🚀 SpaceX Falcon 9 first stage Landing Prediction

Classification model predicting Falcon 9 landing success based on mission data (IBM Data Science course capstone)

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Project objectives

Explore, visualize and model SpaceX Falcon 9 launch data using a complete data science pipeline. The main goal is to identify the key factors that influence the success rate of first stage landings.

⚠️ Note: This project was developed in an educational context using partially pre-prepared code from the IBM course. However, I completed, extended, and documented all the analysis, modeling, and interpretation steps independently.

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Key steps

1. Data wrangling
2. Exploratory data analysis
3. Implementing and evaluating ML models
4. Summary and conclusions

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Technologies

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• Scikit-learn
• Jupyter Notebooks

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What I learned throughout the project

• Structure and workflow of a data science pipeline.
• Data collection and data wrangling methods.
• Using Pandas for data manipulation and cleaning.
• Using Matplotlib for exploratory data analysis and visualization.
• Applying statistical analysis and machine learning to uncover hidden patterns.
• Creating well-documented Github repositories with Jupyter notebooks.

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Next steps/future work

• Expand the dataset to help the models generalize better.
• Incorporate new features to reveal factors that haven't been considered yet.
• Integrate additional data sources like weather conditions.

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Status

✅ Educational project - completed at 24.05.2025.

✅ Creating the documentation and putting it all together - completed at 31.05.2025.