Popular taxi services such as Uber and Ola provide their users with a prediction of taxi fare before the customer is mapped to a driver.
This case study is to predict the taxi fare for a taxi ride in New York City from a given pickup point to the agreed dropoff location. Random Forest regressor is used for the fare prediction.
We try to provide a similar solution using the Machine Learning technique. The intention is to process voluminous data and perform parallel feature engineering and deploy a prediction engine on top of it.
Hyperparameter tuning - Grid Search CV has been used to boost the efficiency of the model.