Equipped with good data, the avocado pricing and supply problem is ripe with opportunities for demonstrating the power of optimization and data science. This example demonstrates how predictive and prescriptive analytics can optimize avocado prices to maximize revenue. We will use regression and quadratic programming to achieve this goal. We will demonstrate how to implement this model in the Gurobi Python API, and generate an optimal solution using the Gurobi Optimizer.
This modeling tutorial is at the introductory level, where we assume that you know Python and that you have a background on a discipline that uses quantitative methods.
Part I uses an ordinary linear regression model (OLS) to establish the relationship between price and demand based on data from the Hass Avocado Board. Part II replaces the OLS model with a trained
Scikit-learn model and uses the Gurobi Machine Learning package to embed it in a Gurobi optimization model.
You may find it helpful to refer to the documentation of the Gurobi Python API. This notebook is explained in detail in our webinar on data science and mathematical optimization. You can watch these videos by clicking here.
Click on the button below to access the example in Google Colab, which is a free, online Jupyter Notebook environment that allows you to write and execute Python code through your browser.
Check out the Colab Getting Started Guide for full details on how to use Colab Notebooks as well as create your own.
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