MFE admission prediction β GPBoost model (AUC 0.723) on 12,800+ records, 29 programs, 930 LinkedIn profiles. Pure data-driven, no manual tuning.
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Updated
Apr 3, 2026 - Python
MFE admission prediction β GPBoost model (AUC 0.723) on 12,800+ records, 29 programs, 930 LinkedIn profiles. Pure data-driven, no manual tuning.
Admission Prediction website in US elite colleges. This website uses a Machine Learning model trained using Linear Regression technique. Website takes score as input from users to predict the results based on previously trained Machine Learning model.
π End-to-end ML project predicting graduate admission chances using Ridge, Lasso & Linear Regression β with interactive Streamlit dashboard, full EDA, and live probability gauge.
π Graduate Admission Chances Prediction β A Machine Learning model that predicts the probability of a student getting admitted to a university based on GRE score, TOEFL score, CGPA, SOP, LOR, and research experience. Built with Regression models, Scikit-Learn, Pandas, and Python.
A web Portal Used for the Prediction of Admission Jobs in Engineering and Technology /Management/Pharmacy with respect to demographic locations.
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