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I am currently reading Probabilistic Programming & Bayesian Methods for Hackers, to upskill myself on Bayesian experiments. After going through the authors examples, I was curious to try it on a dataset and problem I was already familiar with: predicting IPF disease progression, considering the uncertainty of the preditions.

This blogpost presents a part of my team’s solution for achieving 2 place in Capgemini’s annual Global Data Science Challenge (GDSC). Similiar to the Kaggle competition on Humpback Whale Identification. The goal was to identify, whether a given photo of the whale fluke belongs to one of the thousand known individuals of whales, or it is a new_whale, never observed before. The core difference was that this time it was not the humpback whale flukes but the sperm whale. Identifying matches of the individual whales, helps scientists to track migration routes, look at the social structure of the sperm whale groups, and protect the whales’ natural habitats.

The goal of this notebook is to provide an analysis of the time-series data from a user of a fitbit tracker throughout a year. I will use this data to predict an additional year of the life of the user using Generalized Additive Models.

This notebook describes my approach to the Personalized Healthcare Redefining Cancer Treatment Kaggle competition. This was a research competition at Kaggle in cooperation with the Memorial Sloan Kettering Cancer Center (MSKCC). The goal of the competition was to create a machine learning algorithm that can classify genetic variations that are present in cancer cells.

Being able to see the future would be a great superpower (or so one would think). Luckily, it is already possible to model the future using Python to gain insights into a number of problems from many different areas. In marketing, being able to model how successful a new product will be, would be of great use. In this post, I will take a look at how we can model the future revenue of a product by making certain assumptions and running a Monte Carlo Markov Chain simulation.