Imagine being able to handle data where the response variable is either binary, count, or approximately normal, all under one single framework. Well, you don't have to imagine. Enter the Generalized Linear Models in Python course! In this course you will extend your regression toolbox with the logistic and Poisson models, by learning how to fit, understand, assess model performance and finally use the model to make predictions on new data. You will practice using data from real world studies such the largest population poisoning in world's history, nesting of horseshoe crabs and counting the bike crossings on the bridges in New York City.
Review linear models and learn how GLMs are an extension of the linear model given different types of response variables. You will also learn the building blocks of GLMs and the technical process of fitting a GLM in Python.
This chapter focuses on logistic regression. You'll learn about the structure of binary data, the logit link function, model fitting, as well as how to interpret model coefficients, model inference, and how to assess model performance.
Here you'll learn about Poisson regression, including the discussion on count data, Poisson distribution and the interpretation of the model fit. You'll also learn how to overcome problems with overdispersion. Finally, you'll get hands-on experience with the process of model visualization.
In this final chapter you'll learn how to increase the complexity of your model by adding more than one explanatory variable. You'll practice with the problem of multicollinearity, and with treating categorical and interaction terms in your model.
DatasetsWell switch due to arsenic poisoningNesting of the female horseshoe crabCredit defaultLevel of salary and years of work experienceMedical costs per person given age and BMIBike crossings in New York City
PrerequisitesIntroduction to Linear Modeling in Python
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