Python Tutorial : How to fit a GLM in Python?

Want to learn more? Take the full course at https://learn.datacamp.com/courses/generalized-linear-models-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now that you understand the building blocks of GLMs it is time to learn how to fit a GLM in Python. The starting point is the statsmodels library, which is used for statistical and econometric analysis. We import the library using statsmodels dot API. From 0.5.0. version the formula-based entry is supported which we can import as follows, or we can import the glm function directly via statsmodels dot formula dot API. To fit a model we first need to describe the model using the model class glm. Then the method fit is used to fit the model. Very detailed results of the model fit can be analyzed via the summary method, and finally, we can compute predictions using the predict method. There are two ways to describe the model, using formulas or arrays. If you are familiar with R language then you will appreciate the ability to fit a GLM using the R-style formulas. The statsmodels uses the patsy package to convert formulas and data to the matrices which are then used in model fitting. Note that if you are using the array-based method the intercept is not included by default. You can add it using the add constant function. For this course, we will use the formula based method. The main arguments are formula, data, and family. The formula is at the heart of the modeling function, where the response or output is modeled as a function of the explanatory variables or the inputs. Each explanatory variable is specified and separated with a plus sign. Note that the formula needs to be enclosed in quotation marks. There are different ways we can represent explanatory variables in the model. Categorical variables are enclosed with capital C, removing the intercept is done with minus one, the interaction terms are written in two ways depending on the ne