# Get started with Python and Scikit-Learn for Regression models
> Sketchnote on three types of Regression
> Infographic on the types of Regression covered here: linear and logistic. Follow: https://en.wikipedia.org/wiki/Linear_regression#/media/File:Linear_regression.svg and https://en.wikipedia.org/wiki/Logistic_regression#/media/File:Logistic-curve.svg with explanatory graphics
# Build a Regression Model using Scikit-Learn: Prepare and Visualize Data
> Sketchnote on data and visualization
> Sketchnote on data and visualization - show several different types of visualizations common to the topic, such as these https://seaborn.pydata.org/examples/index.html
# Build a Regression Model using Scikit-Learn: Regression Two Ways
> Sketchnote on Linear vs. Polynomial Regression
> Sketchnote on Linear vs. Polynomial Regression - what's the difference? https://en.wikipedia.org/wiki/Polynomial_regression#/media/File:Linear_regression.svg vs. https://en.wikipedia.org/wiki/Polynomial_regression#/media/File:Polyreg_scheffe.svg
@ -25,10 +23,14 @@ For our purposes, we will express this as a binary: 'Orange' or 'Not Orange'. Th
Logistic Regression differs from Linear Regression, which you learned about previously, in a few important ways.
### Binary Classification
Logistic Regression does not offer the same features as Linear Regression. The former offers a prediction about a binary category ("orange or not orange") whereas the latter is capable of predicting continual values, for example given the origin of a pumpkin and the time of month, how much its price will rise.
Logistic Regression does not offer the same features as Linear Regression. The former offers a prediction about a binary category ("orange or not orange") whereas the latter is capable of predicting continual values, for example given the origin of a pumpkin and the time of harvest, how much its price will rise.
> Infographic about binary classification using logistic regression for pumpkins ("orange or not orange") - like this, with new labels and maybe little pumpkin dots https://miro.medium.com/max/1586/1*Yiv9NLy06vzJoUhvC6uBTA.png
### Other Classifications
There are other types of Logistic Regression, including Multinomial and Ordinal. Multinomial involves having more than one categories - "Orange, White, and Striped". Ordinal involves ordered categories, such as if we wanted to order our outcomes logically, like our pumpkins that are ordered by a finite number of sizes (mini,sm,med,lg,xl,xxl).
There are other types of Logistic Regression, including Multinomial and Ordinal. Multinomial involves having more than one categories - "Orange, White, and Striped". Ordinal involves ordered categories, useful if we wanted to order our outcomes logically, like our pumpkins that are ordered by a finite number of sizes (mini,sm,med,lg,xl,xxl).
> Infographic on the difference between multinomial vs. ordinal logistic regression in the context of our pumpkin dataset: there are images here for multinomial https://www.codespeedy.com/multinomial-logistic-regression-in-python/ and for ordinal check this out: http://fa.bianp.net/blog/static/images/2013/ordinal_1.png - you can show the pumpkin sizes in a line - the smaller, the more expensive by the bushel, for example.
Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore Logistic Regression to determine a given pumpkin's likely color.
> infographic here
> infographic here (an image of logistic regression's sigmoid flow, like this: https://en.wikipedia.org/wiki/Logistic_regression#/media/File:Exam_pass_logistic_curve.jpeg)
Jen Looper
5 years ago