Machine Learning Kaggle Competition Part Two: Improving

I recommend against the "lone genius" path, not only because it's exceedingly lonely, but also because you will miss out on the most important part of a Kaggle competition: learning from other data scientists. If you work by yourself, you end up relying on the same old methods while the rest of the world adopts more efficient and accurate techniques. As a concrete example, I recently have been dependent on the random forest model, automatically applying it to any supervised machine learning task. This competition finally made me realize that although the random forest is a decent starting model, everyone else has moved on to the superior gradient boosting machine. I also don't recommend the "copy and paste" approach, not because I'm against using other's code (with proper attribution), but because you are still limiting your chances to learn.