https://github.com/chinmoyt03/machine-learning-based-selection-of-phd-admission

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.
https://github.com/chinmoyt03/machine-learning-based-selection-of-phd-admission

hyperparameter-tuning knearest-neighbor-classification knn logistic-regression machine-learning python

Last synced: 3 months ago
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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.

• Host: GitHub
• URL: https://github.com/chinmoyt03/machine-learning-based-selection-of-phd-admission
• Owner: chinmoyt03
• Created: 2024-05-22T06:24:23.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-05T12:13:03.000Z (12 months ago)
• Last Synced: 2025-01-16T09:07:27.149Z (4 months ago)
• Topics: hyperparameter-tuning, knearest-neighbor-classification, knn, logistic-regression, machine-learning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 219 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
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.