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:
- Building a Machine Learning pipeline with scikit-learn: part 01
- Building a Machine Learning pipeline with scikit-learn: part 02
as well as the following lab:
before starting this challenge.
- Knowledge of classification Completion of the Building a Machine Learning pipeline with scikit-learn: part 02 course is highly recommended
- Machine learning engineers
- Data scientists
- Your ability to create a machine learning pipeline
- Your ability to fit a logistic regression model
- Your ability to evaluate a classification model
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.