Regression is predicting a number rather than a category, learning the relationship between inputs and a continuous value.
Classification predicts a category from a fixed list. Regression predicts a number on a continuous scale.
Draw the single best straight line through a cloud of data points — that line is your prediction machine.
The whole point of building a model is to predict values you have never seen — let's do that.
Some data curves — and a straight line through a curve is like wearing the wrong prescription glasses.
Not all numbers are created equal — some can only be whole numbers, and others can be infinitely precise.
The data type is your compass — it points you toward the right analysis technique and away from meaningless results.
With thousands of data points, scatter plots reveal patterns that are invisible in small samples.
Regression is predicting a number. The model learns how the inputs relate to a continuous value and then estimates that value for new cases.
House prices, delivery times, tomorrow's demand. The answer sits on a scale rather than in a bucket, and being close counts for something, which is not true of categories.
Think of it like this. Think of estimating someone's height from a photograph. You will not be exactly right, and being off by a centimetre is very different from being off by a metre. Regression is scored on how close you got.