Bridging the Gap Between Data Scientists and Business Users

  • Building a predictive model:
  • In this step, we use the sklearn package for data preprocessing and model training to build a predictive model that predicts the profitability of restaurants based on their characteristics.
  • Building an optimization model:
  • In this step, we build and optimize a model to find the optimal investment strategy at the county level. The model seeks to maximize the total annual profitability while keeping the total investment budget below the limit.
  • Model deployment:
  • In this step, we use the TabPy package to deploy models and make them accessible through the Tableau platform for the end-users.
  • For example, a new report can be built to examine the impact of changing the budget limit and can be used for what-if analysis. In this example, it seems increasing the investment budget by 25% from $8M to $10M would lead to a 30% increase in profitability:

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ODSC - Open Data Science

ODSC - Open Data Science

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