SQL and Python Scripts for OpenSky Trino Database Analysis. This release includes SQL scripts and Python code for analyzing ADS-B messages stored in the OpenSky Trino database.
iOS App that aims to improve safety amongst glider pilots by making the pilot's practice state easily accessible. Written with SwiftUI.
🤗 항공 안전 도메인에 특화된 한국어 BERT 모델
A project analyzing Bird Strike incidents with data visualizations and dashboards.
Machine Learning-Driven Clear Air Turbulence Prediction for Aviation Safety
Join our mission to enhance aviation safety by diving into a century's worth of aerial accidents. As the data analyst for the International Civil Aviation Organization (ICAO), I've meticulously examined data, conducted extensive analyses, and developed an interactive Tableau Dashboard to provide insightful visualizations.
This project investigates the impact of flight type, crash cause, and region on fatality rates using t-tests, proportion tests, ANOVA, and linear regression. Developed for the Foundations of Machine Learning course, demonstrating proficiency in hypothesis testing, statistical modelling, and data-driven decision-making.
The aim of this project is to build a machine learning model that will predict the level of crash severity which is compared to the percentage of deaths with respect to total Souls on board.
Unveiling Aviation's Hidden Dangers: A Data-Driven Exploration of Crashes and Fatalities (1980-2023)
This repository contains the final project for Applied Machine Learning, where we built and evaluated predictive models to assess the risk of bird strikes on aircraft. The project explores various machine learning techniques to classify incidents and determine whether they resulted in aircraft damage.
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.
Risk quantification of UAS (drones) in the real world using OSM data.
An API that uses aircraft performance data, and Canadian aviation rules, regulations and definitions, to produce VFR flight plans
This project analyzes aviation accident data using machine learning to predict and prevent fatal accidents. By testing models like Linear Regression, Random Forest, and XGBoost, the study found XGBoost to be the most accurate in predicting high-risk scenarios, aiding efforts to improve aviation safety.
SkyWalk is a tool that is developed to help pilots become more aware of the various aspects of their flight plan as it can address situations that may need extra attention.
🦅 Analyze bird strike data from 2000 to 2011 to improve aviation safety and develop effective mitigation strategies for collisions with aircraft.
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