This project discusses the machine learning algorithms for predicting students chances of admission to a doctoral program. Students will be able to predict their chances of acceptance of ahead of time. I present a novel dataset called Phd_admission_dataset and examine it to determine the performance of several machine learning methods, such as Logistics Regression, KNN. Experimental results show that the KNN model outperforms the Logistics Regression model.
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The project discusses using machine learning to predict doctoral program admissions and compares the performance of Logistic Regression and KNN models, finding that KNN outperforms Logistic Regression.
chinmoyt03/Machine-Learning-Based-Selection-of-PhD-Admission
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The project discusses using machine learning to predict doctoral program admissions and compares the performance of Logistic Regression and KNN models, finding that KNN outperforms Logistic Regression.
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