Exploratory Data Analysis
==2024-12-24=!=
What does your data really look like?
Theory
Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) is a critical step in the Data Analysis process that involves examining and visualizing datasets to uncover their main characteristics, identify patterns, spot anomalies, and test hypotheses. This approach allows data scientists to gain insights and prepare the data for further analysis.
Importance of EDA
- Identify Mistakes: EDA helps in detecting errors in data collection and processing.
- Check Assumptions: It allows for the verification of statistical assumptions necessary for modeling.
- Observe Patterns: Analysts can recognize trends and patterns within the data.
- Formulate Hypotheses: EDA aids in Generating Hypotheses for further investigation.
Examples
Implementation
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