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Dataset Overview: This dataset presents an opportunity to construct predictive models aimed at estimating the total amount paid by travelers for taxi journeys. With access to a training set containing the target variable 'total_amount' along with various informative features, participants are challenged to create accurate predictive models.

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MLP_project

Dataset Overview:

This dataset presents an opportunity to construct predictive models aimed at estimating the total amount paid by travelers for taxi journeys. With access to a training set containing the target variable 'total_amount' along with various informative features, participants are challenged to create accurate predictive models.

Columns Description:

The dataset comprises various columns, each offering valuable insights into taxi rides. Notably:

  • total_amount: The total amount paid by the traveler for the taxi ride.
  • VendorID: An identifier for taxi vendors.
  • tpep_pickup_datetime and tpep_dropoff_datetime: Timestamps indicating pickup and dropoff times.
  • passenger_count: The number of passengers during the ride.
  • trip_distance: The distance traveled during the trip.
  • RatecodeID: Rate code for the ride.
  • store_and_fwd_flag: A flag indicating whether the trip data was stored and forwarded.
  • PULocationID and DOLocationID: Pickup and dropoff location identifiers.
  • payment_type: Payment type used for the ride.

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Dataset Overview: This dataset presents an opportunity to construct predictive models aimed at estimating the total amount paid by travelers for taxi journeys. With access to a training set containing the target variable 'total_amount' along with various informative features, participants are challenged to create accurate predictive models.

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