Taxi Fare Prediction- Machine Learning

• Addressed the challenging problem of predicting taxi fares in New York City, utilizing machine learning

techniques to optimize route planning and enhance fare transparency.

• Developed a scalable machine learning model capable of efficiently processing large datasets,

incorporating various factors such as pickup/dropoff locations, time of day, distance traveled, and other

relevant features.

• Developed a robust machine learning model utilizing the LGBM regressor, achieving exceptional results

with an RMSE of 3.02 and an R2 of 84.6%, demonstrating accurate predictions of taxi fares in New

York City.