Data Science Project Tutorial: How to Build a Recommender System to Suggest What to Wear on the Run

I’m a runner. I live in Chicago, so we get all four seasons, sometimes within the span of one week. I hate the treadmill, and prefer to run outside as much as possible. Yes, that is me above, running a 5K race when it was 3°F.

Every winter, I seem to forget how to dress for the plunging temps, even though I’ve done it for the past 10+ years. There are websites out there like DressMyRun.com, but they offer generic recommendations on the number of layers but not specifics on what type of layers, thickness, material, etc. I want to know which items from my own running wardrobe work for different types of weather conditions.

So I created my own Recommender System.

If you’re not familiar with Recommender Systems, it’s using a machine learning model to rank or sort predictions. It’s how Netflix recommends movies and TV shows or Amazon recommends products.

Read on for how I created my own Recommender System to suggest what I should wear based on the current weather conditions.

Note: This tutorial assumes that you are familiar with:

• Python programming language
• Dataframes, specifically using Pandas