Check out my NBA page which automatically pulls current stats on the leaders in various categories.
Today's NBA Games
This is a geometry calculator, which you use to get the area of a circle, rectangle, or triangle... Here's my source code....
Here's a simulation of Heads Or Tails where it displays ten outcomes of either heads or tails. Here's my source code....
Here is a program of balancing a check book. It gives the user three transactions to choose from: depositing money, withdrawing money using a check, or withdrawing money from an ATM. There are fees associated with depositing money, and withdrawing using a check, .25. There's also a $35 overdraft fee for the check and ATM withdrawals if there's a negative balance, with the ATM advising there will be a fee charged if the user chooses to continue with the transaction. After the user makes a transaction, it shows the current balance, and when the user quits the program, it displays the totals of the transactions. This program uses objects and classes. Here's the source code.
This is an algorithm for a credit card checker, which validates a credit card. Here's the source code.
Here's a random password generator And it's source code, here.
This one is an investment portfolio that demonstrates inheritance with multiple classes...source code.
And here is just a simple bubble sort algorithm that puts 10 numbers in ascending order...source code.
For my Applied Statistics class project, I chose to build a model used to predict wins for the Boston Red Sox by performing a Linear Regression Analysis. Here is my paper, and some of my R files and code used in my analysis.
- Univariate Analysis Code
- Bivariate Analysis Code
- Multivariate Anlaysis Code
- T Test
This is a training dataset of some PITCHf/x data from a few years ago. An important aspect of PITCHf/x data is attaching pitch tags to the data - that is, is a given pitch a fastball, curveball, etc? I used a Naive Bays
and a Random Forest model to create a pitch classification alogrithm. My Bays model got 83% of the pitches correct, while I was able to get my Random Forest model to get almost 90% correct.
Feeling thirsty? Check out a map I did in R on all the New England breweries!
Here's a map I did with total reported UFO sightings in the U.S., that I explored using the UFO data set on Kaggle.
Are you ready for some football?!
I will be using Bayesian Statistics and Decision Trees to predict the outcome for each NFL game this season! It will be interesting to see if my Bayesian model or DT model does a better job than my own predictions. Check it out!
Here's a look at the history of home runs in MLB by year. Notice the spike during the steroid era. Here's a barplot of the Top 10 schools that have sent players to MLB. Any idea on some of these schools? There's a few surprises.
Social Network Analysis: A graph I did in R that shows my LinkedIn network with degrees of centrality and betweeness.
How Markets React To Terrorism: Here's an analysis I did in R on how a few companies' stock price changed over the course of 3 weeks from the Paris attacks to the Colorado shooting, to the San Bernadino shooting. Here's the same thing in Excel.
Here's a word cloud I did in R from an analysis on all my Facebook posts. If you look at the word cloud,
I seem to be focused on the present (as opposed to the past or future), as the words ("today", "now", "day", "tonight") have appeared the most from my posts.
It's also no surprise to see how many times I've included "soccer", "happy", and "friday". The run line chart I did shows my total posts by year.
The results make sense to me as I was more of a fan of MySpace back in day. (Who remembers MySpace?!) The huge spike in 2012 was because that was when I got my first smartphone, and could access Facebook more frequently.
The decline over the last couple of years is probably because I've been using other social media channels more, such as Instagram, Twitter, and SnapChat. This was fun to do!