Back to Catalog

Create confusion matrices and compute metrics with Python

BeginnerGuided Project

Confusion matrices are a common and useful technique for classification tasks. They provide a perspective on the accuracy and effectiveness of algorithms. In this project, work with confusion matrices and classification accuracy as we analyze the effectiveness of spam detection. Uncover valuable insights into sensitivity, specificity, accuracy, and precision. Join us on a journey of discovery through the matrix of classification metrics for some effective spam detection.

4.3 (12 Reviews)

Language

  • English

Topic

  • Machine Learning

Enrollment Count

  • 76

Skills You Will Learn

  • Machine Learning, Python, sklearn, Scikit-learn

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 15 minutes

Platform

  • SkillsNetwork

Last Update

  • December 13, 2025
About this Guided Project
In this project, you get to explore different metrics that can be from a confusion matrix, a fundamental tool in evaluating the performance of classification models. Learn how to derive different metrics such as accuracy, specificity, sensitivity, and precision with a practical problem - spam detection. Learn different ways to visualize confusion matrices and engage in exercises to build skills that are needed in classification problems. Join us on this journey to enhance your understanding of classification metrics and strengthen your ability to combat spam effectively.

This hands-on project is based on the Create a confusion matrix with Python tutorial. The guided project format combines the instructions of the tutorial with the environment to execute these instructions without the need to download, install, and configure tools.

A look at the project ahead

Tell your audience what they can expect to learn. Better yet, tell them what they will be able to do as a result of completing your project:
  • Use Python to create confusion matrices
  • Learn to derive different measures from a confusion matrix mathematically
  • Derive measured from a confusion matrix with sklearn

What you'll need

  • No installation required: Everything is available in the JupyterLab, including any Python libraries and data sets.
  • Basic understanding of Python: Some basic understanding of Python will be beneficial.
  • Some understanding of statistical concepts: It's helpful to have some understanding of statistic concepts, particularly terms like accuracy, specificity, and precision.

Instructors

Lucy Xu

Data Scientist

I am a Data Scientist Intern at IBM. I am also currently in my fourth year at the University of Waterloo studying Statistics with a minor in Computing.

Read more

Contributors

Wojciech "Victor" Fulmyk

Data Scientist at IBM

Wojciech "Victor" Fulmyk is a Data Scientist and AI Engineer on IBM’s Skills Network team, where he focuses on helping learners build expertise in data science, artificial intelligence, and machine learning. He is also a Kaggle competition expert, currently ranked in the top 3% globally among competition participants. An economist by training, he applies his knowledge of statistics and econometrics to bring a distinctive perspective to AI and ML—one that considers both technical depth and broader socioeconomic implications.

Read more

Kang Wang

Data Scientist

I was a Data Scientist in the IBM. I also hold a PhD from the University of Waterloo.

Read more