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Authors
Aditya Natrajan,APOORVA SP,ARPIT SHARMA,DARSHAN ND,DARSHAN S
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Keywords
Taxi Fares, Dataset, MSE, Paradigm
Abstract
Estimating taxi fares has been an important
research field within the transport industry since it homeworks
pricing trends and transparency of the system both for service
providers and customers. The project aims to find machine
learning models that predict taxi trip fares using numerous
variables, such as distance travelled during trips, time of the
day, traffic conditions, number of passengers, and weather.
Fare estimate will be addressed with robust data preprocessing,
optimal feature engineering, and advanced model training
using a synthetic dataset "constructed for practical regression
tasks.
The dataset is a rich source of over a 1000 data points with
value-key trips varying the trips duration and fare amount, as
well as contextual parameters like traffic and weather. This
dataset will provide you with real world problems like missing
values, outliers, correlation of features all together in one bundle.
Applying and comparing ML models like Random Forest and
Logistic regression and decision tree on this dataset based on
Realization above had proven that Random forest Model proved
the best with lower values in MSE, and was capable of fitting
even non-linear relationship between the features. Along with its
machine-learning train and evaluation functionality, it also
provides a lightweight mechanism for predicting fares from
input at application runtime.
It also aims to collect the data on traffic, updates from weather
and real-life datasets which could to be placed into next
architecture to enhance the data feeding and to improve the
model adaptability in future attempts. The predictive analytics
embodied in this work speaks to power as it pertains to the taxi
domain in such a way where it burns stronger in the empirical
sense given the scope of paradigm machine in the usage to
enhance fare predictions and decision making in transportation's
dynamic environment.
IJCRT's Publication Details
Unique Identification Number - IJCRT2501293
Paper ID - 275584
Author type - Indian Author
Page Number(s) - c554-c564
Pubished in - Volume 13 | Issue 1 | January 2025
DOI (Digital Object Identifier) -
No Of Downloads - 139
Author Country - India, 560004, BENGALURU, BENGALURU, 560004, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
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Aditya Natrajan,APOORVA SP,ARPIT SHARMA,DARSHAN ND,DARSHAN S, "TAXI FARE PREDICTION", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 1, pp.c554-c564, January 2025, Available at :http://www.ijcrt.org/papers/IJCRT2501293.pdf
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