Taxi Data Analysis using K-mean Clustering Algorithm


Authors : Dev Mishra; Manvik Sagar; Kartikey Gaur; Indrasen Gupta

Volume/Issue : Volume 8 - 2023, Issue 4 - April

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://bit.ly/3NRIcH8

DOI : https://doi.org/10.5281/zenodo.7912172

- In this research, we analyze taxi pickup data using k-means clustering to gain insights into the spatial distribution of pickups and identify areas with high demand. We apply a k-means clustering algorithm to group pickups into clusters based on their location and time, which helps us identify areas with high demand and plan our operations accordingly. To evaluate the performance of our clustering model, we use the inertia score, which measures the within-cluster sum of squares and indicates how well the data points are separated into different clusters. Our results show that our clustering model achieves a low inertia score of X, indicating that the data points are well separated into different clusters. This demonstrates the effectiveness of using k-means clustering for taxi data analysis and highlights the importance of evaluating clustering models using appropriate metrics.

Keywords : Taxi data analysis, machine learning, regression analysis, k-means clustering, prediction scheduling, latitude and longitude data, transportation data, urban mobility, data visualization, data pre-processing.

CALL FOR PAPERS


Paper Submission Last Date
31 - March - 2024

Paper Review Notification
In 1-2 Days

Paper Publishing
In 2-3 Days

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe