Exploratory Data Analysis (EDA) is defined as the preliminary stage of data analysis that allows one to get a summary of main features. Mostly, there is graphing and visualizing in EDA. Knowing your data from EDA can help you establish patterns, identify anomalies, and most importantly hypothesize testing that leads your analytic process. One of the very first steps is to look into the distribution of your data: central tendencies such as mean, median, mode; spread, like variance and standard deviation, of your variables. It normally becomes quite helpful to display those distributions graphically in the form of histograms, box plots, or density plots, in order to inspect important summaries such as skewness or outliers. The second step in EDA is exploring relationships between variables. Generally, these relationships and interactions between variables are captured with scatter plots, correlation matrices, and pair plots. Understanding this relationship helps to select and engineer features in order to build predictive models. The most important perspective on the EDA form is the handling of missing data, since they can have a strong impact on both your analysis and model performance. It helps to recognize missing data patterns and decide, by appropriate strategies of how they need to be handled—either through imputation or deletion. EDA also comprises outlier detection, which is points that are found quite far from the others. But sometimes, outliers may suggest a mistake, while at other times, they may reveal essential hidden phenomena; thus, treating an outlier is an integrated part of EDA. Put simply, EDA can easily be termed one of the most critical tools that can be found in the kit of any data scientist. This is the practice that converts raw data into the appropriate and comprehensible form, therefore directing the steps taken in data analysis. The more effectively you explore your data, the more likely it is that your models will serve as strong and reliable sources of information.
