Analyzing Uber's data to extract valuable insights using Machine Learning

Uber was founded only eleven years ago but has become one of the largest companies in the world. UberX claims to charge 30% less than taxis in Boston. This is a great way to attract customers' attention. Today, we have seen many applications of machine learning and artificial intelligence in many fields, which led us to explore their use in predicting Uber taxi prices. In this project, we conducted an experiment using real-world data to explore the use of machine learning algorithms in identifying patterns in data. We mainly focus on using machine learning algorithms to predict the price of various Uber rides. Our problem is in the regression tracking training category. We use a variety of machine learning algorithms, such as linear regression, tree pruning, random forest regressors, and gradient boosting regressors, and finally choose the algorithm that produces the most accurate cost estimates. We should choose algorithms that maximize accuracy and minimize overfitting. We have a lot of experience preparing data for the Boston Uber dataset. It is also fascinating to learn about the many factors that affect Uber ride prices.