lab challenge

Machine Learning Python Challenge: Classification

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Lab description

The aim of this lab is to challenge you on building a supervised machine learning pipeline to predict the probability that a subject will suffer from a heart stroke. Here, you will be tested on data preprocessing, fitting, and evaluation of a classification model.

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

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

as well as the following lab:

before starting this challenge.

Prerequisites
  • Knowledge of classification Completion of the Building a Machine Learning pipeline with scikit-learn: part 02 course is highly recommended
Intended audience
  • Machine learning engineers
  • Data scientists
What will be assessed
  • Your ability to create a machine learning pipeline
  • Your ability to fit a logistic regression model
  • Your ability to evaluate a classification model
About the author
Avatar
Andrea Giussani
Data Scientist
Students
5,677
Labs
13
Courses
8
Learning paths
6

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.

Covered topics
Mission
Machine Learning Challenge: Classification