Predict Taxi Fares with Decision Tree and Random Forests
In this project, I use regression trees and random forests to find places where New York taxi drivers earn the most. The workbook demonstrate how the model worked with the data from a large number of taxi journeys in New York from 2013. Regression trees and random forests are used to predict the value of fares and tips, based on location, date and time.
The dataset used in this project is a sample from the complete 2013 NYC taxi data, which was originally obtained and published by Chris Whong.