Predictive Analysis of Taxi Fare using Machine Learning

Authors

  • Pallab Banerjee  Assistant Professor, Department of Computer Science and Engineering, Amity University Ranchi, Jharkhand, India
  • Biresh Kumar  Assistant Professor, Department of Computer Science and Engineering, Amity University Ranchi, Jharkhand, India
  • Amarnath Singh  Assistant Professor, Department of Computer Science and Engineering, Amity University Ranchi, Jharkhand, India
  • Priyeta Ranjan  B.Tech Scholar, Department of Computer Science and Engineering, Amity University Ranchi, Jharkhand, India
  • Kunal Soni  B.Tech Scholar, Department of Computer Science and Engineering, Amity University Ranchi, Jharkhand, India

DOI:

https://doi.org//10.32628/CSEIT2062108

Keywords:

Machine Learning, Fare Prediction, Predictive Analysis, Supervised Learning, Feature Selection.

Abstract

This research aims to study the predictive analysis, which is a method of analysis in Machine Learning. Many companies like Ola, Uber etc uses Artificial Intelligence and machine learning technologies to find the solution of accurate fare prediction problem. We are proposing this paper after comparative analysis of algorithms like regression and classification, which are useful for prediction modeling to get the most accurate value. This research will be helpful to those, who are involved in fare forecasting. In previous era, the fare was only dependent on distance, but with the enhancement in technologies the cab’s fare is dependent on a lot of factors like time, location, number of passengers, traffic, number of hours, base fare etc. The study is based on Supervised learning whose one application is prediction, in machine learning.

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Published

2020-04-30

Issue

Section

Research Articles

How to Cite

[1]
Pallab Banerjee, Biresh Kumar, Amarnath Singh, Priyeta Ranjan, Kunal Soni, " Predictive Analysis of Taxi Fare using Machine Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 2, pp.373-378, March-April-2020. Available at doi : https://doi.org/10.32628/CSEIT2062108