Logistic Regression in Python | Databytes | Machine Learning in Python

DataCamp · Beginner ·📐 ML Fundamentals ·3y ago
This tutorial will walk you through the process of performing a logistic regression in Python for supervised machine learning. The topics covered in this video will be: 00:00 - 00:57 Logistic regression theory 00:58 - 03:50 Exploring the case study 03:51 - 04:26 Handling categorical variables 04:27 - 05:25 Splitting response and explanatory columns 05:26 - 06:14 Creating training and testing sets 06:15 - 07:28 Fitting the model to the training set 07:29 - 08:28 Making predictions using the model 08:29 - 14:00 Checking model fit [Try it yourself!] Pre-prepared workspace: https://bit.ly/39wpZgu [More about Python] Python is the most popular programming language today and is widely used across verticals from software and web development, game development, data science, machine learning, and more. Learning Python is imperative for aspiring data scientists, data analysts, data engineers, and machine learning scientists. To learn more about Python, check out the following resources: Subscribe to our Youtube Channel - https://www.youtube.com/c/Datacamp Check out our Introduction to Python course - https://www.datacamp.com/courses/intro-to-python-for-data-science R vs Python — which tool is best for you? - https://www.datacamp.com/community/tutorials/r-or-python-for-data-analysis Read our cheat sheets! - https://www.datacamp.com/cheat-sheet Instagram: https://www.instagram.com/datacamp Twitter: https://twitter.com/DataCamp Facebook: https://www.facebook.com/datacampinc/ Youtube: https://www.youtube.com/channel/UC79G... LinkedIn: https://www.linkedin.com/school/datac... Website: https://www.datacamp.com/ Interesting Reads: Data Science in Banking: Fraud Detection | DataCamp https://www.datacamp.com/blog/data-science-in-banking Data Scientist vs Data Engineer | DataCamp https://www.datacamp.com/blog/data-scientist-vs-data-engineer 12 Best Practices to Grow in Your Data Career | DataCamp https://www.datacamp.com/blog/12-best-practices-to-gr
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Related AI Lessons

Chapters (8)

00:57 Logistic regression theory
0:58 03:50 Exploring the case study
3:51 04:26 Handling categorical variables
4:27 05:25 Splitting response and explanatory columns
5:26 06:14 Creating training and testing sets
6:15 07:28 Fitting the model to the training set
7:29 08:28 Making predictions using the model
8:29 14:00 Checking model fit
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