Overcoming Uncertainty with Bayesian Probability in Python

New and rarely seen items often present difficulties in data science. Even though data is available in larger quantities today than ever, dealing with sparse data is as important as ever. As the number of users has grown, so has the number of things they can interact with—consider the sheer number of videos on YouTube, for example. Not only that but as the amount of data grows, we often try to push it to its limit by analyzing ever more fine-grained segmentation.

Comparing items with different amounts of data can be tricky. In this project, we will use the Bayesian probability theory to solve this and related problems mathematically principled. We will apply Bayesian inference to multiple datasets, using data visualization throughout to show how the models work and understand their outputs.