This project show an analysis of the publicly available data set on liquor retail sales in the state of Iowa, US. The goal is to try and predict the monthly sales per liquor type for a particular store based on information from historical sales and weather data. The work consists of few steps, briefly described as:

1. Exploratory data analysis:

• Determine the data set attributes' type and range of allowed (or normal) values
• Plot a few summarizing bar and line charts to become familiar with attributes' value distribution and relationship (correlation)
  • Notice the (expected) cyclic trend of the sales
• Remove uninformative or not needed attributes (dimensionality reduction)

2. Transform the data:

• Detemine the different sources of data (liquor sales come from the web site of the government of Iowa, weather information comes from NOAA's website
• Merge the disparate sources
• Generate new features
  • Lag values for the weather and sales parameters

3. Do (simple) machine learning:

• The problem is simple univariate regression
• Try: linear, lasso and ridge regression
• Evaluate results on R^2 value

4. Summarize results

• Models perform more or less the same
• The evaluation period contains the outbreak of the COVID-19 pandemic and shows very interesting results (last plot in the notebook)
  • Total liquor retail sales in Iowa dropped for more than 90% during this period!
  • R^2 values become negative because the model is simply not able to predict this (to be fair, neither was any of us :))!

For more information, check out the report and presentation files. For the actual code, take a look at the Jupyter notebook.