Summary: Writing better quality data mining code requires you to write code that is self-explanatory and does one thing at a time well. In terms of analysis, you should be cross-validating and watching for slowly changing relationships in the data.
Summary: XGBoost and ensembles take the Kaggle cake but they’re mainly used for classification tasks. Some tools like factorization machines and vowpal wabbit make occasional appearances.
Summary: To stay on top of your personal development, try learning new things like a programming language, an instrument, or exposure to a new field (e.g. biology or accounting). Exposure to new ideas helps you avoid confirmation bias and increase you willingness to explore your analysis further.
Summary: The foreach package provides parallel operations for many packages (including randomForest). Packages like gbm and caret have parallelization built into their functions. Other tools like bigmemory and ff solve handling large datasets with memory management.
My friend, Josh Jacquet, and I competed in the DMA’s 2016 Analytics Challenge (powered by EY) and placed 4th out of the 50 entrants. Given that the majority of the other contestants were agencies vying for a little exposure, I think we did well.
