Problem Statement

Taxi Fare Prediction
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: Column Description
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

Codes for the above problem