Improve Relevance through Weather Targeting - Demo

**Overview**
Weather targeting is a common pattern of use for AccuWeather customers especially in retail. It is the practice of targeting consumers based on local weather through serving advertisments, distributing promotions or campaigns. For example, during periods of time in Dallas, TX, when the temperatures rise above 65 degrees Fahrenheit, consumers of hot beverages make the switch to cold, prompting restaurants to switch their strategy and prepare for increasing demand for iced coffees, smoothies, and sparkling spritzers. Knowing which products are most influenced by changing weather conditions, which weather conditions hold the most signficance or relevance, and where, businesses can better plan for, adapt to, and take advantage of consumer purchasing behaviors.

**Scenario**
This Databricks Notebook will provide an example of an exploratory analysis of categorical product sales information along with a goal of predicting when a certain product category will expect an increase in weekly sales based on a single weather condition. This analytical process can be reviewed like a template for those interested in weather-based triggering for their own business needs.

Data used within this analysis is provided publicly through US Department of Agriculture Data Products which contains historical weekly retail sales data by product category.

**Dataset details**
- Daily Historical Weather in a Weekly Rollup (AccuWeather)
- Weekly Retail Sales ([USDA](https://www.ers.usda.gov/webdocs/DataFiles/100189/StateAndCategory.xlsx?v=7373.2))

For more details, refer to the embedded notebook.