Grid Search Optimization Algorithm in Python



In this tutorial, we are going to talk about a very powerful optimization (or automation) algorithm, i.e. the Grid Search Algorithm. It is most commonly used for hyperparameter tuning in machine learning models. We will learn how to implement it using Python, as well as apply it in an actual application to see how it can help us choose the best parameters for our model and improve its accuracy. So let’s start.


To follow this tutorial, you should have a basic understanding of Python or some other programming language. It is preferred, but not essential, that you have some basic knowledge of machine learning as well. Other than that, this article is beginner-friendly and can be followed by anyone.


To go through the tutorial, you need to have the following libraries/frameworks installed in your system:

  1. Python 3
  2. NumPy
  3. Pandas
  4. Keras
  5. Scikit-Learn

They are all quite simple to install – you can click on each to go to their respective websites where detailed installation instructions are provided. Generally, the packages can be installed using

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