Machine Learning Python Challenge: Regression
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The hands-on lab is part of this learning path
Advanced
1h 30m
62
Lab challenges are hands-on labs with the gloves off. You jump into an auto-provisioned cloud environment and are given a goal to accomplish. No instructions, no hints. To pass, you'll have a limited time to demonstrate your problem-solving skills and get the checks that inspect the state of your lab environment.
In this lab challenge, you will be tested on your scikit-learn skills to build a machine learning pipeline to predict the price of a stock. Here, you will be tested on data preprocessing, fitting, and evaluation of the regression 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.
You are strongly encourage to have completed 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.
Updates
September 20th, 2021 - Updated Python libraries to resolve an issue using the Yahoo APIs
What will be assessed
- Your ability to create a machine learning pipeline
- Your ability to fit a linear regression model
- Your ability to evaluate a fitted model
Intended audience
- Machine Learning Engineers
- Data scientists
Prerequisites
- Knowledge of regression: Completion of the Building a Machine Learning pipeline with scikit-learn: part 02 course is highly recommended
- Knowledge of preprocessing: Completion of the Building a Machine Learning pipeline with scikit-learn: part 01 course is highly recommended
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.