How to Implement Linear Regression From Scratch in Python

Last Updated on August 13, 2019

The core of many machine learning algorithms is optimization.

Optimization algorithms are used by machine learning algorithms to find a good set of model parameters given a training dataset.

The most common optimization algorithm used in machine learning is stochastic gradient descent.

In this tutorial, you will discover how to implement stochastic gradient descent to optimize a linear regression algorithm from scratch with Python.

After completing this tutorial, you will know:

  • How to estimate linear regression coefficients using stochastic gradient descent.
  • How to make predictions for multivariate linear regression.
  • How to implement linear regression with stochastic gradient descent to make predictions on new data.

Kick-start your project with my new book Machine Learning Algorithms From Scratch, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

  • Update Jan/2017: Changed the calculation of fold_size in cross_validation_split() to always be an integer. Fixes issues with Python 3.
  • Update Aug/2018: Tested and updated to work with Python 3.6.
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