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Logistic regression is a simple, yet powerful classification model. In this tutorial, learn how to build a predictive classifier that classifies the age of a vehicle. Then use `ggplot` to tell the story!

Here are the links to get set up. ????

## The Story

In this analysis we learn that newer vehicles are MORE EFFICIENT, and we’ll make a data visualization that tells the story.

## How did we make this plot?

1. Our logistic regression classifier modeled the data
2. We used `VIP` to find the most important features
3. We visualized with ggplot ????

## Making a Logistic Regression Classifier

Logistic regression is a must-know tool in your data science arsenal.

• Logistic Regression is easy to explain
• The classifier has no tuning parameters (no knobs that need adjusted)

Simply split our dataset, train on the training set, evaluate on the testing set.

Folks, it’s that simple. ????

## Evaluating Our Classification Model

Question: How do we know our if our model is good?
Answer: Area Under the Curve (AUC)!

• Simple measure.
• We want greater than 0.5.
• Closer to 1.0, the better our model is.
• Bonus: ROC Plot – A way to visualize the AUC.

## Telling the Story

What can we do with a Logistic Regression Classifier? Let’s develop a story to communicate our insight!

1. First, find the most important features (predictors) using `vip()`.

2. Next, use `ggplot()` to make a visualization that focuses on the top features:

• HWY: The highway fuel economy (miles per gallon)
• CLASS: The Vehicle Class (e.g. pickup, subcompact, SUV)

### What did we learn using Logistic Regression?

It’s clear now:

• Vehicles have become more efficient over time.
• Highway fuel economy has gone up for every single class of vehicle.

Your story-telling skills are amazing. Santa approves. ????

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