Uber Data Analysis Using Machine Learning

Uber Data Analysis Using Machine Learning

Syeda Hiba Fathima1, Tharun M Kumaran2, Sumedha N3 Ms. Sapna R4,

Final Year Students, Department of Computer Science Engineering, Presidency University, Bengaluru, Karnataka, India1,2,3.

Assistant Professor Department of Computer Science Engineering, Presidency University, Bengaluru, Karnataka, India4.

Abstract:

Uber is one of the fastest-growing companies in the world and is defined as a P2P platform. The platform that links you to drivers who can take you to your destination The paper explains the workings of an Uber dataset from Boston in 2018. In this paper, we experimented with a real-world dataset and explored how machine learning algorithms could be used to find patterns in the data. We mainly analysed the price prediction of different Uber cabs by using machine learning algorithms like linear regression, decision tree, random forest regression, and gradient boosting regression, and finally chose the one that proved best for the price prediction. We must choose an algorithm that improves accuracy and reduces overfitting.

Keywords: Machine Learning, Dataset, Price Prediction, Boston, Linear Regression, Decision Tree, Random Forest Regressor, Gradient Boosting Regressor, flask.