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Zero-inflated Poisson regression is used to model count data that has an excess of zero counts. By this I mean that the dependent variable has large number zeros. The theory suggests that the excess zeros are generated by a 2 separate process that can be modeled independently. Thus the zip model has two parts, a Poisson count model and the logit model for predicting excess zeros.

In this section, we’ll learn how to build a regression model for count based datasets in which the dependent variable contains an excess of zero-valued data. We will also learn how to track experiments and features, along with automatic EDA, using VevestaX.

Count based regression models are used where the value of dependent variable is a whole number. Few of the use cases of this model are:

Number of doctor visits per year. In the real world scenario there are many cases that produce counts which are almost always zero. For example:

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