Feature Selection for Forecasting

In this jupyter notebook modeling example, we look at a feature selection forecasting problem where we will solve a linear regression problem

In this example, you will learn how to perform linear regression with feature selection using mathematical programming. We’ll show you how to construct a mixed-integer quadratic programming (MIQP) model of this linear regression problem, implement this model in the Gurobi Python API, and generate an optimal solution.

This modeling example is at the intermediate level, where we assume that you know Python and are familiar with the Gurobi Python API. In addition, you should have some knowledge about building mathematical optimization models.

 


 

Access the Jupyter Notebook Modeling Example

Click on the link 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.

 

Best Feature Selection for Forecasting

 

How to Run the Jupyter Notebook Modeling Example

-To run the example the first time, choose “Runtime” and then click “Run all”.

-All the cells in the Jupyter Notebook will be executed.

-The example will install the gurobipy package, which includes a limited Gurobi license that allows you to solve small models.

-You can also modify and re-run individual cells.

-For subsequent runs, choose “Runtime” and click “on “Restart and run all”.

-The Gurobi Optimizer will find the optimal solution of the modeling example.

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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