What is the goal of EDA?
The primary goal of EDA is to ensure that the dataset used for complex processes is clean and reliable. This involves addressing two critical aspects: eliminating missing values and outliers that have the potential to skew subsequent analyses, and selecting relevant variables that contribute substantive information while discarding those that are primarily noise.
By thoroughly cleaning the dataset, we eliminate potential sources of inaccuracy in the conclusions derived from subsequent processes. Missing values and outliers can disrupt the integrity of statistical analyses and lead to inaccurate results. Therefore, one of the first focuses of EDA is to identify and handle missing values appropriately, either by imputing appropriate estimates or by removing them altogether. Similarly, outliers, extreme observations that deviate significantly from the overall pattern, are identified and treated to avoid undue influence on subsequent analyses.
In addition...
