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Similar to GeorgeTown CCPE Data Science Capstone Final Presentation, Cohort 10, The Data Extractors - Progress In Washington D.C.
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GeorgeTown CCPE Data Science Capstone Final Presentation, Cohort 10, The Data Extractors - Progress In Washington D.C.
- 1. GeorgetownCCPE Data Science Capstone Project Dec 16th, 2017 Mapping Progress in Washington D.C. Presented by:Team Data Extractors Tony Sanchez, Jay Huang, Ken Shuart, Jason Coffey
- 4. Data Ingestion Data Munging and Wrangling Computation and Analyses Modeling and Application Reporting and Visualization
- 5. • Census.gov • Opendata.dc.gov • Zillow.com • USBoundary.com
- 7. Data Ingestion Data Munging andWrangling Computation and Analyses Modeling and Application Reporting and Visualization
- 10. Data Ingestion Data Munging and Wrangling Computation and Analyses Modeling and Application Reporting and Visualization
- 11. * Factored negatively in progress score
- 12. Data Ingestion Data Munging and Wrangling Computation and Analyses Modeling and Application Reporting and Visualization
- 14. Standard Scaler Principal Component Analysis Gaussian Mixture Model • Different units • Different order of magnitude • Reduce complexity • Prevent overfitting • Flexibility in covariance • Won't bias cluster sizes
- 15. • Amount of variance retained from the original data set • Chose 2 components with 75% explained variance
- 16. • Coefficients of features inside components • First component: HS max % and poverty • Second component: population and total crime
- 17. • Silhouette score = cohesion and separability of clusters • Balance between high silhouette score and neighborhood movement between clusters • 30 clusters chosen 30
- 18. • Clusters are greater than mean silhouette score • Acceptable cohesion and separability
- 19. • Clustering did not converge into an optimal solution • Iterated 1000 times
- 20. Data Ingestion Data Munging and Wrangling Computation and Analyses Modeling and Application Reporting and Visualization http://arcg.is/2C4zGNp
- 22. Lessons Learned • Education • Unemployment • New Construction • Foreclosures • Parks and Infrastructure • Jobs added/lost • Transportation • Homeless Shelters
- 23. ThankYou!
Editor's Notes
- Remove background
- Hypothesis only, not challenges. This should say overview not hypothesis.
- Hypothesis only, not challenges. This should say overview not hypothesis.
- race age gender education native born violent crimes naturalized no citizen. List our data sources vs the philly sources. talk to ACS and everything inlcuded.
- Drop Box was worm store write once read many
- Hypothesis only, not challenges. This should say overview not hypothesis.
- Hypothesis only, not challenges. This should say overview not hypothesis.
- Move score information to computation and analysis slides.
- Hypothesis only, not challenges. This should say overview not hypothesis.
- Philly model: Since the primary factor of each neighborhood score was based on DC Mean Growth over the 5 year period, one might conclude that the results were pretty accurate. Off the bat it makes sense that the south east concentrated city development would be reasonably hotter than the upper west side of the district. Hot areas, Kalorama, Adams Morgan, Foggy Bottom, Anacostia TDE: However, looking at our model, it’s more equally distributed. Since we factored ever single month into our analysis and added variables such as: Rent, Violent Crime, Demographics, there’s more detail in the outcomes. Hot areas are Navy Yard, makes since thinking about the growth around the ballpark, Union Station had higher scores. Foggy Bottom and Columbia Heights Scored Similarly. Crime was unusually high in CH and Realestate/Income was very high in Foggy Bottom.