Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) is a crucial step in the data science and machine learning workflow that involves examining, cleaning, and understanding datasets before building predictive models. It helps identify missing values, duplicates, outliers, inconsistencies, and patterns that may influence model performance. Through statistical summaries and visualizations, EDA reveals relationships between variables and provides insights into the underlying structure of data. In machine learning projects, particularly entity resolution, EDA helps assess data quality, understand feature distributions, and identify similarities and differences across data sources. A systematic EDA process enables informed feature engineering, improves data reliability, and supports the development of accurate, efficient, and robust machine learning solutions.