Exploratory Data Analysis (EDA) is the process of analyzing and summarizing datasets to uncover patterns, relationships, and insights before applying more complex modeling techniques. It is a key step in data science and analytics, used to better understand the data’s structure and key characteristics.
Key Steps in EDA:
- Data Summarization:
- Calculate basic statistics like mean, median, mode, and standard deviation.
- Understand data distributions and ranges.
- Data Visualization:
- Use charts like histograms, scatter plots, and box plots to visualize data trends, distributions, and outliers.
- Missing Values and Outliers:
- Identify and handle missing data.
- Detect outliers that might skew analysis or require further investigation.
- Variable Relationships:
- Explore correlations and relationships between variables using methods like correlation matrices or pair plots.
Purpose:
EDA helps you:
- Gain insights into data before modeling.
- Identify anomalies, trends, and potential data quality issues.
- Choose the right techniques for further analysis.
In summary, EDA is an essential process for data understanding, cleaning, and preparation before applying predictive models or statistical tests.