The dataset is based on the 2016 NYC Yellow Cab trip record data made available in Big Query on Google Cloud Platform. The data was originally published by the NYC Taxi and Limousine Commission (TLC). The data was sampled and cleaned for the purposes of this playground. Based on individual trip attributes, should predict the duration of each trip.
NYC Taxi and Limousine Commission (TLC) : http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml
Kaggle : https://www.kaggle.com/c/nyc-taxi-trip-duration/data
Download Data : https://drive.google.com/open?id=1OyOC9y2x4uyT7drXJBOEZ2yRBktiQB8H
File descriptions :
● train.csv - the dataset (contains 1458644 trip records)
● id - a unique identifier for each trip
● vendor_id - a code indicating the provider associated with the trip record
● pickup_datetime - date and time when the meter was engaged
● dropoff_datetime - date and time when the meter was disengaged
● 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
● store_and_fwd_flag - This flag indicates whether the trip record was held in vehicle memory before sending to the vendor because the vehicle did not have a connection to the server - Y=store and forward; N=not a store and forward trip
● trip_duration - duration of the trip in seconds
Fitting different models For modeling, we tried various classification algorithms like: Linear regression with regularization (Lasso & Ridge) Decision Tree Random Forest regression XG boost Gradient Boosting
