predicting pothole repair times for the city of seattle
TRANSCRIPT
![Page 1: Predicting Pothole Repair Times for the City of Seattle](https://reader031.vdocuments.mx/reader031/viewer/2022022001/5877419f1a28ab342e8b6f57/html5/thumbnails/1.jpg)
Richard Anderson
Predicting Pothole Repair Times for the City of Seattle
![Page 2: Predicting Pothole Repair Times for the City of Seattle](https://reader031.vdocuments.mx/reader031/viewer/2022022001/5877419f1a28ab342e8b6f57/html5/thumbnails/2.jpg)
Data PipelineObtain Data
![Page 3: Predicting Pothole Repair Times for the City of Seattle](https://reader031.vdocuments.mx/reader031/viewer/2022022001/5877419f1a28ab342e8b6f57/html5/thumbnails/3.jpg)
Data PipelineObtain Data
Geocode Locations
![Page 4: Predicting Pothole Repair Times for the City of Seattle](https://reader031.vdocuments.mx/reader031/viewer/2022022001/5877419f1a28ab342e8b6f57/html5/thumbnails/4.jpg)
Data PipelineObtain Data
Geocode Locations
![Page 5: Predicting Pothole Repair Times for the City of Seattle](https://reader031.vdocuments.mx/reader031/viewer/2022022001/5877419f1a28ab342e8b6f57/html5/thumbnails/5.jpg)
Data PipelineObtain Data
Geocode Locations
Engineer Features
![Page 6: Predicting Pothole Repair Times for the City of Seattle](https://reader031.vdocuments.mx/reader031/viewer/2022022001/5877419f1a28ab342e8b6f57/html5/thumbnails/6.jpg)
Data PipelineObtain Data
Geocode Locations
Engineer Features
![Page 7: Predicting Pothole Repair Times for the City of Seattle](https://reader031.vdocuments.mx/reader031/viewer/2022022001/5877419f1a28ab342e8b6f57/html5/thumbnails/7.jpg)
Data PipelineObtain Data
Geocode Locations
Engineer Features
● Cumulative Number of unrepaired potholes
● Minimum Distance to closest prominent city landmark
● Number of months until end of fiscal year
● Seattle Neighborhood● Closest Street Features
Additional Features
![Page 8: Predicting Pothole Repair Times for the City of Seattle](https://reader031.vdocuments.mx/reader031/viewer/2022022001/5877419f1a28ab342e8b6f57/html5/thumbnails/8.jpg)
Data PipelineObtain Data
Geocode Locations
Engineer Features
Implement ML Models
Exploratory Data Analysis
Models and Algorithms
Scatterplots
Numerical Features
Categorical Features
Logistic Regression
130 features
Random Forest Classifier
Parameter search
![Page 9: Predicting Pothole Repair Times for the City of Seattle](https://reader031.vdocuments.mx/reader031/viewer/2022022001/5877419f1a28ab342e8b6f57/html5/thumbnails/9.jpg)
Data PipelineObtain Data
Geocode Locations
Engineer Features
Implement ML Models
Draw Conclusions
Cumulative No. of Potholes
Temperature
Minimum Distance
Median Home Value
Months to end of FY
![Page 10: Predicting Pothole Repair Times for the City of Seattle](https://reader031.vdocuments.mx/reader031/viewer/2022022001/5877419f1a28ab342e8b6f57/html5/thumbnails/10.jpg)
Data PipelineObtain Data
Geocode Locations
Engineer Features
Implement ML Models
Draw Conclusions
Cumulative No. of Potholes
Temperature
Minimum Distance
Median Home Value
Months to end of FY
+++
+
--
--
![Page 11: Predicting Pothole Repair Times for the City of Seattle](https://reader031.vdocuments.mx/reader031/viewer/2022022001/5877419f1a28ab342e8b6f57/html5/thumbnails/11.jpg)
Data PipelineObtain Data
Geocode Locations
Engineer Features
Implement ML Models
Draw Conclusions
Future Work:● Challenge is to find variables that are
both independent and add the most information to the model
● Careful investigation of whether median home value is really driving repair times.
Cumulative No. of Potholes
Temperature
Minimum Distance
Median Home Value
Months to end of FY
+++
+
--
--