Zindi Competition 2 – Trying CatBoost on the Traffic Jam Challenge

Zindi ran a challenge predicting bus ticket sales into Nairobi. It is now closed, but we can still make predictions and see how they would have done. This was a very quick attempt, but I wanted to try out CatBoost, a magical new algorithm that’s gaining popularity at the moment.

With a little massaging, the data looks like this:

The ‘travel_time’ (in minutes) and ‘day’ columns were derived from the initial datetime data. I’ll spare you the code (it’s available in this GitHub repo) but I pulled in travel times from Uber Movement, and added them as an extra column. The test data looks the same, but lacks the ‘Count’ column – the thing we’re trying to predict. Normally you’d have to do extra processing: encoding the categorical columns, scaling the numerical features… luckily, catboost makes it very easy:

Training the model

This is convenient, and that would be enough reason to try this model first. As a bonus, they’ve implemented all sorts of goodness under the hood to do with categorical variable encoding, performance improvements etc. My submission (which took half an hour to implement) achieved a score of 4.21 on the test data, which beats about 75% of the leaderboard. And this is with almost no tweaking! If I spent ages adding features, playing with model parameters etc, I have no doubt this could come close to the winning submissions.

In conclusion, I think this is definitely a tool worth adding to my arsenal. It isn’t magic, but for quick solutions it seems to give good performance out-of-the-box and simplifies data prep – a win for me.

This was a short post since I’m hoping to carry on working on the AI Art contest – expect more from that tomorrow!

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2 thoughts on “Zindi Competition 2 – Trying CatBoost on the Traffic Jam Challenge

  1. Merci beaucoup pour ce que vous faites. Que Dieu vous bénisse !
    Je suis débutant en data science et résidant en côte d’ivoire. Je vous contacterai souvent pour que vous m’aidiez dans mon parcours.

    Like

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