Saltar al contenido principal
InicioR

proyecto

Predict Taxi Fares with Random Forests

Principiante
Actualizado 09/2024
Use regression trees and random forests to find places where New York taxi drivers earn the most.
Iniciar proyecto de forma gratuita

Incluido conPremium or Teams

RData VisualizationMachine Learning45 minutos11 Tasks1,500 XP8,857

Crea Tu Cuenta Gratuita

GoogleLinkedInFacebook

o

Al continuar, acepta nuestros Términos de uso, nuestra Política de privacidad y que sus datos se almacenan en los EE. UU.
Group

¿Entrenar a 2 o más personas?

Probar DataCamp for Business

Preferido por estudiantes en miles de empresas

Descripción del proyecto

Predict Taxi Fares with Random Forests

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.

Predict Taxi Fares with Random Forests

Use regression trees and random forests to find places where New York taxi drivers earn the most.
Iniciar proyecto de forma gratuita
  • 1

    49999 New York taxi trips

  • 2

    Cleaning the taxi data

  • 3

    Zooming in on Manhattan

  • 4

    Where does the journey begin?

  • 5

    Predicting taxi fares using a tree

  • 6

    It's time. More predictors.

  • 7

    One more tree!

  • 8

    One tree is not enough

  • 9

    Plotting the predicted fare

  • 10

    Plotting the actual fare

  • 11

    Where do people spend the most?

Únete a más 15 millones de estudiantes y empezar Predict Taxi Fares with Random Forests ¡Hoy!

Crea Tu Cuenta Gratuita

GoogleLinkedInFacebook

o

Al continuar, acepta nuestros Términos de uso, nuestra Política de privacidad y que sus datos se almacenan en los EE. UU.