Data Mining: Mushroom Identification
2018
Mushroom identification joint project with Baskar Dakshin for the Data Mining class (IST 707) at Syracuse University. Detailed contribution attribution labeled in table of contents in project report.
Summary:
Analyzed a mushroom data set by developing R code for cleaning and visualizing the data, as well as code for the following models: Naïve Bayes, Association Rule Mining, K-Means Clustering, Decision Tree, Support Vector Machine, and Random Forest. Determined that odor is the strongest predictor of whether a mushroom is poisonous or edible.