Project 76 - Data Science - Data Import, Cleaning, and EDA Analysis - Crash Patterns (TARP Week 1-3)#1734
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khalidatdeakin wants to merge 21 commits intomasterfrom
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Project 76 - Data Science - Data Import, Cleaning, and EDA Analysis - Crash Patterns (TARP Week 1-3)#1734khalidatdeakin wants to merge 21 commits intomasterfrom
khalidatdeakin wants to merge 21 commits intomasterfrom
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Added initial project folder and placeholder file inside Playground/KhalidT12026/Traffic_Accident_Prediction
- Imported Victorian crash dataset - Explored dataset structure and columns - Identified key variables - Created data dictionary
Imported Pedestrian and Bicycle dataset from MOP API and notebook cre…
Pedestrian and Bicycle Data Cleaning
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Hi team, PR created from branch Khalid_TARP into main as per recommended workflow. Please review. |
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Hi team, just to note that this pull request includes collaborative work from both myself and Suba. In addition to the crash data EDA, it also incorporates Suba’s contributions on the pedestrian and bicycle datasets, including data import and data cleaning. All technical checks have passed for this PR. Please review when available. Thank you! |
khalidatdeakin
changed the title
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PR reviewed by internal team member and waiting for approval from Leaders with writing access |
Made few changes in Data collection & cleaning and EDA completed for Pedestrian & Bicycle Data
- Created temporal features (hour_of_day, day_of_week, weekend_flag, rush_hour) - Added derived features (time_period, week_part, speed_zone_numeric) - Performed validation checks - Saved feature-engineered dataset
Mobility features
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This pull request includes the Exploratory Data Analysis (EDA) for crash data as part of TARP Week 3, performed after necessary data import and cleaning activities.
Key contributions:
The analysis highlights peak accident periods (morning and evening commuting hours), increased risk towards the end of the week (Friday), and concentration of accidents in urban areas and major transport corridors.
Branch: Khalid_TARP
Target: master