Episode

Improving Pumpkin Price Predictions with Linear and Polynomial Regression using Scikit-learn (Part 12 of 17) | Machine Learning for Beginners

with Bea Stollnitz

Machine Learning for Beginners

Join Bea Stollnitz, a Principal Cloud Advocate at Microsoft, as she explores linear and polynomial regression models for predicting pumpkin prices using Scikit-learn. This video is part of our Machine Learning for Beginners series, where we cover various machine learning topics and their implementation using Python code in Jupyter notebooks.

In this tutorial, we will work with the pumpkin dataset and continue adding code to our Jupyter notebook from the previous video.

In this video, you'll learn:

  • How to train and test linear and polynomial regression models
  • How to calculate mean squared error and coefficient of determination
  • How to visualize the results with Matplotlib

Will we find a better prediction model using more features? Watch to find out!

Stay tuned for the next video in this series, where we'll see if we can improve our model by using more features. See you there!

Chapters

  • 00:00 - Introduction
  • 00:33 - Create a linear regression model to predict pumpkin prices
  • 02:09 - Mean squared error
  • 02:22 - Coefficient of determination
  • 02:50 - Calculate the slope and intercept from the model
  • 03:23 - Create a polynomial regression model
  • This course is based on the free, open-source, 26-lesson ML For Beginners curriculum from Microsoft.
  • The Jupyter Notebook to follow along with this lesson is available!

Connect

Azure Machine Learning
Python

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