Aviation Safety in Canada: Facts and Predictions.
Frontend URL: www.cadorsmap.ca
Account: cadorsquicksightuser
Username: quicksight_reader
Password: quicksight@cadorscmpt732
- Created a highly interactive visual dashboard to display Canadian aviation incident and accident statistics
- Scrapes new records from Transport Canada CADORs website on a weekly basis using serverless cloud technologies
- Developed an ML Classification Engine which predicted the likelihood of a casualty resulting from an incident with 70% test accuracy.
- Implemented a k-means Clustering algorithm to identify key attributes of CADORs records that resulted in casualties.
- Use the two provided scraper applications to perform the initial processing of CADORs records and store the results in an S3 bucket
- Setup the given scripts in AWS, and connect the components as indicated on the system design graphic.
- Connect your QuickSight dashboard to Athena and publish it on an available domain.
More detailed instructions are available in the file RUNNING.txt
- Python and PySpark
- Selenium and Beautiful Soup for scraping
- AWS S3, Lambda, SQS, Glue, Athena, and Quicksight for Cloud Pipeline
- Photon, Nominatim, and MeteoStat for Geocoding and Weather Augmentation
Given the input features of an aviation incident, predicted whether or not fatalities would result with 70% Test Accuracy.