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Data Science Presentation

N
Noah Kiwanuka

This document discusses the VacAdvisor tool, which helps users plan vacations based on their budget. It analyzes vacation destination data using clustering algorithms to group similar cities together. It evaluates different clustering methods and selects k-means clustering as the best approach. VacAdvisor then uses the clusters to recommend vacation options to users based on their budget.

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VacAdvisor Fred N. Kiwanuka Fellow Insight Data Science August 6, 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
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 Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Cluster Validation Table: (Cluster Validation) Number of Clusters WSS(103) City Cities Closest to Centroid 2 10.32 Seattle Detroit, Charlotte, South Bend 3 9.93 Seattle Boston, Phoenix, Detroit 4 9.91 Seattle Charlotte, South Bend, Minneapolis 5 8.40 Seattle Detroit, Charlotte, South Bend 7 9.62 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
Silhouette Score Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Silhouette Score Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Silhouette Score Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Silhouette Score Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Silhouette Score 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 k-means 0.03 0.971 0.971 0.971 0.988 0.970 VQ 0.04 1.000 1.000 1.000 1.000 1.000 PCA +kmeans 0.00 1.000 1.000 1.000 1.000 1.000 Mean Shift 0.24 1.000 0.970 0.972 0.980 0.972 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
Feature Engineering Number of Images: 50,000 and 60 feature vector for each image Perimeter Moment of Inertia [4 features] Elongation Jaggedness Circularity Moment features [9 features] Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor
Results Fred N. Kiwanuka Fellow Insight Data Science VacAdvisor

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