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In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.

Keep scrolling. Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.

First, some intuition

Let’s say you had to determine whether a home is in San Francisco or in New York. In machine learning terms, categorizing data points is a classification task.

Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.

Based on the home-elevation data to the right, you could argue that a home above 73 meters should be classified as one in San Francisco.

Adding nuance

Adding another dimension allows for more nuance. For example, New York apartments can be extremely expensive per square foot.

So visualizing elevation and price per square foot in a scatterplot helps us distinguish lower-elevation homes.

The data suggests that, among homes at or below 73 meters, those that cost more than $19,116.7 per square meter are in New York City.

Dimensions in a data set are called features, predictors, or variables.

Drawing boundaries

You can visualize your elevation (>73 m) and price per square foot (>$19,116.7) observations as the boundaries of regions in your scatterplot. Homes plotted in the green and blue regions would be in San Francisco and New York, respectively.

Identifying boundaries in data using math is the essence of statistical learning.

Of course, you’ll need additional information to distinguish homes with lower elevations and lower per-square-foot prices.