• Abstract:

In the last few years, the number of for-hire vehicles operating in NY has grown from 63,000 to more than 100,000. However, while the number of trips in app-based vehicles has increased from 6 million to 17 million a year, taxi trips have fallen from 11 million to 8.5 million. Hence, the NY Yellow Cab organization decided to become more data-centric. Then we have apps like Uber, OLA, Lyft, Gett, etc. how do these apps work? After all, that set price is not a random guess.

• Problem Statement:

Given pickup and dropoff locations, the pickup timestamp, and the passenger count, the objective is to predict the fare of the taxi ride using Random Forest.

• Dataset Information:

unique_id: A unique identifier or key for each record in the dataset

date_time_of_pickup: The time when the ride started

longitude_of_pickup: Longitude of the taxi ride pickup point

latitude_of_pickup: Latitude of the taxi ride pickup point

longitude__of_dropoff: Longitude of the taxi ride dropoff point

latitude_of_dropoff: Latitude of the taxi ride dropoff point

no_of_passenger: count of the passengers during the ride

amount: (target variable)dollar amount of the cost of the taxi ride

• Scope:

● Prepare and analyse data ● Perform feature engineering wherever applicable ● Check the distribution of key numerical variables ● Training a Random Forest model with data and check it’s performance ● Perform hyperparameter tuning