This project analyzes space mission data to uncover trends and insights related to global space exploration efforts. The analysis includes launch frequencies, organization-wise performance, and temporal trends. Visualizations are created using Python libraries such as plotly, matplotlib, and seaborn to make the insights engaging and informative.
- Data Cleaning and Preparation:
- Handling missing values and inconsistencies in the dataset.
- Leveraging ISO 3166 for standardized country data.
- Launch Analysis:
- Frequency of space missions over the years.
- Organization-wise analysis of launch activity.
- Interactive and Static Visualizations:
- plotly for interactive plots.
- matplotlib and seaborn for detailed static visualizations.
- Annotations: Highlights specific findings, such as the organization with the most launches in a given decade.
- Python 3.8 or higher
- Installed libraries:
- numpy
- pandas
- plotly
- matplotlib
- seaborn
- iso3166
- datetime
1- Clone the repository:
git clone https://github.com/yourusername/space-missions-analysis.git
2- Navigate to the project directory:
cd space-missions-analysis
3- Install dependencies:
pip install -r requirements.txt
1- Load the Notebook: Open the Jupyter Notebook:
jupyter notebook Space_Missions_Analysis_(start).ipynb
2- Run the Analysis: Execute each cell to perform the data analysis and generate visualizations.
- Trends Over Time: Identified periods of increased space mission activity.
- Top Organizations: Analysis highlights leading contributors to space exploration efforts.
- Annotations: Added annotations to visualizations, marking standout data points, such as the most active organization within specific time frames.
This project is licensed under the MIT License.
- Dataset Source: https://nextspaceflight.com/launches/past/
- Tools: Built using Python libraries such as numpy, pandas, plotly, matplotlib, and more.