What is EDA and how does it relate to modeling?

What is EDA and how does it relate to modeling?

Exploratory Data Analysis (EDA) is a critical step in the data analysis process. It involves summarizing the main characteristics of a dataset, often using visual methods. EDA helps in understanding the data's structure, detecting outliers, and identifying patterns, which are essential for building effective models.

Can you give me some common techniques used in EDA?

Sure! Common EDA techniques include data visualization (like histograms, scatter plots, and box plots), summary statistics (mean, median, mode), and correlation analysis. These techniques help in gaining insights into the data.

How does EDA influence the modeling process?

EDA influences modeling by guiding feature selection, helping to identify the right algorithms, and revealing potential data issues that need to be addressed before modeling.

What tools can I use for EDA?

Popular tools for EDA include Python libraries like Pandas, Matplotlib, and Seaborn, as well as R packages like ggplot2 and dplyr. Additionally, tools like Tableau and Power BI are great for interactive visualizations.