Kaggle challange: https://www.kaggle.com/c/PLAsTiCC-2018
Help some of the world's leading astronomers grasp the deepest properties of the universe.
The human eye has been the arbiter for the classification of astronomical sources in the night sky for hundreds of years. But a new facility -- the Large Synoptic Survey Telescope (LSST) -- is about to revolutionize the field, discovering 10 to 100 times more astronomical sources that vary in the night sky than we've ever known. Some of these sources will be completely unprecedented!
The Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) asks Kagglers to help prepare to classify the data from this new survey. Competitors will classify astronomical sources that vary with time into different classes, scaling from a small training set to a very large test set of the type the LSST will discover.
More background information is available here: https://arxiv.org/abs/1810.00001
This repository is only a basic work trying to build classification model from scratch based on my own understanding. This repository is not a full work or even approved correct approach.
Jupyter notebook contains data exploratory analysis and ML algorithm veryfication.
- Load data.
- Clean data.
- Feature engineering.
- Merging.
- ML algorithms veryfication (k-fold cross validation)
- Deep Learning approach
- Ensamble methods (combining few best algorithms into one better)