Explore and analyze SpaceX Falcon 9 launch data to uncover patterns, visualize key metrics, and prepare the dataset for machine learning applications focused on landing prediction.
This repository contains an end-to-end Exploratory Data Analysis (EDA) workflow for SpaceX launches. You'll collect, clean, and explore real-world launch data, generate mission-critical insights, and prepare labels for future predictive modeling.
spacex-eda-project/
│
├── data/
│ └── dataset_part_1.csv
│ └── dataset_part_2.csv
│
├── notebooks/
│ └── 1_data_collection.ipynb # Gathers raw data from SpaceX API
│ └── 2_eda_and_labeling.ipynb # Cleans and analyzes launch data, creates label columns
│
├── images/
│ └── launch_site_distribution.png
│ └── landing_success_chart.png
│
├── README.md
├── requirements.txt
└── .gitignore
- Pulls launch data via the SpaceX API
- Cleans and normalizes with pandas
- Visualizes launch sites, payloads, and outcomes
- Creates binary landing success labels for ML
- Ready for extending to feature engineering and modeling
Get started in a few steps
- Clone the repository
git clone https://github.com/tvankurt-cloud/spacex-eda-project.git cd spacex-eda-project - Install dependencies
pip install -r requirements.txt
- Run notebooks
Open
notebooks/in Jupyter and execute each notebook step by step.
-
1_data_collection.ipynb
Fetches and saves SpaceX launch data from API as CSVs -
2_eda_and_labeling.ipynb
Explores key metrics (launch site, payload, outcomes) and creates label columns for ML
Outputs: See visualizations in the images/ folder (e.g. 
).
- Feature engineering
- Model training and evaluation
- Python >=3.8
- pandas
- numpy
- matplotlib
- seaborn
- requests
Install all dependencies:
pip install -r requirements.txtGitHub: tvankurt-cloud
MIT License. See LICENSE for details.
Contributions and suggestions are welcome! Please open an issue or submit a pull request.