1. Predicting Graduate School Admission NICHOLAS GRABON ECE 539 12/13/13

2. DATA SOURCE  Self Submitted  Physics only  Often incomplete  ~500 total data points  http://www.physicsgre.com/results.php?school=berkeley http://www.physicsgre.com/results.php?school=berkeley

3. DATA PREPARATION AND METHODS  Converted binaries to -1 or 1  Eliminated Data with no acceptance information  Formatted GRE and grades  Used svmtrain and svmclassify from class  Radial basis function worked best  Chose best rate out of 1000 trials  Held out 100 points per trial

4. DATA ANALYSIS  Chose an average resume to investigate (my own)  Varied this with respect to  Physics GRE score  Gender  Year of Application  School Applied to  Residency  GPA  And Combinations of these

5. PGRE SCORE and SCHOOL Classifier Error=16% Green is accepted, blue is denied

6. YEAR and PGRE SCORE

7. PGRE and Gender 1 corresponds to female; -1 to male 20092011 2013 2008

8. PGRE and Race 2008 2009 1 denotes ethnic minority, -1 denotes ethnic majority 2011 2013

9. PGRE and Residence  0 indicates domestic; -1 is international 201320112009 2008

10. PGRE and GPA 2011 Total GPA2011 Major GPA Here I used Princeton to emphasize the difference

11. PGRE score and Verbal GRE Score

12. Conclusions  The PGRE seems to be the most important factor  There is a small amount of randomness but essentially predictable  Gender, ethnicity, and residence matter  General GPA matters more than the subject GPA but not as much as PGRE  The top block of schools have roughly the same criteria