Predicting Demand of Beer Styles

GOAL:

Help brewers make the right beer in the right place at the right time

But Why Beer Models?

• Big industry
• Major year-over-year growth
• Few players in the data/beer space
• New players need data to succeed and represent an opportunity

Data Science Approach

Get data:

• Sales data and consumption data from various sources
• Untappd, OpenBeerDB, FRED, Twitter

Clean and transform data:

• Load all data into uniform RDBMS

Explore:

• Do exploratory analysis to understand correlations and trends

Model:

• Build and test various models to predict ratings, sentiment, and consumption location, including multiple models by style and location

Present and polish:

• Visualize models in a Shiny dashboard for breweries to use

Next Steps

• Build out website with interactive dashboard based on demo
• Add sentiment aggregated by style to supplement maps
• Adapt style model to account for regional biases in data
• Keep looking for new data sources to incorporate
• Get more feedback from breweries of what problems they have that data science can solve