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In this project, you get to work with the data from a large number of taxi journeys in New York from 2013. You will use regression trees and random forests to predict the value of fares and tips, based on location, date and time. While not required, it can help to have some extended experience with the packages `dplyr`, `ggplot2` and `randomForests`. The dataset used in this project is a sample from the [complete 2013 NYC taxi data](https://chriswhong.com/open-data/foil_nyc_taxi/), which was originally obtained and published by Chris Whong.
- 149999 New York taxi trips
- 2Cleaning the taxi data
- 3Zooming in on Manhattan
- 4Where does the journey begin?
- 5Predicting taxi fares using a tree
- 6It's time. More predictors.
- 7One more tree!
- 8One tree is not enough
- 9Plotting the predicted fare
- 10Plotting the actual fare
- 11Where do people spend the most?
Founder & Data Sherpa at bayescamp.com
Robert offers training and career coaching for statistics and data scientists, especially around data visualization and Bayesian modeling, using software like R, Stata, and Stan. Before setting up BayesCamp, he was a university statistics lecturer and healthcare researcher. He is a contributor to the open-source Bayesian software, Stan.
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