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Taxi Trajectory Prediction using Decision Trees, Numpy, Pandas, Scipy in R

Given a partial trajectory of a taxi, you will be asked to predict its final destination using the taxi trajectory dataset.
4.44.4

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What will you learn

  • Python Numpy
  • Python Pandas
  • Scipy
  • Scikit
  • Matplotlib
  • Decision Tree Algorithm
  • Categorical Data

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Prerequisites

  • Language used: R

Project Description

It is important to predict the final destination of a taxi to enhance the efficiency of electronic taxi dispatching systems. When there is high demand, often there could be a taxi whose current ride can end near or exactly at the requested pick up location from a new rider. Predicting the final destination of taxi will help the dispatcher know where the driver would be ending their current ride so they can identify which taxi should be assigned for the next pickup request.

In this data science project, you will predict the destination of a taxi given the variable-length sequence of GPS points which represent the beginnig of its trajectory and other information in the taxi trajectory dataset such as taxi id, client information, date and time. The training data has details of all the taxi rides from 2013-2014 in the city of Porto, Portugal.  The taxi trajectory dataset represent around 1.7 million rides run by 442 taxis.

Instructors

 
Jeeban

Senior Statistical Analyst

"I enjoy 3 things in Analytics - Machine Learning, Image/Video Processing, Natural Language Processing."