This project explores data analytics techniques applied to space mission data. It involves data collection, cleaning, analysis, and visualization to derive meaningful insights from mission-related datasets. The project aims to highlight trends, patterns, and anomalies in space missions, focusing on areas such as mission success rates, spacecraft performance, and mission timelines.
The primary goal of this project is to:
- Collect data from various space missions.
- Perform data cleaning and preprocessing.
- Conduct exploratory data analysis (EDA) to identify key trends.
- Visualize the results through insightful graphs and charts.
- Derive actionable insights to support future space missions.
- Python: For data analysis and visualization
- Libraries: Pandas, NumPy, Matplotlib, Seaborn
- Jupyter Notebook: For interactive analysis
- Streamlit: For interactive app
The dataset includes information about various space missions, such as:
- Mission name
- Launch date
- Spacecraft type
- Mission objectives
- Success/failure outcomes
- Duration of the mission
- Budget allocation (if available)
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Clone the repository:
git clone https://github.com/laraibzafarlaraib/Data-Analytics-Project-on-Space-Mission.git