Using Machine Learning To Predict Stanford Admission | My First Classifier With SciKit Learn
See The Code Here:
https://github.com/adameubanks/Stanfo...
See The Original CollegeConfidential Forum:
https://talk.collegeconfidential.com/...
I used a machine learning library called scikit learn for python.
I have been getting more and more into machine learning recently so I decided to build a basic classifier using scikit learn in under 3 hours. I built a predictor that predicts whether or not a student would be admitted to Stanford University. I did this by gathering data from collegeconfessions.com that applicants from the class of 2022 posted. I gathered information on their gender, class rank, GPA (unweighted and weighted) as well as leadership in clubs and if the applicant was accepted or rejected, and put that into a google sheet. The classifier then learned from that data. To determine whether or not the classifier was working, I ask the classifier if a student whose data was not in the training data would get in or not. On the couple models that I tested, the classifier predicted the outcome with 100% accuracy. Now obviously, I would want to get more data, better data, and use something much more complicated than a simple scikit classifier.
I think next I will focus more on deep learning with tensorflow, and maybe even experiment with OpenAI Gym.
If you have any suggestions or comments, please tell me so I can improve as I get more into machine learning.
You can see how to build something like this quickly by watching this video from Google Developers:
• Let’s Write a Decision Tree Classifier fro...
Sorry for the audio, I kinda messed up with the recording.
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