Running a data mining contest on Kaggle


Following the success last year, I’ve decided once again to introduce a data mining contest in my Business Analytics using Data Mining course at the Indian School of Business. Last year, I used two platforms: CrowdAnalytix and Kaggle. This year I am again using Kaggle. They offer free competition hosting for university instructors, called InClass Kaggle.

Setting up a competition on Kaggle is not trivial and I’d like to share some tips that I discovered to help fellow colleagues. Even if you successfully hosted a Kaggle contest a while ago, some things have changed (as I’ve discovered). With some assistance from the Kaggle support team, who are extremely helpful, I was able to decipher the process. So here goes:

Step #1: get your dataset into the right structure. Your initial dataset should include input and output columns for all records (assuming that the goal is to predict the outcome from the inputs). It should also include an ID column with running index numbers.

  • Save this as an Excel or CSV file. 
  • Split the records into two datasets: a training set and a test set. 
  • Keep the training and test datasets in separate CSV files. For the test set, remove the outcome column(s).
  • Kaggle will split the test set into a private and public subsets. It will score each of them separately. Results for the public records will appear in the leaderboard. Only you will see the results for the private subsets. If you want to assign the records yourself to public/private, create a column Usage in the test dataset and type Private or Public for each record.

Step #2: open a Kaggle InClass account and start a competition using the wizard. Filling in the Basic Details and Entry & Rules pages is straightforward.

Step #3: The tricky page is Your Data. Here you’ll need to follow the following sequence in order to get it working:

  1. Choose the evaluation metric to be used in the competition. Kaggle has a bunch of different metrics to choose from. In my two Kaggle contests, I actually wanted a metric that was not on the list, and voila! the support team was able to help by activating a metric that was not generally available for my competition. Last year I used a lift-type measure. This year it is an average-cost-per-observation metric for a binary classification task. In short, if you don’t find exactly what you’re looking for, it is worth asking the folks at Kaggle.
  2. After the evaluation metric is set, upload a solution file (CSV format). This file should include only an ID column (with the IDs for all the records that participants should score), and the outcome column(s). If you include any other columns, you’ll get error messages. The first row of your file should include the names of these columns.
  3. After you’ve uploaded a solutions file, you’ll be able to see whether it was successful or not. Aside from error messages, you can see your uploaded files. Scroll to the bottom and you’ll see the file that you’ve submitted; or if you submitted multiple times, you’ll see all the submitted files; if you selected a random public/private partition, the “derived solution” file will include an extra column with labels “public” and “private”. It’s a good idea to download this file, so that you can later compare your results with the system.
  4. After the solution file has been successfully uploaded and its columns mapped, you must upload a “sample submission file”. This file is used to map the columns in the solutions file with what needs to be measured by Kaggle. The file should include an ID column like that in the solution file, plus a column with the predictions. Nothing more, nothing less. Again, the first row should include the column names. You’ll have an option to define rules about allowed values for these columns.
  5. After successfully submitting the sample submission file, you will be able to test the system by submitting (mock) solutions in the “submission playground”. One good test is using the naive rule (in a classification task, submit all 0s or all 1s). Compare your result to the one on Kaggle to make sure everything is set up properly.
  6. Finally, in the “Additional data files” you upload the two data files: the training dataset (which includes the ID, input and output columns) and the test dataset (which includes the ID and input columns). It is also useful to upload a third file, which contains a sample valid submission. This will help participants see what their file should look like, and they can also try submitting this file to see how the system works. You can use the naive-rule submission file that you created earlier to test the system.
  7. That’s it! The rest (Documentation, Preview and Overview) are quite straightforward. After you’re done, you’ll see a button “submit for review”. You can also share the contest with another colleague prior to releasing it. Look for “Share this competition wizard with a coworker” on the Basic Details page.
If I’ve missed tips or tricks that others have used, please do share. My current competition, “predicting cab booking cancellation” (using real data from YourCabs in Bangalore) has just started, and it will be open not only to our students, but to the world. 

Submission deadline: Midnight Dec 22, 2013, India Standard Time. All welcome!

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