We already know about the Linear Regression, which helps us answer questions like "How much will a house with these characteristics cost?". Or the the Logistic Regression, which is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick.
But, what happens when the questions are "How many costumers will come today?", "How many people are in line at the grocery store?", "How many...?", and one way to answer these questions is using the Poisson Regression Model.
Poisson Regression is used to model count data. For this, we assume the response variable Y has a Poisson Distribution, and assumes the logarithm of its expected value can be modeled by a linear combinations of unknown parameters.
You'll find in this notebook a simple implementation for the Poisson Regression, along with a little example with generated data ✨
Curious about the Poisson Regression? I got you! You can fin the full explanation on my blog
By the way, thanks for passing by ✨ 💕