Scatterplots are an effective and commonly used technique to show the relationship between two variables. However, as the number of data points increases, the chart suffers from “over-plotting” which obscures data points and makes the underlying distribution of the data difficult to discern. Reducing the opacity of the data points is an effective way to address over-plotting, however, setting the individual point opacity is a manual task performed by the chart designer. We present a user-driven model of opacity scaling for scatter plots. We built our model based on crowd-sourced responses to opacity scaling tasks using several synthetic data distributions, and then test our model on a collection of real-world data sets.