📖 Overview
This project explores global space missions between 2000 and 2025 using a Kaggle dataset. The goal is to analyze how different countries invest in space exploration, the types of missions launched, and how budgets, technologies, and success rates have evolved over time.
This notebook provides clear data visualizations using Python, Pandas, and Seaborn to highlight key insights about the global space industry.
🚀 Dataset
Source: Global Space Exploration Dataset (2000–2025) – Kaggle
Columns include:
Country
Year
Mission Name
Mission Type
Launch Site
Satellite Type
Budget (in Billion $)
Success Rate (%)
Technology Used
Environmental Impact
Collaborating Countries
Duration (in Days)
🧠 Objectives
Analyze the number of space missions per year
Compare space missions by country
Examine the average mission budget by type
Visualize global trends in space exploration
🧩 Tools and Libraries
This project uses Python with the following key libraries:
pandas
matplotlib
seaborn
🧰 Installation
Download the dataset from Kaggle and upload it to Google Colab.
Run the notebook Space_Exploration_Project.ipynb.
All plots will be generated automatically.
📊 Results
The total number of missions per year shows growth over time, especially after 2010.
A few countries dominate the number of missions, reflecting significant national investments in space programs.
Average budgets vary by mission type, showing differences in scientific, commercial, and exploratory missions.
🪐 Future Work
Add deeper analysis by comparing environmental impact and duration of missions.
Build predictive models for future mission success rates.
Integrate data from new launches and agencies.
👨💻 Author
Developed by Angelo Sorte – Computer Engineer Passionate about AI, Physics, and Space Technology.
🧾 License
This project is released for educational purposes under the MIT License.