Efficient convolutional neural networks with smaller filters ...
A Better Vacation -...
Transcript of A Better Vacation -...
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A Better VacationJared Tom
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What is the Problem?
• Expedia wants to provide personalized recommendations for users
• Limited information given
• Goal: Predict the “hotel cluster” for a user
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The Data• From Kaggle
• Anonymized
• Train (37,670,293 / 24)
• Test (2,528,243 / 22)
• Target: 100 categories of “hotel cluster”
• For ease of use I chose to use 30,000 observations from the Train set
• https://www.kaggle.com/c/expedia-hotel-recommendations/data
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Predictors
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Exploration
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Total Bookings
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Booking Frequency
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Packages
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Mobile
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Country with the Most Users
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Most Traveled Country
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Models• Chosen Models:
• Decision Tree: quick, possible overfit
• Random Forest: multiple trees
• KNeighbors Classifier: similar neighbors, not the most accurate
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ModelsDecision Tree Random Forest
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Models
• There was not a strong separation between the models.
• Decision Tree: 27%
• Random Forest: 29%
• KNeighbors Classifier: 28%
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Final Results• The data did not have a
significant amount of variance
• All three models used were significantly better than the baseline, but gave between 27 - 29%
• Need more info to give personalized recommendations
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Call to Action• Give a few more filters in the
initial search
• price, rating
• Build a recommender system so people do not have to search through multiple pages for their “best” hotel and flight
• recommendation system might translate to higher conversion rate
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Next Steps• Move to AWS or Spark to
handle entire dataset
• Explore specific countries more in depth
• Use SVC for a future model
• Collaborative Filtering
• Use location as one training set and take out location in another set