End-to-end predictive analysis on Uber data

Kalal Nanditha , Kadarla Mani Chandana , Kattasavugari Chaithanya Kumar Reddy

End-to-end predictive analysis on Uber data, Machine Learning

We have choosen this project as it mainly focuses on the Uber data, which has become one of the most trending apps these days. Companies have used data analytics to improve and expand their performance since decades. Visualisation and data analysis have helped us in many ways, including detecting new trends, examining correlations and patterns in the data, doing in-depth research, and, the cherry on top, drawing conclusions from these patterns. For all of the benefits that this notion offers, it is necessary that we study it in depth over time. Using Machine learning algorithms in making the right analysis from the data ,that helps in making a decision.The varying cost of Uber rates is probably going to be significantly influenced by the weather. Various climate factors will have different impacts on the price rise. and at various levels: We believe that weather conditions like cloudiness or clarity do not have the same impact on inflation rates as weather conditions like snow or fog. Addressing the day of the week, recognising weekends and weekends is essential as individuals frequently engage in distinct activities, visit different locations, and retain a different mode of transportation during weekends and weekends.

Published Paper   E-Certificate

"End-to-end predictive analysis on Uber data ", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 3, page no.c91-c97, March-2023, Available :https://ijnrd.org/papers/IJNRD2303215.pdf

Volume 8 Issue 3, March-2023

Pages : c91-c97

Paper Reg. ID: IJNRD_188448

Published Paper Id: IJNRD2303215

Downloads: 000118842

Research Area: Engineering

Country: Hyderabad , Telangana , India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2303215

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303215

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition