Exploratory data analysis is the most challenging task when building a machine learning model, especially for beginners.
A result of the No-Free-Lunch-Theorem is that there’s no single model that will perform well for every dataset. In other words, there’s no silver bullet Machine Learning Algorithm.
The practical consequence is that we need to make a LOT of human decisions when building our model: which algorithm to use, which features to use, which features to discard, apply normalization, regularization, hyperparameters to tune.
And because the space of decisions is so vast, going on simple trial-and-error is a shot in the dark. We need to drive our decisions on actions that could potentially benefit our model.
So, the only way to make better decisions when building a model is to understand our dataset. And that’s why an excellent Exploratory Data Analysis is an essential step in Machine Learning model building.
Reinforcement Learning for Fun 