AlexCodeGlider / gliderAviationSafety

Explore glider aviation safety through in-depth data analysis. This project leverages incident reports and manufacturing data, utilizing Python and Jupyter Notebooks for trend identification, risk assessment, and safety enhancement in glider aviation.

Glider Aviation Safety Analysis

Overview

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.

Data Source

The analysis is based on two primary data sources:

1. cases2023-06-03_22-15.json: This file contains detailed records of glider incidents, including dates, types, and outcomes.
2. num_gliders_built.csv: This dataset provides information on the number of gliders built, offering insights into manufacturing trends.

Repository Structure

• 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.

Analysis Overview

The project includes the following key components:

1. Data Loading and Preprocessing: Importing data, handling missing values, and preparing the data for analysis.
2. Exploratory Data Analysis (EDA): Conducting a thorough examination of the data through various statistical methods and visualizations.
3. Statistical Analysis: Applying statistical tests and models to understand the relationships between different variables.
4. Conclusion: Drawing conclusions from the analysis and suggesting potential safety measures.

Getting Started

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 scipy

Then, you can run the Jupyter notebook glider_aviation_safety.ipynb to view the analysis.

Contributing

Contributions to this project are welcome. Please feel free to fork the repository, make changes, and submit a pull request.

License

This project is open-sourced under the MIT License. See the LICENSE file for more details.

Contact

For any queries or suggestions, please contact me on GitHub.