Exploratory Data Analysis (EDA)

Here you’ll find all articles related to exploratory data analysis (EDA), whether its Python tutorials or conceptual articles.

EDA is used to analyze and examine data sets and summarize their main characteristics. This process often involves the use of data visualization techniques. EDA plays an important role in developing machine learning models in the context of feature engineering, for example, by making relationships between features and target variables apparent. It makes it easier for data scientists to discover patterns, detect anomalies, test a hypothesis, or verify assumptions. It also plays an important role in creating, discovering, and selecting features for machine learning (feature engineering). For example, EDA can highlight relationships among variables that help data scientists to select and improve a feature subset.

The goal of EDA is to uncover the underlying structure and patterns in a dataset, and to identify any potential anomalies or problems that need to be addressed. This can be done using a variety of techniques, such as visualizing the data using plots and graphs, computing summary statistics, or fitting simple models to the data.

EDA is an important step in the data analysis process, because it allows analysts to gain insights and understanding of the data that can inform subsequent steps in the analysis. It is also an iterative process, and analysts may go back and forth between different techniques and approaches as they mixes to other data sets.