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shreyagoel / expedia_lookers_vs_bookers

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Visualizations added to this folder were visualized by Shreya Goel.

These are just 4 of the 7 total visualisations we presented from my data visualizations.

  • Data Cleaning
  • Feature Engineering
  • Formulation of Idea of visualisations
  • Utilized algorithms I knew and Heuristic Algorithms
  • Created more than 50 data viz using R

Skills utlized by Shreya

R, Powerpoint, Design, Heuristic Algorithms, Geospacial mapping

ASA DataFest 2017

Hosted by Columbia Statistics Club

Sponsored by: American Statistical Association, Expedia.com, Google, Inc

  • Team name: The Wanderlusters
  • Project title: Expedia Vacations in 2015: Lookers vs. Bookers
  • Team members
    • Christopher Rusnak
    • Shreya Goel
    • Stephanie L. Tokpe
    • Yanni Ma
  • Project summary: We are comparing the booking habits of Expedia users in 2015 who stay on long vacations versus short vacations. We have segmented their customer base into two initial groups: those who are planning a hotel stay for at least 10 days, and those who want to stay at a hotel for less than 10 days. For each of these populations, we analyze users who are interested in booking hotels on the same day that they plan to check in to them. Also, the graph which shows the location of the users who booked for more than 10 days of a vacation or a package.

Contribution statement:

Shreya: Generated the network of Expedia.com searches on World Map - 7 different ways to understand which countries are involved and which country are the ones where bookings take place for more than 10 days, less than 10 days and where only clicking is being done.

Yanni Ma: Analyzed the customer pattern of people trying to book for a trip less than 10 days, especially on the original location, destination, and the length of the trip.

All members assisted with developing and refining the project prompt. CR, and ST created a project plan and narrowed its scope. CR and ST wrote a recommended timeline for remaining work. CR wrote a process to sample and clean the data, sending these data files out to the entire team. CR also performed exploratory analysis on how relevant features were related to whether or not a user booked, and produced bar charts, queries, and statistical significance tests highlighting such.

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