Kaggle Competition 'Home Depot Product Search Relevance'

Contributed by Amy(Yujing) Ma, Brett Amdur, Christopher Redino. They enrolled in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between January 11th to April 1st, 2016. This post is based on their machine learning project (due on the 8th week of the program).

Given only raw text as input, our goal is to predict the relevancy of products to search results at the Home Depot website. Our strategy is a little different from most other teams in this Kaggle competition, where we generated a workflow that starts with text cleaning, passes through feature engineering and ends with model selection and parameter tuning in the attempt to stand out among thousands of competitors.