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Noah Kiwanuka

This document discusses the VacAdvisor tool, which helps users plan vacations based on their budget. It analyzes data on flight costs, hotel prices, daily expenses, and location attributes for over 700 US cities. It uses clustering algorithms like k-means to group similar cities together based on this data. It then validates the optimal number of clusters and compares different initialization and validation methods for the algorithms. The goal is to recommend vacation locations that fit within a user's budget.

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VacAdvisor Fred N. Kiwanuka Fellow Insight Data Science July 21, 2015 Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
What are my options for vacation with a speci鍖ed budget ? Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
The Data Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
The Data Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
The Data Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
The Data Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Features Flight cost from New York to 720 cities in the US Average cost of hotels for 3143 counties Average daily expenses including car Location of city [east, west, central, south, north] City speci鍖cs like beaches, museum, national parks Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Conceptual Framework Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Algorithm: Clustering Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Algorithm: Clustering Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Algorithm: Clustering Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Cluster Validation Table: (Cluster Validation) Number of Clusters WSS(105) City Similarity 2 9.98 Seattle Detroit, Charlotte, South Bend 3 9.73 Seattle Boston, Phoenix, Detroit 4 9.87 Seattle Charlotte, South Bend, Minneapolis 5 9.15 Seattle Detroit, Charlotte, South Bend 7 9.43 Seattle Detroit, Charlotte, South Bend 10 9.63 Seattle Sacrameto, San Jose, Colombus 12 9.52 Seattle Sacrameto, San Jose, Colombus Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Cluster Initialization and Validation Table: (Cluster Initialization and Validation) Alg Time(s) homo compl v-meas ARI AMI silhouette k-means 0.03 0.971 0.971 0.971 0.988 0.970 0.389 VQ 0.04 1.000 1.000 1.000 1.000 1.000 0.388 After PCA 0.00 1.000 1.000 1.000 1.000 1.000 0.388 Mean Shift 0.24 1.000 0.970 0.972 0.980 0.972 0.386 Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Fred N. Kiwanuka PhD(Groningen), MSC(London), MIT(Fellow) Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Mobile Malaria Diagnosis Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Classi鍖cation Challenge Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Results Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor

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  • 1. VacAdvisor Fred N. Kiwanuka Fellow Insight Data Science July 21, 2015 Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 2. What are my options for vacation with a speci鍖ed budget ? Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 3. The Data Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 4. The Data Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 5. The Data Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 6. The Data Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 7. Features Flight cost from New York to 720 cities in the US Average cost of hotels for 3143 counties Average daily expenses including car Location of city [east, west, central, south, north] City speci鍖cs like beaches, museum, national parks Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 8. Conceptual Framework Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 9. Algorithm: Clustering Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 10. Algorithm: Clustering Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 11. Algorithm: Clustering Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 12. Cluster Validation Table: (Cluster Validation) Number of Clusters WSS(105) City Similarity 2 9.98 Seattle Detroit, Charlotte, South Bend 3 9.73 Seattle Boston, Phoenix, Detroit 4 9.87 Seattle Charlotte, South Bend, Minneapolis 5 9.15 Seattle Detroit, Charlotte, South Bend 7 9.43 Seattle Detroit, Charlotte, South Bend 10 9.63 Seattle Sacrameto, San Jose, Colombus 12 9.52 Seattle Sacrameto, San Jose, Colombus Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 13. Cluster Initialization and Validation Table: (Cluster Initialization and Validation) Alg Time(s) homo compl v-meas ARI AMI silhouette k-means 0.03 0.971 0.971 0.971 0.988 0.970 0.389 VQ 0.04 1.000 1.000 1.000 1.000 1.000 0.388 After PCA 0.00 1.000 1.000 1.000 1.000 1.000 0.388 Mean Shift 0.24 1.000 0.970 0.972 0.980 0.972 0.386 Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 14. Fred N. Kiwanuka PhD(Groningen), MSC(London), MIT(Fellow) Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 15. Mobile Malaria Diagnosis Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 16. Classi鍖cation Challenge Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
  • 17. Results Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor