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Download: [Presentation](non-images/MAGANA-ZOOK_Random ForestsAndTheirApplicationToCancerPrediction.pdf)
Overview: This presentation introduces viewers to what random forests are, how machine learning has played a historic role in the medical field, and how random forests are becoming the preferred solution for classifying samples as cancerous or benign over black-box techniques like neural networks.
Technologies Used: R
Project Writeup: Report
Overview: Using a dataset from a Kaggle competition, practice of the data science process was conducted in the R language to include: data acquisition, ingestion, preparation, exploratory analysis, and model building.
- R
- Python (matplotlib, scikit-learn, etc.)
- SQL
- Tableau
- ML Algorithms: Random Forests, Logistic Regression, SVM, Naive Bayes for document classification, LDA for topic analysis, Recommendation engine (spark based?), K-means or something more interesting for unsupervised, a streaming online learning algorithm (use spark streaming + KAFKA)
Technologies Used: Java (Swing, JDBC)
Code Repository: GitHub
Overview: This application manages an JDBC-based database persisted contact book using a graphical use interface. Features of this application include: adding / editing / deleting contacts from a database, adding / viewing / deleting notes on a contact, searching for contacts, and searching for notes.
Technologies Used: Java (Android SDK), RoboGuice (dependency injection framework)
Code Repository: GitHub
Overview: This application was written to make it easy for people who need to track their blood pressure to do so from anywhere using their smartphone. Features of this application include: adding blood pressure readings, viewing readings in list or graph form, export of readings for sending to a health provider, and a help section that provides information on blood pressure ranges from a trusted medical source (Mayo Clinic).