Summary

In this chapter, you built on the topics of independent and dependent variables, splitting data into train/validation/test splits for modeling and providing unbiased estimates of model performance. Here, you learned a range of basic data modeling methods using resampling (up and downsampling data frequency) and rolling window approaches to smoothing and estimating. You began your detailed investigation of data modeling with pandas tools for smoothing and resampling data, and some particular capabilities to handle time series. Importantly, you saw that smoothing methods can highlight patterns in very noisy data and that smoothing can be non-uniform in time, such as using .ewm() or a custom weighting function. With these foundational methods in hand, the next chapter will conclude data modeling with a deeper exploration of linear regression and then non-linear and powerful modeling methods, using Random Forest as a regression model.