Machine Learning for Beginners - Logistic Regression for classification of data [Part 16] | Machine Learning for Beginners

In this video, Bea Stollnitz, a Principal Cloud Advocate at Microsoft, guides you through training a logistic regression model using the pumpkin data we cleaned and transformed in the previous video. What you'll learn: ✅ How to divide data into input features and labels ✅ How to create a logistic regression model and train it using our 🎃 data ✅ How to analyze the predictions using accuracy, precision, recall and F1 score Join Bea as she unravels the fascinating world of logistic regression, and learn how it can be utilized in classification problems. This video is perfect for those who want to expand their understanding of regression techniques and enhance their machine learning skill set. Make sure to subscribe and hit the notification bell 🔔 so you won't miss our next video, where you'll learn one other method that helps you analyze the quality of your model: ROC curves. See you there! 📙 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/4-Logistic #Python #ScikitLearn #pandas #LogisticRegression #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 0:00 - Intro 0:16 - The notebook we are using - https://aka.ms/ml-beginners 0:43 - Divide the data into input features and label 0:57 - Train/test splot 1:08 - Create a logistic regression class using SciKit Learn 1:30 - Analyze the results of the regression 1:45 - What is a confusion matrix? 2:35 - Calculate accuracy 2:52 - Precision, recall, and F1 score 3:55 - Macro average and weighted average