Using a lot of data wrangling and NLP, I created a movie and tv show recommender: Basically, the user selects from the drop-down list their movie/tv show of interest, and the algorithm will recommend a combination of movies and tv shows that the user is most likely to be interested in. I tried to include […]
Tag: machine learning
I wanted to test out R on Apple’s new M1/ARM architecture. Here’s how it went:
Using mathematics to analyze who is a “better” player By: Dr. Ikjyot Singh Kohli It seems that nowadays one cannot escape the never-ending debate of “Who is better? Michael Jordan or Lebron James?” The situation is made worse by organizations like ESPN that have endless debates on their various shows combined with releasing lists of […]
I wrote an extensive application using NLP and TensorFlow/Keras in Python that looks at all of the current and upcoming Hollywood releases for 2020 and tracks the online Twitter sentiment for each of them. The model output was then displayed in a PowerBI dashboard. In essence, we are predicting the classification probability . You can […]
By: Dr. Ikjyot Singh Kohli The conventional wisdom by the political pundits/analysts who are seeking to explain Joe Biden’s massive win in the 2020 South Carolina primary is that Jim Clyburn’s endorsement was the sole reason why Biden won. (Here is just one article describing this.) I wanted to analyze the data behind this and […]
By: Dr. Ikjyot Singh Kohli The Golden State Warriors have posed quite the conundrum for opposing teams. They are quick, have a spectacular ability to move the ball, and play suffocating defense. Given their play in the playoffs thus far, all of these points have been exemplified even more to the point where it seems […]
By: Dr. Ikjyot Singh Kohli So, I came across this article today by Matt Moore on CBSSports, who basically once again has taken to the web to bash the Triangle Offense. Of course, much of what he claims (like much of the Knicks media) is flat-out wrong based on very primitive and simplistic analysis, and […]
I have now made a significant update to my applied machine learning paper on predicting patterns among NBA playoff and championship teams, which can be accessed here: arXiv Link .
Where is Lebron James most effective on the court? Based on 2015-2016 data, we obtained from NBA.com the following data which tracks Lebron’s FG% based on defender distance: From Basketball-Reference.com, we then obtained data of Lebron’s FG% based on his shot distance from the basket: Based on this data, we generated tens of thousands of […]
An interesting machine learning problem: Can one figure out the relationship between the popular vote margin, voter turnout, and the percentage of electoral college votes a candidate wins? Going back to the election of John Quincy Adams, the raw data looks like this: Electoral College Party Popular vote Margin (%) Turnout Percentage of EC John […]
Relativity Digest 