Sep 19-28 Journal


This week the machine learning group and I explored Decision Trees and I also continued work on the paper airplanes project, meeting Dr. Hassibi to discuss our research plan at Caltech as well.


Decision Trees:
After completing the Concept Map on Linear Regression (my subconcept was the derivative), the group and I looked into Decision Trees through the David Fumo Medium articles. The first article revolved around the iris dataset and using decision trees to classify an iris into one of three species. Having most of my coding experience either just with the Runestone Textbook or Codeacademy's platform, I was initially very confused as to what to do logistically (how to work PyCharm, pip install, etc). Observing and asking group mates though, I soon began to feel accustomed to the general process and realized that even if I didn't have as strong a foundation, I was here because my personality enabled me to learn, catch up, and excel. After the iris decision tree, we started coding the decision tree for predicting survivability on the Titanic and while the process seemed straightforward, some issues regarding the setup of PyCharm on the computer I was using resulted in delays.


Visualization of Decision Trees with the Iris characteristics 


Caltech Research:
On Thursday, we meet with Dr. Hassibi at Caltech and covered linear regression in more detail. He explained the benefits of the algorithm (it's simple) and its flaws (possible false lines of best fit because of gradient descent). After that, he introduced the k-means algorithm which we will be looking into this week and touched on spectral clustering. Walking to the bus, we discussed possible research ideas like clustering SMHS students' insecurities for the upcoming Wellness Center. A special highlight on this visit was the free food the group and I came across as we passed the auditorium, really ending the visit on a high note and also encouraging us to bond more as well.

Prior to going to Caltech, Puja, Will, and I looked through possible datasets to use such as UNICEF Literacy Rates or CDC Hospital Quality. And back at school today, the group and I also discussed the idea of accessing SMHS data on student backgrounds to possibly correlate background with college attendance or something else. Another possible idea proposed was using Kaggle's stock market prediction challenge as our research project where we would try to correlate news with stock price changes. Interested in business and computer science, this idea really appealed to me (not to mention a possible prize!). We discussed seeking advice from AP Statistics teachers and also the challenges of possibly gathering our own dataset.

Dr. Hassibi's notes on Linear Regression (right) and K-Means (left)

Dataset I found on Tuesday that I sent to our group chat as a reminder/brainstorm point 



Paper Airplanes:
After completing Lab Write Up 2, the paper airplanes project transitioned to planning Experiment 3. In the third experiment, we would be altering something in the small Hammer design as we concluded it was the most efficient out of the first and second experiments. While not being able to test the experiment this week, Elizabeth and I concluded the experiment plan which would test how weight distribution would impact flight. We hope to see if weights will reduce or raise flight efficiency as possibly a backlight would angle the plane so that it would fly faster. Ms. H also showed us her research book from college on wine making which also crystallized what my paper airplane project would shape up to be.

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