rkraig / cabi-predict

Predict near-term Capital Bikeshare availability using a random forest and Poisson regression. Display current status and predictions with leaflet.js map visualization.

cabi-predict

Capital Bikeshare: Predictions of Near-Term Supply and Demand

This project is a demand and outage predictor for Capital Bikeshare. I take dock status and trip history, weather and calendar data, then fit a random forest regression model to estimate the customer demand for bikes and docks at each bikeshare station as a function of ten predictors:

• Time of Day
• Day of Week
• Day of Year
• Holiday (Y/N)
• Year
• Air Temperature
• Relative Humidity
• Wind Speed
• Precipitation within the past hour
• Snow depth

I demonstrate this model with a customer-facing app that predicts CaBi demand and station outages in the immediate future. This app scrapes real-time dock status and weather data to form a feature vector, estimates customer demand for bikes and docks using the random forest, then computes outage probabilities with a Poisson model. Predictions are visualized on a map using leaflet.js. Result is published to a web app using Flask, hosted on heroku.

App

https://cabi-predict.herokuapp.com/

(Update: I see that the leaflet.js map of prediction output data has broken on heroku... I have not investigated or fixed that yet.)

cabi-predict's Issues

Panda version??

It is showing me error with the panda version. I need it for my final project. it would helpful to get the answer asap.
Please specify the exact version.
Thanks