An Exploratory Data Analysis (EDA) project analyzing global space missions from 1957 to August 2022.
The project explores trends in space launches, mission success rates, launch locations, and organizations involved in global space exploration.
Using Python-based data analysis tools, this project demonstrates how data science techniques can extract insights from real-world datasets.
Space exploration has evolved significantly over the past several decades.
This project analyzes historical mission data to understand patterns in global space launches, mission outcomes, and participating organizations.
The analysis focuses on identifying trends such as:
- Growth of space missions over time
- Distribution of launches across countries and companies
- Mission success and failure patterns
- Frequently used rockets and launch sites
The results are presented through data visualizations and an interactive dashboard built with Streamlit.
This project investigates several analytical questions:
- How has the number of space launches changed over time?
- Which countries and organizations conduct the most launches?
- What are the most common launch locations worldwide?
- How do mission success rates vary across organizations?
- Which rockets are used most frequently?
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Streamlit
- Jupyter Notebook
Files Description
- app.py → Streamlit application for interactive exploration of the dataset
- space-missions (2).ipynb → Notebook containing exploratory data analysis
- space_missions1.csv → Dataset containing historical space mission records
The dataset was analyzed using Python to uncover trends in space missions.
Key analysis steps included:
- Handling missing values
- Formatting and preparing dataset fields
- Structuring mission data for analysis
- Analyzing launch frequency across decades
- Examining mission success vs failure outcomes
- Identifying major space organizations and launch sites
Several visualizations were created to highlight patterns in the data, including:
- Launch trends over time
- Distribution of launches by country
- Mission success vs failure comparison
- Activity of major space organizations
An interactive dashboard was developed using Streamlit to allow users to explore the dataset dynamically.
The application enables users to:
- View mission statistics
- Explore launch trends
- Analyze organizations and rockets
- Interact with visualizations
Topics
- data-science
- exploratory-data-analysis
- python
- streamlit
- data-visualization
- pandas
- space-data
To run the dashboard locally:
streamlit run app.py