chances

Here we seek to use machine learning methods for prospective student probability of university admission. We develop models that properly predict admissions by using applicant data. These models are predicated on key variables including GPA, TOEFL, and GRE scores.

Methodological Approach:

• Creating a dataset for university applicants including GRE results, TOEFL scores, undergraduate GPA, & admission status helps you better understand them.
• Dealing with missing values; translating categorical variables into numerical form; normalising numerical features;
• Feature Selection: Determining the main characteristics enabling precise admission outcome
• Model Development is done by using Random Forest, Decision Tree, and Linear Regression among other regression techniques.
• Using measures like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) helps one evaluate a model.

The outcomes show that the random forest approach outperforms other approaches in terms of accuracy, so improving the admission predictions. By means of a framework for building predictive models that can support university admissions, this paper emphasizes the potential application of machine learning for educational purpose.