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WeAreDevelopers LIVE β€’ Jun 1, 2023

Hacking Your Vacation: Using Data for Fun

Becky Gandillon

Official theme park wait times are artificially inflated. Learn how to use Python and random forest algorithms to outsmart the crowds and dynamically optimize your next big vacation.

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#1 about 4 min

Approaching data problems with an engineering and strategy mindset

How a background in biomedical engineering informs a structured approach to solving complex data problems.

#2 about 6 min

Defining optimization goals and constraints for theme park vacations

Identifying key variables like park capacity, attendance, and ticket costs to formulate a machine learning problem.

#3 about 7 min

Identifying public and proprietary data sources for predictive modeling

Leveraging school calendars, economic data, and real-time wait times to forecast anticipated crowd behaviors.

#4 about 9 min

Predicting theme park wait times using random forest algorithms

Applying Python and random tree forests to aggregate five-minute wait time increments into daily crowd levels.

#5 about 8 min

Optimizing attraction itineraries to minimize walking and waiting intervals

Using an interactive platform to calculate an efficient touring plan based on anticipated crowd curves.

#6 about 3 min

Integrating real-time dynamic updates via interactive mobile applications

Adjusting predicted itineraries on the fly by replacing static plans with live API polling and user submissions.

#7 about 7 min

Evaluating variable pricing and premium queues to optimize spending

Analyzing the cost-benefit ratio of lightning lanes and variable tickets to maximize time saved per dollar.

#8 about 8 min

Mapping satisfaction metrics against median costs for vacation amenities

Plotting restaurant and hotel data in quadrants to find options with above-average satisfaction and below-average cost.

#9 about 2 min

Envisioning recommendation systems for personalized theme park attraction routing

Conceptualizing a feedback-loop application that suggests subsequent attractions based on real-time user ratings and current wait times.

#10 about 9 min

Extrapolating data collection methods and cloud architecture data pipelines

Clarifying how wait times are continuously scraped and stored directly into cloud databases for ongoing analysis.

Matching moments

15:08 min

Audience questions on practical machine learning operational strategies

Lina Weichbrodt Β· LIVE

2:39 min

Harnessing data mapping tools to identify unmapped opportunities

Laura MΓΆller Laura MΓΆller +3 Β· WWC 2024

1:57 min

Uncovering link opportunities using edge-level analytics

Ekaterina Sirazitdinova Β· WWC 2022

2:38 min

Shifting focus from historical reporting to predictive action

Sandeep Kusuma Sandeep Kusuma Β· WWC Europe 2026

2:18 min

Introduction to data science applications in the retail sector

Julian Joseph Β· LIVE

3:18 min

Utilizing AI and public data sharing for urban planning

Christian Wiegand +3 Β· WWC 2024

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