This project analyzes real-world meteorite landing data collected by NASA, using powerful Python libraries such as pandas, matplotlib, seaborn, and folium. The goal is to extract insights and visualize patterns in meteorite mass, year of fall, class types, and global landing locations.
| File | Description |
|---|---|
meteorite_landings.ipynb |
Jupyter Notebook containing the full project code and visualizations |
meteorite_landings.csv |
Dataset from NASA’s open data portal |
README.md |
This readme file explaining the project structure |
-
✅ Data Cleaning
- Drops null values in key columns
- Filters records between years 860 and 2025
- Converts year column to integer format
-
📈 Visual Analysis
- Line plot of meteorite landings per year
- Histogram of meteorite masses (filtered to below 50kg)
- Bar chart of top 10 most common meteorite classes
-
🌍 Interactive Map
- Plots 500 random global landing sites on a Folium world map
- Red markers for large meteorites (> 50kg), blue for smaller
An interactive Folium map displays meteorite landings across the globe.
(Live map in the notebook)
- Python 3
- Jupyter Notebook
pandas,matplotlib,seabornfolium(for interactive maps)
This dataset is publicly available at NASA’s Open Data Portal:
🔗 Meteorite Landings Dataset
- Clone the repository or download the files
- Open
meteorite_landings.ipynbin Jupyter Notebook - Ensure
meteorite_landings.csvis in the same directory - Run all cells to generate the plots and map
🎓 This project was developed as part of a data analysis capstone project using public datasets from NASA.