7 datasets found
  1. f

    Taxi Fare Classification Using Decision Tree Classifier Algorithm To...

    • figshare.com
    application/csv
    Updated Feb 15, 2024
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    NIYIGENA Irene Richard (2024). Taxi Fare Classification Using Decision Tree Classifier Algorithm To Passengers In Different Location Of Yogyakarta City In Indonesia [Dataset]. http://doi.org/10.6084/m9.figshare.25224002.v1
    Explore at:
    application/csvAvailable download formats
    Dataset updated
    Feb 15, 2024
    Dataset provided by
    figshare
    Authors
    NIYIGENA Irene Richard
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Yogyakarta City, Yogyakarta, Indonesia
    Description

    To improve the accuracy of the charge figure, our ponder employments a choice tree classifier strategy and a demonstrate choice change. Within the case of preparing information, both strategies are utilized. To spare our information in a genuine dataset, we to begin with connected a show choice prepare. Moment, we utilized the choice tree classifier approach to resolve the issues with our dataset. At long last, based on the discoveries, we made a expectation in agreement with the time, date, number of travelers, and separate between both the pick-up and drop-off areas evaluated utilizing longitude and scope information by utilizing Python Libraries.

  2. Cab Fare Prediction AI Challenge

    • kaggle.com
    zip
    Updated May 31, 2021
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    Rahul Singh Maures (2021). Cab Fare Prediction AI Challenge [Dataset]. https://www.kaggle.com/rahulsingh731/cab-fare-prediction-ai-challenge
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    zip(1168871 bytes)Available download formats
    Dataset updated
    May 31, 2021
    Authors
    Rahul Singh Maures
    Description

    Dataset

    This dataset was created by Rahul Singh Maures

    Contents

  3. New York City Taxi Fare Prediction

    • kaggle.com
    Updated Sep 13, 2018
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    DanB (2018). New York City Taxi Fare Prediction [Dataset]. https://www.kaggle.com/datasets/dansbecker/new-york-city-taxi-fare-prediction/discussion
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 13, 2018
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    DanB
    Area covered
    New York
    Description

    Dataset

    This dataset was created by DanB

    Contents

  4. New York City Taxi Fare BigQuery Dataset

    • kaggle.com
    zip
    Updated Feb 12, 2019
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    DJ Sterling (2019). New York City Taxi Fare BigQuery Dataset [Dataset]. https://www.kaggle.com/dster/nyc-taxi-fare-bigquery-dataset
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    zip(0 bytes)Available download formats
    Dataset updated
    Feb 12, 2019
    Authors
    DJ Sterling
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    New York
    Description

    BigQuery table with the training and test datasets for the New York City Taxi Fare Prediction Competition

  5. NYC_taxi_reduced

    • kaggle.com
    Updated Mar 10, 2021
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    Stephan Laage-Witt (2021). NYC_taxi_reduced [Dataset]. https://www.kaggle.com/stephanlaagewitt/nyc-taxi-reduced/discussion
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 10, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Stephan Laage-Witt
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    New York
    Description

    Subset of training and test datasets for the New York City Taxi Fare Prediction

  6. Cab Price Prediction

    • kaggle.com
    zip
    Updated Aug 5, 2021
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    Shubham Varshney (2021). Cab Price Prediction [Dataset]. https://www.kaggle.com/shakshyathedetector/cab-price-prediction
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    zip(161947 bytes)Available download formats
    Dataset updated
    Aug 5, 2021
    Authors
    Shubham Varshney
    Description

    Dataset

    This dataset was created by Shubham Varshney

    Contents

    It contains the following files:

  7. m

    CAB Stock Price Predictions

    • meyka.com
    json
    Updated May 12, 2025
    + more versions
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    MEYKA AI (2025). CAB Stock Price Predictions [Dataset]. https://meyka.com/stock/CAB/forecasting/
    Explore at:
    jsonAvailable download formats
    Dataset updated
    May 12, 2025
    Dataset provided by
    Meyka AI
    Authors
    MEYKA AI
    License

    https://meyka.com/licensehttps://meyka.com/license

    Time period covered
    Jun 17, 2025 - Jun 17, 2032
    Variables measured
    Weekly Forecast, Yearly Forecast, 3 Years Forecast, 5 Years Forecast, 7 Years Forecast, Monthly Forecast, Half Year Forecast, Quarterly Forecast
    Description

    AI-powered price forecasts for CAB stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
NIYIGENA Irene Richard (2024). Taxi Fare Classification Using Decision Tree Classifier Algorithm To Passengers In Different Location Of Yogyakarta City In Indonesia [Dataset]. http://doi.org/10.6084/m9.figshare.25224002.v1

Taxi Fare Classification Using Decision Tree Classifier Algorithm To Passengers In Different Location Of Yogyakarta City In Indonesia

Explore at:
application/csvAvailable download formats
Dataset updated
Feb 15, 2024
Dataset provided by
figshare
Authors
NIYIGENA Irene Richard
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Yogyakarta City, Yogyakarta, Indonesia
Description

To improve the accuracy of the charge figure, our ponder employments a choice tree classifier strategy and a demonstrate choice change. Within the case of preparing information, both strategies are utilized. To spare our information in a genuine dataset, we to begin with connected a show choice prepare. Moment, we utilized the choice tree classifier approach to resolve the issues with our dataset. At long last, based on the discoveries, we made a expectation in agreement with the time, date, number of travelers, and separate between both the pick-up and drop-off areas evaluated utilizing longitude and scope information by utilizing Python Libraries.

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