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figures
 
 
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results
 
 
.gitignore
.gitignore
 
 
1. Setup Project.ipynb
1. Setup Project.ipynb
 
 
2. DataPrepAWSSpark.ipynb
2. DataPrepAWSSpark.ipynb
 
 
3. ConvertRDDtoCSV.ipynb
3. ConvertRDDtoCSV.ipynb
 
 
4a. Random Forest (average days).ipynb
4a. Random Forest (average days).ipynb
 
 
4b. Random Forest (specific days).ipynb
4b. Random Forest (specific days).ipynb
 
 
4c. k-Nearest Neighbors (average days).ipynb
4c. k-Nearest Neighbors (average days).ipynb
 
 
5a. Destinations - DataPrepAWSSpark.ipynb
5a. Destinations - DataPrepAWSSpark.ipynb
 
 
5b. Destinations - Random Forest.ipynb
5b. Destinations - Random Forest.ipynb
 
 
Data Prep - Destinations.ipynb
Data Prep - Destinations.ipynb
 
 
DataPrepAWSSpark.ipynb
DataPrepAWSSpark.ipynb
 
 
DataPrepLocalTest.ipynb
DataPrepLocalTest.ipynb
 
 
GeoSpatial Analysis Sandbox.ipynb
GeoSpatial Analysis Sandbox.ipynb
 
 
Homepage.md
Homepage.md
 
 
JFK Pickups by Day of the Week and Time.ipynb
JFK Pickups by Day of the Week and Time.ipynb
 
 
Preprocess data (locally).ipynb
Preprocess data (locally).ipynb
 
 
README.md
README.md
 
 
RepartitionRDDs.ipynb
RepartitionRDDs.ipynb
 
 
Schema.xlsx
Schema.xlsx
 
 
TaxiSparkSchema.py
TaxiSparkSchema.py
 
 
Untitled.ipynb
Untitled.ipynb
 
 
geohash.py
geohash.py
 
 
myConfig.json
myConfig.json
 
 
streams_favorites.xml
streams_favorites.xml
 
 
tableau-test.html
tableau-test.html
 
 

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Predicting the Distribution of Taxis in New York City

Project Website

Project Screencast Video

IPython Notebooks:

1. Setup Project

2. Data Cleaning and Aggregation on AWS using Spark

3. Spark Output to CSV

4a. Machine Learning: Random Forest Prediction - Average Days Week

4b. Machine Learning: Random Forest Predicting the Future - Specific Days

4c. Machine Learning: k-Nearest Neighbors Prediction - Average Days Prediction

5a. Destination Data Cleaning and Aggregation

5b. Machine Learning: Predicting Destination (Pandas and sklearn)

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