Considering the everyday increase in popularity of uber and to enhance transparency, to improve user experience, to make better decision-making for both users and the company, it got crucial to build an app to optimize its pricing strategy.
The goal of this project is to develop a machine learning model that can accurately predict the fare of an Uber ride given specific input features such as the pickup and dropoff locations, time of day, and other relevant factors.
The details of the dataset can be found in the below metadata.
key - A unique identifier for each trip
fare_amount - The cost of each trip in usd
pickup_datetime - Date and time when the meter was engaged
passenger_count - The number of passengers in the vehicle (driver entered value)
pickup_longitude - The longitude where the meter was engaged
pickup_latitude - The latitude where the meter was engaged
dropoff_longitude - The longitude where the meter was disengaged
dropoff_latitude - The latitude where the meter was disengaged
The Gradient boosting regressor model and the random forest regressor model both worked well, however, GB has better result, I selected Gradient boosting regressor model.
In conclusion, the Uber Fare Prediction project successfully demonstrates the application of machine learning to predict ride costs based on various factors such as distance, time, and location. By developing this model, we enhance transparency for users, allowing them to anticipate fare costs more accurately, which improves their overall experience. Additionally, the model provides valuable insights for Uber, enabling the company to manage demand more efficiently and optimize pricing strategies. This project lays the groundwork for future enhancements, such as integrating real-time data, expanding to other ride-sharing platforms, and developing personalized fare prediction models. Overall, the project not only benefits users by offering cost predictability but also supports Uber in maintaining a competitive edge in the dynamic ride-sharing market.