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Table Of Contents
Python in Excel for Data Analytics
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In this chapter, you got a practical introduction to predictive modeling in Python in Excel. You built a linear regression model to predict flipper length from physical measurements, learning how to read coefficients, R-squared, and p-values along the way. You then used logistic regression to classify penguins by species, seeing how the same general workflow adapts when the outcome is a category instead of a number. Finally, you used train/test splits to evaluate whether your models generalize beyond the data they were trained on.
The techniques in this chapter form the foundation of predictive modeling and machine learning. Every technique in this space: from random forests to neural networks, builds on the same core ideas: fit a model to training data, evaluate it on held-out data, and interpret the results. If you understand what this chapter covered, you have the conceptual foundation to explore more advanced techniques on your own.
In the next chapter, you will apply a related...