Hello,

I am taking a graduate level course on applied machine learning. The goal of the course is not to have a rigorous understanding of the mathematics behind ML algorithms, but to develop an intuitive sense for how to handle data and use it for an end-to-end ML project.

To this end, we are required to do a class project with a fairly rich (a good amount of features which I can later engineer) and large dataset. Pretty much the only requirement for the dataset is that it needs to have at least 1000 instance. While that seems like a small number, my professor has insisted that something like 1000 entries from time series data will not cut it. Instead, it has to be at least 1000 uniquely identifiable instances.

My background is in materials science and nanotechnology, but I cannot find any interesting datasets for the same which meets the above requirement. It would be great if someone could share ideas for some datasets I can look into. PLEASE NOTE that I am not very picky about what domain the dataset is from. My only request is that it is not a toy dataset (like MNIST or Titanic Survival), but it should be something that has interesting applications if I choose to work on it.

So far I have been looking at the UCI repository and Kaggle and I am drawn towards human activity recognition or text classification (sentiment analysis and author attribution), but I am open to any suggestion ya'll have. 