Koober Preduction IO Presentation

1. PredictionIO
Adam Wang
Annie Cheng
Brett Clancy
Erin Chen
Han-Wen Chen
Siyuan Wang
Yiting Wang
Koober–

2. Koober
Meet Tim

3. Koober
Tim’s Challenge
+
Provide an efficient service for customers and taxi
drivers by…
Wait Times Revenue

4. Tim has some questions
How should taxis be allocated
among different locations?
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What factors drive customers
to use taxis more?

5. But most importantly…
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How can the taxi industry utilize past data
to predict the future needs of their riders?

6. Current Solutions
Koober
iTaxi
Shiny
TaxiPrediction

7. Our Solution
Koober. An open-source interactive website for
visualizing past and predicting future taxi demand.
Koober

8. Why Koober?
Demand Analysis + Prediction. Empower taxi
owners with valuable rider information backed by
past trends.
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Anyone in Ride Sharing Industry. Plan more
appropriate routes and make better transportation
decisions.

9. So how does it work?
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1. Choose your info need: analysis or prediction.
OR

10. So how does it work?
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2. Adjust your input query parameters.

11. So how does it work?
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3. Visualize taxi demand on the heat maps.

12. So how does it work?
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4. Make more informed taxi resource decisions.

13. Let’s try it out!
https://koober-dashboard.herokuapp.com/
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14. How does it really work?
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Train and test machine learning models.
Display demand on interactive heat maps.

15. What have we done?
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Task 1: Generate dataset and extract features.
Task 2: Build various machine learning models.
Task 3: Develop the website and dashboard interface.
Task 4: Integrate Mapbox data visualization.

16. Raw Data
NYC Taxi & Limousine Commission:
Yellow Taxi Cab Dataset
Koober
Weather Underground API:
• Historical Weather Data
• Current Conditions
• 10-Day Forecast
Task 1

17. Demo Data Loader
Koober
Task 1
Development Production
• Generate fake traffic data
based on user-defined
parameters
• Simplify debugging and
validating the model
prediction result
• Process NYC Taxi Data and
integrate with the historical
weather data
• Incorporate Kafka to
facilitate importing large
amount of training data

18. Feature Extraction
Koober
Task 1
{
"eventTime": "2017-01-20T18:54:07.000-05:00",
"lat": 40.713802337646484,
"lng": -77.0088882446289,
"temperature": 0,
"clear": 1,
"fog": 0,
"rain": 0,
"snow": 0,
"hail": 0,
"thunder": 0,
"tornado": 0
}
Weather
Location
Event Time
Temperature

19. Location Clustering
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Preparator:
• K-Means Location Clustering
(200 clusters)
• Each cluster represents a
neighborhood geographically
• Demand Aggregation per Unit
Time Interval and Location
Cluster
Task 1

20. What have we done?
Koober
Task 1: Generate dataset and extract features.
Task 2: Build various machine learning models.
Task 3: Develop the website and dashboard interface.
Task 4: Integrate Mapbox data visualization.

21. Koober
Task 2
Gradient-Boosted Trees
Linear Regression with Stochastic Gradient Descent
Neural Network
Random Forest
Ridge Regression
Multiple Models. Supports many classic individual
and combined machine learning models.
Models

22. Koober
Task 2
PredictionIO - Train

23. Koober
Task 2
PredictionIO - Predict

24. What have we done?
Koober
Task 1: Generate dataset and extract features.
Task 2: Build various machine learning models.
Task 3: Develop the website and dashboard interface.
Task 4: Integrate Mapbox data visualization.

25. What have we done?
Koober
Task 1: Generate dataset and extract features.
Task 2: Build various machine learning models.
Task 3: Develop the website and dashboard interface.
Task 4: Integrate Mapbox data visualization.

26. Koober
Task 4
User
Interface
Time
Location
Weather Predict Query
Predict Result
for Each
Algorithm
Map GL
component
Mapbox PredictionIO
Engine
Web App
Map Visualization

27. Koober
How can the taxi industry utilize past data
to predict the future needs of their riders?
Looking Back
Koober. An open-source interactive website for
visualizing past and predicting future taxi demand.
Website Data
Machine
Learning
Visualization

28. Koober
How can the taxi industry utilize past data
to predict the future needs of their riders?
Looking Back
Koober. An open-source interactive website for
visualizing past and predicting future taxi demand.
Website Data
Machine
Learning
Visualization

29. Koober
How can the taxi industry utilize past data
to predict the future needs of their riders?
Looking Back
Koober. An open-source interactive website for
visualizing past and predicting future taxi demand.
Website Data
Machine
Learning
Visualization

30. Koober
How can the taxi industry utilize past data
to predict the future needs of their riders?
Looking Back
Koober. An open-source interactive website for
visualizing past and predicting future taxi demand.
Website Data
Machine
Learning
Visualization

31. Koober
How can the taxi industry utilize past data
to predict the future needs of their riders?
Looking Back
Koober. An open-source interactive website for
visualizing past and predicting future taxi demand.
Website Data
Machine
Learning
Visualization

32. Looking Forward
Koober
ModelsTraining Data Prediction Modes
More Cities
Uber/Lyft
Better Tuning
New Models
Wait Times
Prices

33. Our Mentor
Engineering and Open Source Ambassador at
Salesforce.com
Koober
James Ward
www.jamesward.com

34. Our Professor
Assistant Professor in Computer Science at
Cornell University
Koober
Ross Tate
http://www.cs.cornell.edu/~ross/

35. Contribute at
Koober
https://github.com/jamesward/
koober

36. Questions?
Koober