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Exploratory Data Analysis (EDA)

In this section, we explore and analyze the structure and distribution of the dataset. The goal is to identify trends, outliers, correlations, and data quality issues that may impact our predictive modeling.

The visualizations below summarize key features such as payload mass, orbit type, launch success rates, and landing outcomes. We use both static and interactive charts to highlight relevant insights.

EDA Tools Used:

  • pandas – Data filtering and aggregation
  • matplotlib / seaborn – Static data visualizations
  • plotly – Interactive charts and graphs

Key Insights from EDA:

  • Most launches were conducted from CCSFS SLC 40.
  • The most frequent orbits are GTO, LEO, and ISS.
  • Payload mass ranges from 400 kg to over 6000 kg, showing strong variability.
  • Landing success rate improved significantly after 2014.