Much of the conversation about improving predictive models focuses on finding the optimal model technology, such as linear regression, logistics regression, classification trees, etc. We argue that there should be just as much focus on Exploratory Data Analysis (EDA) techniques by exploring, diagnosing and transforming the data - before, during and after the modeling process. We will also demonstrate how to apply different Exploratory Data Analysis (EDA) techniques to find optimal transformation and identify outliers.