{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/09e8b3ec0782438ebd5ba8afc8db0248\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/09e8b3ec0782438ebd5ba8afc8db0248-1446c839cf785a2b.gif","duration":302.4848,"title":"Michael Teixeira S4133975 - Data Wrangling Assessment Task 3: Dataset challenge","description":"Today, I walk you through my process for analyzing road accidents data in Victoria. We delve into three key datasets covering accidents, atmospheric conditions, and road surfaces, totaling over 170,000 rows. From data cleaning to merging and handling missing values, we ensure data accuracy and create new variables for deeper insights. Action: Watch to understand our comprehensive data analysis approach."}