In a recent kaggle competition, the goal was to use a dataset on shelter animals to do two things: gain insights that can potentially improve their outcome, and to develop a classification model which predicts the outcome of animals (adoption, died, euthanasia, return to owner, transport).
I describe the results of data exploration, my thoughts on what a useful classification model should do, and how we trained and evaluated the properties of the classifier. This was another great competition and I learnt an awful lot!
In a recent kaggle competition, the goal was to develop a face recognition software for right whales. Right whales are critically endangered and only a few hundred individual whales exist. The task was to tell which individual is seen on aerial images like the one below. Such an automatic whale identification helps marine biologists in their conservation efforts.
I describe the methods I developed in python for the competition in this post. I placed 72nd out of 364 teams (top 20%) and I was quite happy with my achievement but see the title! This was my first real-world machine learning project (i.e., not a lecture assignment with a clean, already prepared data set), I had very little prior experience in image processing and machine learning, and I worked mostly alone. This blog is my way to bring the competition to closure, reflect on what I learnt, and what I would do differently next time in a similar competition/project.