README.md

---
license: mit
language:
- en
pretty_name: HW1 Tabular Dataset (Airlines)
---


# Dataset Card for {{ pretty_name | default("Dataset Name", true) }}

This dataset covers 30 flights leaving Pittsburgh International Airport between 9/7 and 9/27. It includes information on
the airline, weekday, flight time, layovers, days from departure, and price


## Dataset Details

### Dataset Description

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- **Curated by:** Carnegie Mellon University: 24-679
- **Shared by [optional]:** Devin DeCosmo
- **Language(s) (NLP):** English
- **License:** MIT

### Dataset Sources [optional]

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- **Repository:** {{ repo | default("[More Information Needed]", true)}}


## Uses

The main use was to train tabular machine learning models to predict the price of tickets based on the outlined features. 

### Direct Use

The direct use would be price prediction for airline flights. 

### Out-of-Scope Use

This could be used to predict other features or future prices, locations, or airlines in Pittsburgh.

## Dataset Structure

This dataset is in a tabular format with features
Airline, Destination, Day of the Week, Days from Departure, Flight_Time_Minutes, and Price

The two splits are original and augmented
The original has 30 rows.
The augmented has 300rows. 

## Dataset Creation

### Curation Rationale

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### Source Data

Source data is from Google Flights

#### Data Collection and Processing

Data for this was collected directly through Google Flights then tabulated by Google Gemini

#### Who are the source data producers?

Data was initially produced by Google Flights. 

## Bias, Risks, and Limitations

This is a very small data set and will likely have issues with training and fitting, especially for specific regression problems surrounding price.

### Recommendations

This dataset probably has limited accuracy as a first draft but may be useful for learning how to train tabular models.