A week ago I started a new natural language processing (NLP) project in the area of machine translation. Essentially I’m building a scaled down version of Google Translate. Google Translate will translate between virtually any language, but my translator will only translate from Old Norse into English.

I just finished my second NLP project. Even though this project went much smoother than the first (if you follow the link please wait to see ‘go’ in the ‘The next word is predicted to be:’ box before entering text: LINK) it reminded me that I still have a lot to learn about this fascinating topic. My goal for this second project was to use the text from Stack Overflow questions to predict how many responses that question would receive. Needless to say my predictions did not preform as well as planned. In spite of my efforts clean my coprus tweak the parameters in sklearn’s TFIDF objects, and switching between multinomial naive Bayes and 1 vs all logistical regression, the model never achieved more than 57% accuracy with precision and recall scores less than 50%.

I just finished analyzing data scraped from Box Office Mojo and Rotten Tomatoes, and it was an eye opening experience. To begin, I wanted to determine the impact Rotten Tomatoes’ critic’s score had on the total box office gross of a movie. I became interested in the affect of critic reviews in general and Rotten Tomatoes in particular after “Batman Vs Superman” received a poor rating on Rotten Tomatoes. So how much more could that movie had made if it had scored higher with the critics?

Hello. A little bit about me…. I’ve been a research scientist for many years getting my start in high school as a summer intern at NASA AMES Research Center in Mountain View, CA. Since then I’ve worked at many of Silicon Valley’s leading research institutes and am currently a research affiliate at Lawrence Berkeley Lab. Now I’m studying data science.