Koober Machine Learning
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Koober is an open-source interactive website that uses machine learning models trained on historical taxi and weather data to visualize past taxi demand and predict future demand. It generates datasets by clustering taxi pickup locations and extracting features from the data, then builds models using techniques like gradient-boosted trees and neural networks. The website integrates these predictions with interactive maps to help the taxi industry optimize operations and better meet customer needs based on past trends.
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Koober Machine Learning
- 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? Koober What factors drive customers to use taxis more?
- 5. But most importantly… Koober 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. Koober Anyone in Ride Sharing Industry. Plan more appropriate routes and make better transportation decisions.
- 9. So how does it work? Koober 1. Choose your info need: analysis or prediction. OR
- 10. So how does it work? Koober 2. Adjust your input query parameters.
- 11. So how does it work? Koober 3. Visualize taxi demand on the heat maps.
- 12. So how does it work? Koober 4. Make more informed taxi resource decisions.
- 13. Let’s try it out! https://koober-dashboard.herokuapp.com/ Koober
- 14. How does it really work? Koober Train and test machine learning models. Display demand on interactive heat maps.
- 15. 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.
- 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 Koober 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