Linear and Polynomial Regression using Scikit-learn [Part 12] | 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!

Make sure to subscribe and hit the notification bell 🔔 so you won't miss our next video, where we'll see if we can improve our model by using more features. See you there!

0:00 - Intro
0:33 - Create a linear regression model to predict pumpkin prices
2:09 - Mean squared error
2:22 - Coefficient of determination
2:50 - Calculate the slope and intercept from the model
3:23 - Create a polynomial regression model

📙 Follow along:
The Jupyter Notebook to follow along with this lesson is available here:

#Python #ScikitLearn #LinearRegression #PolynomialRegression #DataScience #MachineLearning #ml

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