Machine Learning with scikit-learn
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:
- Building a Machine Learning pipeline with scikit-learn: part 1
- Building a Machine Learning pipeline with scikit-learn: part 2
before starting this lab.
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.
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.
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.