Taxi Trip Time Prediction using Regression, Numpy, Scipy in R

Taxi Trip Time Prediction using Regression, Numpy, Scipy in R

In this machine learning project , you will predict the total travel time of taxi trips from their initial partial trajectories.


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What will you learn

Understanding the problem statement
Importing the train and test dataset
Installing packages from R and GoogleMaps in R
Initializing important libraries and understanding its functions
Calculating distances in R using latitude and longitudinal points
Plotting scatter plots between different variables
Performing basic EDA and checking for null values
Imputing null values
Learning different techniques for imputing categorical and numeric variables
Changing character to factor vector in R
Removing unnecessary variables
Time stamping
Binning and visualizing time stamped data
Make a new dataset through data table
Selecting the model for training the pre-processed Dataset
Defining parameters for the model
Defining evaluation metrics for evaluating the model
Applying Random Forest model
Making the final predictions and saving it in CSV format

Project Description

The goal of this machine learning project in R is to build a predictive model that can predict the total travelling time of 442 taxis running in the city of Porto based on their partial trajectories. This predictive framework will be used to enhance the efficiency of electronic taxi dispatching systems in Porto. You will use the taxi trajectory dataset from 01/07/2013 to 30/06/2014 containing the trajectories for all the 442 taxis running in the city of Porto. 


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Curriculum For This Mini Project

03h 21m