Bike rental prediction based on weather forecast with Autogluon

⚠️ NOTE: It is recommended to run this project on a Conda environment.

This project uses Autogluon to help train all different kinds of models and find which model best suits the purpose for this bike rental prediction.

In this project, Autogluon will use L2 Regression (a.k.a Ridge Regression) to train the models. The models trained by Autogluon will predict Divvy's Bike Rental Dataset based on the weather forecast.

If you would like to generate your own dataset, store the weather dataset by Visual Crossing into the chicago_weatherdata folder and the Divvy Bicycles trip dataset into the divvy_tripdata folder. After that, run the create_dataset.sh command.

The source code for the model prediction in the Bike Rental Predictor notebook file.

Here are the results you will get from this project:

Here are the features you need to take note before testing the model's prediction:

Feature	Description	Data Type	Unit of Measurement
week	The week for the date	Integer	Not Specified
day	The day for the date	Integer	Not Specified
month	The month for the date	Integer	Not Specified
year	The year for the date	Integer	Not Specified
temp	The weather temperature	Float	Degrees Fahrenheit
dew	The weather dew point	Float	Fahrenheit
humidity	The weather humidity	Float	Percentage (0-100)
precip	The weather precipitation	Float	Inches
snowdepth	The weather snow depth	Float	Inches
windgust	The weather wind gust	Float	Miles Per Hour
windspeed	The weather wind speed	Float	Miles Per Hour
sealevelpressure	The sea level pressure	Float	Millibar
cloudcover	The cloud coverage	Float	Percentage (0-100)
visibility	The visibility of the area	Float	Miles Per Hour
solarradiation	The radiation of the sun	Float	Watts Per Square Meter
solarenergy	The energy from the sun	Float	Millijoules Per Square Meter
uvindex	The sun's ultraviolet index	Float	Not Specified

Here are a list of applications that bike rental companies like Divvy can use with this project:

• Bike distribution (companies can balance the distribution of bikes to areas with better weather)
• Weather adjustments (if the weather for an area is not suitable for bike renting, companies can use this to close down those areas to save costs)
• Better Bike Availability (users can benefit from more available bikes, such as during peak hours)