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To predict the trip time duration of the Taxis in New Tork City.

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Manishk12/New-York-City-Taxi-Trip-Duration

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New-York-City-Taxi-Trip-Duration

To predict the trip time duration of the Taxis in New Tork City.

The general flow is as follows:

-Understanding the data
-Validation Strategy
-Create a baseline model with basic variables
-Feature Engineering
-Building various models and parameter tuning
-Ensembling / stacking.

Data fields

-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

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To predict the trip time duration of the Taxis in New Tork City.

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