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
Abstract :
- 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.
- 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.
