Semester project
EECS 738 - Machine learning
Team members: Jan Polzer, Ryan Duckworth, Nishil Parmar, Rohan Choudhari, Kunal Karnik
Graduate admission prediction
Dataset: https://www.kaggle.com/mohansacharya/graduate-admissions/version/2
A Machine Learning project to help students get to know their chance of getting admitted to a university
Models used:
Random Forest Regressor
XGBoost
Neural Network
Multivariate Linear Regression
Ridge Regression
Negative Binomial Distribution
Notebooks:
Models and algorithms
Model comparison
Minimum GRE scores
References:
https://en.wikipedia.org/wiki/XGBoost
https://github.com/dmlc/xgboost/tree/master/demo#machine-learning-challenge-winning-solutions
https://en.wikipedia.org/wiki/Gradient_boosting
http://datascience.la/xgboost-workshop-and-meetup-talk-with-tianqi-chen/
https://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/