Exploratory Data Analysis (EDA) is a way of evaluating datasets to summarise their essential properties, generally using visual methods, in data mining. Before beginning the modelling work, EDA is used to see what the data can tell us.
Exploratory Data Analysis (EDA) is the critical process of performing early investigations on data utilising summary statistics and graphical representations in order to identify patterns, detect anomalies, test hypotheses, and check assumptions.
Dress shoes, hiking boots, sandals, and other footwear are available. When you use EDA, you open yourself up to the possibility that anyone may buy any number of various styles of shoes. You use exploratory data analysis to visualise the data and discover that most customers buy 1-3 different styles of shoes.
The goal of exploratory data analysis is to find missing data and other errors. Increase your understanding of the data set and its underlying structure. Find a parsimonious model that explains the data using the fewest possible predictor variables.
In any Data Analysis or Data Science project, exploratory data analysis, or EDA, is a critical phase. EDA is the investigation of a dataset to find patterns and anomalies (outliers), as well as to create hypotheses based on our knowledge of the dataset.
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