EDA with Personal Email

The exploration of useful insights from a dataset requires a great deal of thought and a high level of experience and practice. The more you deal with different types of datasets, the more experience you gain in understanding the types of insights that can be mined. For example, if you have worked with text datasets, you will discover that you can mine a lot of keywords, patterns, and phrases. Similarly, if you have worked with time-series datasets, then you will understand that you can mine patterns relevant to weeks, months, and seasons. The point here is that the more you practice, the better you become at understanding the types of insights that can be pulled and the types of visualizations that can be done. Having said that, in this chapter, we are going to use one of our own email datasets and perform exploratory data analysis (EDA). 

You will learn about how to export all your emails as a dataset, how to use import them inside a pandas dataframe, how to visualize them, and the different types of insights you can gain. 

In this chapter, we will cover the following topics: