This project aims to analyze historical launch data from SpaceX to predict the success of Falcon 9 first stage landings. Using data-driven techniques, we leverage machine learning models to identify patterns and factors that contribute to landing outcomes. This analysis is crucial for enhancing the reliability and cost-effectiveness of space missions, contributing to more sustainable space exploration efforts.
The dataset comprises details of Falcon 9 launches, including launch date, payload mass, orbit type, launch site, and landing outcomes. Data has been sourced from SpaceX launch records and additional space flight tracking resources. For the sake of privacy and compliance, proprietary information has been excluded or anonymized.
- Data Cleaning: Removal of incomplete records, correction of inconsistencies.
- Feature Engineering: Creation of derived variables for analysis, such as flight number, payload-to-orbit type, and a binary landing success indicator.
- Statistical summaries and visualizations to understand the distribution of key variables.
- Correlation analysis to identify potential predictors of landing success.
- Splitting data into training and testing sets.
- Training several machine learning models (e.g., Logistic Regression, Random Forest, Gradient Boosting) to predict landing success.
- Model evaluation using accuracy, precision, recall, and F1 score metrics.
- Python for data processing and model development.
- Libraries: Pandas for data manipulation, Matplotlib and Seaborn for visualization, Scikit-learn for modeling.
- Jupyter Notebook for interactive development and documentation.
To set up the project environment:
- Clone the repository to your local machine.
- Ensure you have Python 3.x installed.
- Install the required libraries using the following command:
pip install pandas matplotlib seaborn plotly scikit-learn jupyter