hands-on lab

Evaluating Model Predictions for Regression Models

Intermediate
Up to 43m
357
4.2/5
Get guided in a real environmentPractice with a step-by-step scenario in a real, provisioned environment.
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Lab description

How do you know if a regression model is a good estimator of what you are trying to predict?
This lab will walk you through building several multivariate linear regression models using different prediction variables and then comparing the model predictions using evaluation tools such as R-squared and Mean Squared Error (MSE).

Learning Objectives

Upon completion of this lab you will be able to:

  • Import data using pandas
  • Prepare data for modeling
  • Build a regression model using scikit-learn
  • Evaluate the regression model using statistics such as R2 and mean squared error
  • Compare multiple models using regression evaluation metrics

Intended Audience

This lab is intended for:

  • Machine learning engineers
  • Anyone interested in evaluating machine learning model performance

Prerequisites

You should possess:

  • A basic understanding of Python
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Calculated Systems was founded by experts in Hadoop, Google Cloud and AWS. Calculated Systems enables code-free capture, mapping and transformation of data in the cloud based on Apache NiFi, an open source project originally developed within the NSA. Calculated Systems accelerates time to market for new innovations while maintaining data integrity.  With cloud automation tools, deep industry expertise, and experience productionalizing workloads development cycles are cut down to a fraction of their normal time. The ability to quickly develop large scale data ingestion and processing  decreases the risk companies face in long development cycles. Calculated Systems is one of the industry leaders in Big Data transformation and education of these complex technologies.

Covered topics
Lab steps
Logging In to the Amazon Web Services Console
Opening the Lab's Jupyter Notebook
Solutions to Evaluating Model Predictions for Regression Models