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.


Each project comes with 2-5 hours of micro-videos explaining the solution.

Code & Dataset

Get access to 50+ solved projects with iPython notebooks and datasets.

Project Experience

Add project experience to your Linkedin/Github profiles.

Customer Love

Read All Reviews

Camille St. Omer

Artificial Intelligence Researcher, Quora 'Most Viewed Writer in 'Data Mining'

I came to the platform with no experience and now I am knowledgeable in Machine Learning with Python. No easy thing I must say, the sessions are challenging and go to the depths. I looked at graduate... Read More


Lead Consultant, ITC Infotech

The project orientation is very much unique and it helps to understand the real time scenarios most of the industries are dealing with. And there is no limit, one can go through as many projects... Read More

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. 


Similar Projects

Forecast the business for the upcoming years by Exploring Hidden Trends, Calculating Machine Productivity , Extrapolation and Assumptions and Summarizing Answers through Visualizations.

In this project, we are going to talk about insurance forecast by using regression techniques.

Using this Kaggle dataset, you will explore which type of employees make less or more money, or which employees get normal pay hikes and promotions.

Curriculum For This Mini Project

03h 21m