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  1. Familiarity Conda package, dependency and virtual environment manager. A handy additional reference for Conda is the blog post “The Definitive Guide to Conda Environments” on “Towards Data Science”.
  2. Familiarity with JupyterLab. See here for my post on JupyterLab.
  3. These projects will also run Python notebooks on VSCode with the Jupyter Notebooks extension. If you do not use VSCode, it is expected that you know how to run notebooks (or alter the method for what works best for you).

Getting started

Let’s create the regression-with-scikit-learn directory and install the required packages.

Exploring the Boston dataset

In this example, we will use the Boston housing dataset to predict the price of a house.

Visualizing the data

We can take the variables we have assign X_rooms and y to visualize the data.

Value of house /1000 ($) vs Number of rooms

Creating a regressor to predict a continuous target variable

Finally, we can build a linear regressor to predict the MEDV variable.

Adding the line to the data


Today’s post was an introduction to regression with Scikit Learn. We used the Boston dataset to predict the MEDV variable.

Resources and further reading

  1. Conda
  2. JupyterLab
  3. Jupyter Notebooks
  4. “The Definitive Guide to Conda Environments”
  5. okeeffed/regression-with-scikit-learn




Senior Engineer @ UsabilityHub. Formerly Culture Amp.

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Dennis O'Keeffe

Dennis O'Keeffe

Senior Engineer @ UsabilityHub. Formerly Culture Amp.

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