Exploratory Data Analysis (EDA)

Definition

Exploratory Data Analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. It helps in understanding the structure of data and identifying patterns, anomalies, or relationships.

5 Must Know Facts For Your Next Test

1. EDA primarily uses graphical techniques like histograms, box plots, and scatter plots.
2. It helps in detecting outliers and missing values in the dataset.
3. EDA is often the first step in data analysis before applying machine learning models.
4. Common Python libraries for EDA include Pandas, Matplotlib, and Seaborn.
5. The goal of EDA is to gain insights that can guide further analysis or hypothesis testing.

Review Questions

• What are some common graphical techniques used in EDA?
• Why is EDA important before applying machine learning models?
• Which Python libraries are commonly used for EDA?

Related terms

Pandas:

A Python library used for data manipulation and analysis. It provides data structures like DataFrames to handle and process structured data.

Matplotlib:

A plotting library for the Python programming language. It enables users to create static, interactive, and animated visualizations.

Seaborn:

A Python visualization library based on Matplotlib that offers high-level interface for drawing attractive statistical graphics.