Alex has been taking an online machine learning course lately and I wanted to learn the Wolfram Language and use the cloud platform. So we took the opportunity to take this Kaggle challenge and build a classification model on the Wolfram Cloud.

What it does

  1. Uploaded ~1000 rows of sample data to the Wolfram DataDrop platform.
  2. Pulls in the data points and splits them into training (70%) and testing (30%) sets.
  3. Builds a classification model from the training data using the the Wolfram Classify functionality.
  4. Runs the classification model on the testing data.
  5. Calculates the percentage correctly predicted, generally ~70%, and renders a bar chart showing the percentages.

How we built it

  1. Set up a DataDrop Databin to store the data.
  2. Parse data with python and post it to the Databin.
  3. Pulls in the data builds the classification model and verifies correctness all on the Wolfram platform.

Challenges we ran into

  1. Getting all the data onto Wolfram Cloud
  2. Learning Mathematica/Wolfram Language

Accomplishments that we're proud of

  1. Increasing familiarity and intuition for the Wolfram Language.
  2. Completing first Kaggle challenge.

What we learned

  1. Wolfram Language - none of us knew it at the start of the weekend.

What's next for Kaggle Data Challenge "SF Crime" with Wolfram Cloud

  1. Running the algorithm on the official challenge data and submitting the code to kaggle.

Try it out!

Here is the Wolfram notebook file:

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