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Today's world is full of data that is easily accessible for anyone. The problem now is how to make sense of this data and extract some useful insights from the terabytes of raw material. Typically, this involves using machine learning tools - allowing you to build classifiers, cluster data, etc. Many of these approaches give you models that describe the data accurately, but may be difficult to interpret. If you want to be able to understand the result more intuitively it is worth looking at Bayesian Networks - a graphical representation that simplifies complex mathematical model into a most likely graph of dependencies between your variables. I will talk about BNFinder - a python library allowing you to take any tabular data and convert it to a much simplified representation of conditional dependencies between variables. It can be the used for classification of unseen objects while the connection structure can be interpreted even by a non specialist. BNfinder is publicly available under GNU GPL and it can be used by anyone on their data.
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