Welcome back! The school semester is starting to pick up!
Last week we worked on getting the website to be up and running. The math heavy researchers of the team were tasked with doing exploratory data analysis on our almost 4000 deal json file.
In order to do this we first had to upload our json file into python and then make a matrix with the deals we wanted!
Stay tuned to see how it turned out for us!
Welcome back everyone! Good to be back!
After this long break we are ready to get back into the research! We are currently making our website for the “groupon” deals so we can begin making a website for our participants to fill out!
We have a Json file with a little under 4000 deals!! Thus the making of the website gets a little tricky.
So what our goals for the week are:
- continue working on the website
13 weeks in! At this meeting we discussed more about our survey and what exactly we wanted it to do. We also discussed making a front end so the user face is nice. Slightly off topic we discussed what we want our goal to be and if we would like to present this at conference.
- Write abstract
- Look at conferences we want to go to.
Catch you later,
“There is no better test of a man’s integrity than his behavior when he is wrong” -Marvin Williams
Week 12 everyone!
Last week after reading a few articles, we began discussing two dimension reduction techniques – principal component analysis and singular value decomposition.
We have found a source to get our data and information from!
- send out a survey allowing a student to either dislike or like a certain item to start building out database.
“Great works are performed not by strength but by perseverance” – Samuel Johnson
Had off for Thanksgiving break!
“As we express our gratitude, we must never forget that the highest appreciation is not to utter words, but to live by them.”
Double Digits! Crazy that we are 10 weeks in!
This week we read about PCA and SVD some more, and at this weeks meeting we discussed the best way to fill in our data. Our data set is a lot of columns but only around 30 rows, due to this we are not sure we will need to reduce the dimensionality of the matrix.
Also our data set will have a lot of columns that will be empty because the “viewer” did not get to see those deals, so we are wondering which would be the best way to “fill” in the data: a random generator, K-nearest neighbor, Mutual nearest neighbor, artificial neural networks? Its still in the air.
- Continue to read, read and read.
- Play around with data sets.
- Try to find the best technique to “fill” the empty spots.
- Decide if deminsionality reduction is necessary.
“Time moves slowly, but passes quickly” – Alice Walker
Bonus GRE word:
Ephemeral (adj). – short-lived
Week 9! The semester is flying by. We have started a literature review on two separate dimensionality reduction techniques:
- Single Value decomposition
- Principal component Analysis
I have previously did a research project where we used PCA so I am very excited to start moving slightly away from the coding aspect and closer to the mathy sides of things. As the next week progresses we will just keep on reading about these dimensionalilty reduction techniques.
“Strength does not come from winning. Your struggles develop your strengths. When you go through hardships and decide not to surrender, that is true strength.” – Arnold Schwarzenegger