Why is EDA important in data science?
EDA is crucial because it helps ensure data quality and improves model accuracy. By understanding patterns and identifying errors early, analysts can make better decisions and avoid misleading results during model training.
What are the main steps in EDA?
The main steps include data cleaning, handling missing values, analyzing distributions, visualizing data, and identifying relationships. These steps help transform raw data into a structured format ready for modeling.
What is the role of data visualization in EDA?
Data visualization simplifies complex datasets by presenting them in graphical formats such as charts and graphs. This helps analysts quickly identify trends, patterns, and outliers that may not be visible in raw data.
How does EDA improve machine learning models?
EDA improves models by ensuring clean, relevant, and well-structured data. It helps in feature selection, removing noise, and identifying important variables, which leads to better model performance.
How do you handle missing values in EDA?
Missing values can be handled by removing incomplete data or imputing values using statistical methods like mean, median, or predictive models. The approach depends on the dataset and analysis goals.