This week, I learned even more about the Weka machine learning system. I am working on a tutorial that teaches about the various features of Weka.
Two important types of machine learning algorithms are nearest-neighbor learning and decision trees. Nearest-neighbor learning involves classifying data points using the instances closest to them, and decision trees are something like using if-then statements based on the values of attributes to classify each data point. Both of these are affected by various factors. In addition, besides using these tools, I viewed visualizations of how they worked.
I am still waiting to receive another student performance data set. Soon, I will have learned enough to start working on the data set I will use for this project.