Aviation Safety in Canada: Facts and Predictions.

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.