What Exploratory Data Analysis (EDA) Meaning, Applications & Example
The process of analyzing and summarizing key data characteristics.
What is Exploratory Data Analysis (EDA)?
Exploratory Data Analysis (EDA) is the process of analyzing and visualizing data to understand its main characteristics before applying formal modeling. EDA involves summarizing data sets, checking for patterns, identifying anomalies, and gaining insights, typically using statistical and graphical techniques.
Types of EDA Techniques
- Univariate Analysis: Examines each variable individually, often using histograms, box plots, and summary statistics to understand data distribution.
- Bivariate Analysis: Analyzes the relationship between two variables using scatter plots, correlation coefficients, and cross-tabulations.
- Multivariate Analysis : Explores interactions among multiple variables, using techniques like pair plots and heatmaps to reveal complex relationships.
Applications of EDA
- Data Cleaning: Identifies missing values, outliers, and inconsistencies that need correction before modeling.
- Feature Engineering : Helps select or create features that enhance model performance by understanding variable importance and relationships.
- Hypothesis Testing : Tests assumptions and generates hypotheses based on observed data patterns, guiding the next steps in analysis or modeling.
Example of Exploratory Data Analysis
An example of EDA is in customer behavior analysis, where EDA techniques like histograms and scatter plots are used to identify spending patterns and segment customers, helping businesses tailor marketing strategies and product offerings to specific customer groups.