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This video teaches how to predict health insurance costs using Python and machine learning. It covers the full process from cleaning data to building and testing a regression model. Viewers will learn to use pandas for data handling, create visualizations, and apply scikit-learn for linear regression. The tutorial provides hands-on experience with real-world data analysis and predictive modeling.

Github repo: https://github.com/KeithGalli/Regress...

Datacamp Project link: https://datacamp.pxf.io/75REKg

Video timeline!
0:00 - Video overview
0:47 - What is regression?
2:29 - Getting started with the code
4:30 - Initial regression modeling strategy
6:41 - Task #1: Clean our health insurance data
25:47 - Task #2: Create scatterplots of our variables mapped to charges
31:34 - Task #3: Prepare the data for regression model fitting
41:32 - Task #4: Fit a linear regression model to our dataframe with sklearn
52:11 - Task #5: Test our model on validation data & submit project

#datascience #regression

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