Data Analysis and Fair Price Prediction Using Machine Learning Algorithms

K. VinayKumar; Santosh N.C; Narasimha reddy soor

doi:10.69996/jcai.2024004

Authors

K. VinayKumar Assistant Professor, Department of CSE, Kakatiya Institute of Technology and Science, Warangal,India Author
Santosh N.C Assistant Professor, Department of CSE, Kakatiya Institute of Technology and Science, Warangal,India Author
Narasimha reddy soor Associate Professor,Department of CSN,Kakatiya Institute of Technology and Science,Warangal,505001,India. Author

DOI:

https://doi.org/10.69996/jcai.2024004

Keywords:

Data analysis, uber data, fare price detection, machine learning algorithms, accuracy

Abstract

Data Analysis is the main important subject in recent times as the ongoing demand for it is growing accordingly with the huge amounts of data we get from many sources, All the huge data we get should be properly analysed so that the information will be used accordingly to its needs. In this paper, the main objective is to analyse the data that is taken from uber related data from a csv file which is already available in the outside world. In addition to the analysing of data we are also including two features to our project, from which the first one is fare price detection and the second one goes with optimal allotment of a cab using appropriate machine learning algorithms. we used k- means clustering, Db Scan for optimal allotment of a cab. We used Linear regression, Logistic Regression ,Random Forest algorithms, Decision Tree Algorithm for fair price detection. We are also finding the best algorithm for increasing the accuracy of selection using unsupervised algorithms which is the best moto of our project. The additional feature we are wanting to add in to this mechanism of analysing the data is to use an android app developed by us in order to receive the required data from the users and perform various actions on it to obtain a best result for segmented two operations.

References

[1] S. Silverstein, “These animated charts tell you everything about uber prices in 21 cities,” Business Insider, vol. 16, 2014.

[2] “Discover what uberx is and how it works,” accessed: 2018-07-18. [Online]. Available: https://www.uber.com/pt-BR/blog/o-que-e-uberx/

[3] P. Cohen, R. Hahn, J. Hall, S. Levitt et al., “Using big data to estimate consumer surplus: The case of uber,” National Bureau of Economic Research, Tech. Rep., 2016.

[4] J. Chao, “Modeling and analysis of uber’s rider pricing,” 01 2019.

[5] R. Srinivas, B. Ankayarkanni, and R. S. B. Krishna, “Uber related data analysis using machine learning,” in 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), 2021, pp. 1148–1153.

[6] A. Brodeur and K. Nield, “An empirical analysis of taxi, lyft and uber rides: Evidence from weather shocks in nyc,” Journal of Economic Behavior & Organization, vol. 152, pp.1–16,2018.

[7] A. Kumar, “Python – replace missing values with mean, median & mode.” [Online]. Available: https://vitalflux.com/pandas-imputemissing-values-mean-median-mode/

[8] B. Annamalai and S. Sundarraj, “An approach to predict taxi-passenger demand using quantitative histogram on uber data,” IOP Conference Series: Materials Science and Engineering, vol.04, pp. 1–4, 2019.

[9] R. Pradhan, P. Mannepallli, and V. Rajpoot, “Analysing uber trips using pyspark,” IOP Conference Series: Materials Science and Engineering, vol. 1119, p. 012013, 03 2021.

[10] L. Moreira-Matias, J. Gama, M. Ferreira et al., “Predicting taxi- passenger demand using streaming data,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, pp. 1393–1402, 09 2013.

[11] A. Zheng and A. Casari, “Feature engineering for machine learning: principles and techniques for data scientists, ” O’Reilly Media, Inc., 2018.

[12] M. K. Chen and M. Sheldon, “Dynamic pricing in a labor market: Surge pricing and flexible work on the uber platform,” Ec, vol. 16, p. 455, 2016.

[13] H. B. Barlow, "Unsupervised learning,Neural computation”, Vol.1, No.3,pp.295-311, 1989.

[14] L. Rokach,.and O. Maimon,”Clustering methods,” Data mining and knowledge discovery handbook, Springer, pp.321-352, 2005.

[15] P. Baldi, "Auto encoders, unsupervised learning, and deep architectures,Proceedings of ICML workshop on unsupervised and transfer learning,” pp.37-49, 2012.

[16] M. B. Rozenwald, A. A. Galitsyna, G. V. Sapunov, et al.,” A machine learning framework for the prediction of chromatin folding in Drosophila using epigenetic features”. PeerJ Comput Sci. vol.6, no.307, 2020.

[17] X. An and W. Wang, “Knowledge management technologies and applications: A literature review”. IEEE, pp.138-141, 2010.

[18] A. Berson, S. J. Smith, and K. Thearling, “Building Data Mining Applications for CRM”. New York: McGraw-Hill, 1999.

[19] Ribeiro and A. R. D. Silva, “Survey on Cross-Platforms and Languages for Mobile Apps,” Eighth International Conference on the Quality of Information and Communications Technology, 2012.

[20] S. S. Jagtap and D. B. Hanchate, “Development of Android Based Mobile App for PrestaShop eCommerce Shopping Cart (ALC) ,” International Research Journal of Engineering and Technology (IRJET), vol. 4, no. 7, pp. 2248–2254, Jul. 2017.

[21] N. Litayem, B. Dhupia, and S. Rubab, “Review of Cross-Platforms for Mobile Learning Application Development,” International Journal of Advanced Computer Science and Applications, vol. 6, no. 1,pp.31–3.