Machine Learning with scikit-learn

Lab Steps

Machine Learning with Python - Data Transformation

The hands-on lab is part of this learning path

Ready for the real environment experience?

Time Limit1h


The aim of this lab is to challenge you on building a supervised machine learning pipeline to predict the median values of owner-occupied housing in USD 1000, denoted as MEDV. We are going to famous Boston dataset, which contains a set of different features that are used to predict the MEDV target variable. Here, you will be guided with an hands-on exercise on data preprocessing, fitting and evaluation of a regression model.

To get the most from this lab, it is recommended to have confidence and exposure to the following libraries: `pandas`, `matplotlib` and `scikit-learn`.

I strongly encourage you to have watched the following courses, available in our content library:

before starting this lab.


Learning Objectives

Upon completion of this lab you will be able to:

  • Build a standard machine learning pipeline with scikit-learn;
  • Scale a dataset using the StandardScaler transformer;
  • Train a Ridge Regression Model;
  • Fit a scikit-learn Pipeline object with a GridSearchCV.

Intended Audience

This lab is intended for:

  • Those interested in performing machine learning with Python.
  • Anyone involved in data science pipelines.


You should possess:

  • An intermediate understanding of Python.
  • Basic knowledge of the following libraries: pandas, scikit-learn, matplotlib, seaborn.
About the Author
Learning paths4

Andrea is a Data Scientist at Cloud Academy. He is passionate about statistical modeling and machine learning algorithms, especially for solving business tasks.

He holds a PhD in Statistics, and he has published in several peer-reviewed academic journals. He is also the author of the book Applied Machine Learning with Python.