Where do we start when we stumble across a dataset we don’t know much about? Lets say one where we don’t necessarily understand the underlying generative process for some or all of the variables. Lets assume for now we’re sure there aren’t one off interventions or level shifts in the data, and we don’t know anything about the distribution of the features, trends, seasonality, model parameters, variance, etc.
I tend to start with the simplest, most interpretable models first, regardless if the problem requires classification, regression, or causality modeling. This allows me to assess how difficult the problem is before wasting time applying a complex solution.
The IPython notebook below will outline exploratory analysis in terms of 1) Histograms and Aggregation, 2) Correlation Structure , 3) Dimensional Reduction. Note this isn’t meant to be an exhaustive effort to enumerate all types of imputation and pre-processing, but a quick examination of some best practices.