Solving Real-World Data Science Problems with Python! (Predicting Healthcare Insurance Costs)

Try out the Datacamp platform - Assess your skills, learn Python, SQL, R and more... and get certified as a data professional! (Get 25% off annual subscription with my link) https://datacamp.pxf.io/75RPRg 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/Regression-Example 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 ------------------------- Follow me on social media! Instagram | https://www.instagram.com/keithgalli/ Twitter | https://twitter.com/keithgalli TikTok | https://tiktok.com/@keithgalli ------------------------- Learn data skills with hands-on exercises & tutorials at Datacamp! https://datacamp.pxf.io/c/3588040/1012793/13294 Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith Join the Python Army to get access to perks! YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon - https://www.patreon.com/keithgalli *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.