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Exploratory Data Analysis

Exploratory Data Analysis (EDA) is a critical first step in any machine learning project. It helps you understand your data, identify patterns, and detect anomalies.

Key Components of EDA​

  1. Understanding the Data Structure: Examine the shape, features, and data types of your dataset.
  2. Descriptive Statistics: Calculate mean, median, standard deviation, etc.
  3. Data Visualization: Create plots and charts to visualize distributions and relationships.
  4. Correlation Analysis: Identify relationships between variables.

Tools for EDA​

  • Python libraries: Pandas, Matplotlib, Seaborn
  • Jupyter Notebooks for interactive analysis

Example: Checking Class Balance​

# Print the class balance
print(heart_disease_df['target'].value_counts(normalize=True))