Machine Learning for Beginners - Categorical Feature Predictions with Linear Regression [Part 13] | Machine Learning for Beginners

🎃 In this video, presented by Bea Stollnitz, a Principal Cloud Advocate at Microsoft, we'll dive into categorical features, understand what they are, when to use them, and how to create them to improve the performance of our regression models. We'll continue to work with the pumpkin dataset, which we've used in our previous videos, as we try to find the cheapest month to buy pumpkins. In this video, you'll learn: ✅ The importance of categorical features in predictions ✅ How to create categorical features using pandas 🐼 ✅ One hot encodings 🔥 ✅ Improving regression results with additional features Make sure to subscribe and hit the notification bell 🔔 so you won't miss our next set of videos, where we'll look at logistic regression. See you there! 0:00 - Intro 0:33 - Recap of the code run in previous video 0:50 - Can we improve our results with more features? 1:16 - Pumpkin varieties as strings don't work for regression 1:32 - Can we replace strings with numbers? No! 1:59 - Converting strings to categorical features using one hot encoding 2:40 - Improve the results using more features 3:03 - Improve the results further using polynomial regression 📙 Follow along: The Jupyter Notebook to follow along with this lesson is available here: https://github.com/microsoft/ML-For-Beginners/tree/main/2-Regression/3-Linear #Python #ScikitLearn #pandas #LinearRegression #PolynomialRegression #DataScience #MachineLearning #ml 📚 Learn more: This course is based on the free, open source, 26 lesson ML For Beginners curriculum from Microsoft, which can be found at https://aka.ms/ml-beginners. 📇 Connect with Bea: Blog: https://bea.stollnitz.com/blog/ LinkedIn: https://www.linkedin.com/in/beatrizstollnitz/ Twitter: https://twitter.com/beastollnitz