Predicting university admissions using data mining and machine learning techniques. This mini-project explores both supervised and unsupervised learning methods to predict the likelihood of admission for prospective students.
This repository contains the code and documentation for a mini-project focused on predicting university admissions. The project utilizes a dataset comprising historical admission data, including features such as GPA, test scores, extracurricular activities, etc.
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Supervised Learning:
- Implemented various supervised learning algorithms such as decision trees, and random forests to predict admission outcomes.
- Evaluated and compared the performance of these models using metrics like accuracy, precision, recall, and F1 score.
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Unsupervised Learning:
- Employed unsupervised learning techniques, including clustering algorithms like k-means, to identify patterns within the data.
- Explored insights into different clusters of applicants and their characteristics.