Congratulations, you just built your first Linear Regression model, created a prediction with it, and displayed it in a plot!
🚀 Challenge: Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
---
## 🚀Challenge
Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
This is a more useful data visualization! It seems to indicate that the highest price for pumpkins occurs in September and October. Does that meet your expectation? Why or why not?
🚀 Challenge: Explore the different types of visualization that matplotlib offers. Which types are most appropriate for regression problems?
---
## 🚀Challenge
Explore the different types of visualization that matplotlib offers. Which types are most appropriate for regression problems?
@ -234,7 +234,10 @@ It does make sense! And, if this is a better model than the previous one, lookin
🏆 Well done! You created two Regression models in one lesson. In the final section on Regression, you will learn about Logistic Regression to determine categories.
🚀 Challenge: Test several different variables in this notebook to see how correlation corresponds to model accuracy.
---
## 🚀Challenge
Test several different variables in this notebook to see how correlation corresponds to model accuracy.
Jen Looper
4 years ago