Exploratory Data Analysis (EDA) is the process of investigating datasets to summarize their main characteristics, often with visual methods. In Python, EDA is commonly performed using libraries like Pandas, Matplotlib, Seaborn, and Plotly.

Why EDA is important:

• Detects patterns, trends, and relationships.
• Identifies missing or anomalous data.
• Guides feature selection and engineering.
• Improves understanding before modeling.

Key steps in EDA:

1. Initial Exploration pythonКопироватьРедактироватьdf.info() df.describe() df.isnull().sum()
2. Univariate Analysis
   • Analyze single variables using histograms, boxplots, or bar charts.
   • Helps understand distributions and detect outliers.
3. Bivariate Analysis
   • Explore relationships between two variables (e.g., scatterplots, correlation matrix).
4. Multivariate Analysis
   • Use pair plots or heatmaps to evaluate interactions among multiple variables.
5. Handling Outliers
   • Identify and optionally remove or transform extreme values.
6. Missing Data Treatment
   • Visualize missingness with tools like missingno, then decide how to handle it.

EDA helps ask the right questions and choose the right modeling techniques. Whether you’re preparing for machine learning or crafting business insights, a thorough EDA sets the stage for success.