Greg Kamradt - Capstone Project Galvanize Data Science Immersive July 2015 View the full story at Ryd.io The idea of this project is to allow a user, or marketing lead, to explore and learn about a city from the frequency and volume of taxi drop off points to focus marketing efforts. My goal was to provide an alternative view of a city that has been clustered into subgroups outlining different weekly and daily ride distributions. ####Example The model is built to predict where and when and how many rides will get dropped off given any location in NYC. Every single block in NYC has as a story to tell. Finding a resource that can tell this story in a meaningful manner is a difficult task. A marketing team foreign to NYC will have little idea about how to navigate this concrete jungle. Using Ryd.io this team will be able to focus their efforts accordingly and minimize misallocation of resources. Let's check out an example query ####Example query: "Terra Blues, Thursday 9pm" ####How it works Once a user enters a query, Google API returns a latitude and longitude of that location...and then the magic happens. With that latitude and longitude we pull a geo bounding box of data from elastic search signaling to us the relevant rides. Now that we know how many rides came into the users location per day for the year, we can feed this information into our SARIMA model. The output of the SARIMA model will tell us how many rides it predicts that location will have at a given point in the future. Knowing how many rides will land in a day is great, but we really would like to know at which hour these rides will come. Utilizing the cluster map (below) we can find the nearest cluster point that the users location is near and apply that hourly distribution to our 'rides per day estimate' output from the SARIMA model. This output signals to us how many rides will come in a given hour. But where do we put the points on the map? We can't just put them randomly anywhere in our bounding box...some of them might land on top of buildings...not great. In order to adjust for this we will use a multivariate KDE that randomly resamples from a distribution that was built off of where rides appeared in the past. Basically we are saying..."hey Ryd, where have all the rides landed for this location the past? Now if you were to guess where these rides would arrive this time where would it be?"

1. Ryd.io
Greg Kamradt

2. GOAL
Predicting population movement
for location based marketing

3. GOAL
Time SeriesClustering
Predicting population movement
for location based marketing

4. Clustering
Manhattan

11. Cluster Map

15. Time Series

16. Time-Series
Seasonal Autoregressive
Integrated Moving Average
“Seasonal ARIMA”

17. Time-Series Pipeline
User queries
NYC Address
Pull data via
Elasticsearch
“Terra Blues Bar”
Thursday, 9pm
[40.728469, -73.999235]