Kaggle competition: dont-overfit-ii

The malware industry continues to be a well-organized, well-funded market dedicated to evading traditional security measures. Once a computer is infected by malware, criminals can hurt consumers and enterprises in many ways. With more than one billion enterprise and consumer customers, Microsoft takes this problem very seriously and is deeply invested in improving security. As one part of their overall strategy for doing so, Microsoft is challenging the data science community to develop techniques to predict if a machine will soon be hit with malware. As with their previous, Malware Challenge (2015), Microsoft is providing Kagglers with an unprecedented malware dataset to encourage open-source progress on effective techniques for predicting malware occurrences.

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.

Human Protein Atlas Image Classification

Kaggle competition for predicting Pneumonia

Building end to end geolocation project.

Capstone project mid report