Create sample dataset

[melissa-ng]

We need a dataset in csv format to train a machine learning model.

• [ ] Task 1: Create dataset
• [ ] Task 2: Save dataset as csv file

[Chimp56]

Features:

• lot_id: unique identifier for the parking lot (integer 1-[number of lots])

• floor: floor number of the parking garage

• timestamp: date and time of the observation

• day_of_week: day of the week of the observation (can be done with datetime module in Python)

• year: year of the observation

• month: month of the observation

• day: day of the observation

• hour: hour of the observation

• minute: minute of the observation

• second: second of the observation

• reported_vacant_spots: number of parking spots reported as vacant on the floor

• floor_total_spots: total number of parking spots on the floor

• lot_total_spots: total number of parking spots on the floor (likely correlated with lot_id, so may not be necessary)

• floor_type: main type of floor in the parking garage (e.g. commuter, faculty/staff, visitor) (1 - commuter, 2 - faculty/staff, 3 - visitor, 4 - housing, 5 - multi-use, 6 - reserved, 7 - service

• temperature: temperature at the time of the observation

• precipitation: amount of precipitation at the time of the observation

• wind_speed: wind speed at the time of the observation

• is_game_day: whether there is a game at the nearby stadium on the day of the observation

• holiday: whether it is a holiday on the day of the observation

Targets:

• vacant_spots: number of vacant parking spots on the floor

[Chimp56]

We should remove the following features: "temperature": 20.5, "precipitation": 0.0, "wind_speed": 0.0, "is_game_day": 0, "holiday": 0