- Task #2: Create scatterplots of our variables mapped to charges
7
- Task #3: Prepare the data for regression model fitting
8
- Task #4: Fit a linear regression model to our dataframe with sklearn
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- Task #5: Test our model on validation data & submit project
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Learn to predict health insurance costs using Python and machine learning in this comprehensive tutorial video. Explore the entire process from data cleaning to building and testing a regression model. Gain hands-on experience with real-world data analysis and predictive modeling using pandas for data handling, creating visualizations, and applying scikit-learn for linear regression. Follow along with step-by-step tasks, including cleaning health insurance data, creating scatterplots, preparing data for regression modeling, fitting a linear regression model with sklearn, and testing the model on validation data. Perfect for aspiring data scientists looking to apply their skills to practical, real-world problems.
Predicting Healthcare Insurance Costs with Python - Real-World Data Science Problem Solving