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--- |
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license: mit |
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language: |
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- en |
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pretty_name: HW1 Tabular Dataset (Airlines) |
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--- |
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# Dataset Card for {{ pretty_name | default("Dataset Name", true) }} |
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This dataset covers 30 flights leaving Pittsburgh International Airport between 9/7 and 9/27. It includes information on |
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the airline, weekday, flight time, layovers, days from departure, and price |
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## Dataset Details |
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### Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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{{ dataset_description | default("", true) }} |
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- **Curated by:** Carnegie Mellon University: 24-679 |
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- **Shared by [optional]:** Devin DeCosmo |
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- **Language(s) (NLP):** English |
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- **License:** MIT |
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### Dataset Sources [optional] |
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<!-- Provide the basic links for the dataset. --> |
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- **Repository:** {{ repo | default("[More Information Needed]", true)}} |
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## Uses |
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The main use was to train tabular machine learning models to predict the price of tickets based on the outlined features. |
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### Direct Use |
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The direct use would be price prediction for airline flights. |
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### Out-of-Scope Use |
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This could be used to predict other features or future prices, locations, or airlines in Pittsburgh. |
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## Dataset Structure |
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This dataset is in a tabular format with features |
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Airline, Destination, Day of the Week, Days from Departure, Flight_Time_Minutes, and Price |
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The two splits are original and augmented |
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The original has 30 rows. |
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The augmented has 300rows. |
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## Dataset Creation |
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### Curation Rationale |
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<!-- Motivation for the creation of this dataset. --> |
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{{ curation_rationale_section | default("[More Information Needed]", true)}} |
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### Source Data |
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Source data is from Google Flights |
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#### Data Collection and Processing |
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Data for this was collected directly through Google Flights then tabulated by Google Gemini |
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#### Who are the source data producers? |
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Data was initially produced by Google Flights. |
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## Bias, Risks, and Limitations |
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This is a very small data set and will likely have issues with training and fitting, especially for specific regression problems surrounding price. |
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### Recommendations |
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This dataset probably has limited accuracy as a first draft but may be useful for learning how to train tabular models. |
