This project is dedicated to analyzing glider aviation safety data. It aims to uncover trends, identify risk factors, and propose measures to enhance safety in glider aviation. The analysis is conducted using a dataset that includes various aspects of glider incidents and manufacturing details.
The analysis is based on two primary data sources:
cases2023-06-03_22-15.json: This file contains detailed records of glider incidents, including dates, types, and outcomes.num_gliders_built.csv: This dataset provides information on the number of gliders built, offering insights into manufacturing trends.
glider_aviation_safety.ipynb: The main Jupyter notebook containing the data analysis.cases2023-06-03_22-15.json: JSON file with glider incident data.num_gliders_built.csv: CSV file with data on the number of gliders built.README.md: This file, providing an overview of the project.
The project includes the following key components:
- Data Loading and Preprocessing: Importing data, handling missing values, and preparing the data for analysis.
- Exploratory Data Analysis (EDA): Conducting a thorough examination of the data through various statistical methods and visualizations.
- Statistical Analysis: Applying statistical tests and models to understand the relationships between different variables.
- Conclusion: Drawing conclusions from the analysis and suggesting potential safety measures.
To run this analysis, you will need Python and the following libraries:
- Pandas
- NumPy
- Matplotlib
- Seaborn
- SciPy
You can install these packages using the following command:
pip install pandas numpy matplotlib seaborn scipyThen, you can run the Jupyter notebook glider_aviation_safety.ipynb to view the analysis.
Contributions to this project are welcome. Please feel free to fork the repository, make changes, and submit a pull request.
This project is open-sourced under the MIT License. See the LICENSE file for more details.
For any queries or suggestions, please contact me on GitHub.