Summary

In this chapter, we explored EDA using a practical use case and traversed the business problem. We started by understanding the overall process of executing a data science problem and then defined our business problem using an industry standard framework. With the use case being cemented with appropriate questions and complications, we understood the role of EDA in designing the solution for the problem. Exploring the journey of EDA, we studied univariate, bivariate, and multivariate analysis. We performed the analysis using a combination of analytical as well as visual techniques. Through this, we explored the R packages for visualization, that is, ggplot and some packages for data wrangling through dplyr. We also validated our insights with statistical tests and, finally, collated the insights noted to loop back with the original problem statement.

In the next chapter, we will lay the foundation for various machine learning algorithms, and discuss supervised learning in depth.