Portfolio #2: I run into brick walls quickly

I downloaded the python code using the link Dr. Zacharski gave us. I then was quickly stopped from going any further because "import feedparser" wasn't working--I kept on getting an error stating that "ImportError: No module named feedparser". I tried to just comment out the import feedparser line in pydelicious.py, but a lot of thigns in pydelicious.py need the feedparser, so that proved to be a useless effort. Instead of banging my head against this wall, I'm just going to continue onto the weka stuff.

Part 1: Playing with Weka
Today seems to be a good day to run into brick walls; I spent a good 10 minutes trying to figure out where the examples that came with Weka were on my computer. I had installed Weka on Ubuntu Linux using the synaptic package manager; when I ran Weka and tried to load the example, the folder it defaulted to was not the Weka folder, but the folder my command prompt happened to be in (not surprising, really). Regardless, because I let SPM install Weka for me, I had no clue where Weka was installed to or where it put it's files, so I had to dig around for a while. In the end, however, there was success:
(This is a screenshot of my success!)












Part 2: Playing More with Weka
So I started I did the same thing as part 1 to the Cleveland Heart Disease data:












The stats are as follows: ~78% correctly classified and ~22% incorrectly classified. This definitely shows that filtering the data worked well for this set--the majority of the data was filtered correctly. However, a good portion of the data was still incorrectly filtered, leaving me to conclude that filtering (or, at least, j48 filtering) would not be a suitable for this data set or to data pretaining to this content.