/Avito

top 5% solution for Avito Demand Prediction Challenge

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Avito Demand Prediction Challenge

top 5% solution for Avito Demand Prediction Challenge

The task of this competition is to predict the deal probability of each Ads in Ruassian online shop Avito. The organizer provided multiple inputs including:

  1. structure data
  2. text for item infomation
  3. images to show the item The total size of training data is 123 GB in which the most part are images.

Learn to ensemble

I learnt from the failure of last competition in TalkingData AdTracking Fraud Detection Challenge in which I didn't use any ensemble at all and dropped from 7% to 16% in the last hour when somebody published a high score solution which ensembled all the public kernel. It was a pain, and I learnt from that pain. So, from the begining of this competition, I kept that in mind to train as many kinds of models as posssible and then ensemble ithem.

Capsule Network on images

It was Geoffrey Hinton who invented the idea of deeplearning published the Capsule Network to fix the lack of the relative position information in traditional CNN. I've been always curious to try capsule network out and this is the time. But it seems not working well in regression problems for capsule network with low accuracy.

Gradient Boost Trees

Then I jump into the GBTs with structure data, TF-IDF from texts and statistic features calculated from numerical and catergorical data. I tried lightgbm and catboost in python, and xgboost in R which deliver better results than python.

Add more features into GBTs

Then I add following features into the gradient boost model:

  1. calculated statistical features from images.
  2. Using kmeans to calculate regional statistic features by join the lat and lon from the data with actually city coordinates.
  3. Aggreated feature for each day.
  4. City population.

After add these information, I got top 7% in the leaderboard. And continue training with kfolds and ensemble all the results, I got top 5%.

What I missed

The first mistake is that I failed to build a end-to-end neual network which was done by the winner of this competition. winner solution The second mistatke is that I didn't do stacking because I don't know how to stack at that moment. I learnt to stack one more week after this competition.