# Implementing a Binomial Logistic Regression model in Python

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So far, we have only discussed regression modelling. However, there is another type of modelling called classification modelling. The primary difference between regression models and classification models is that while regression models are used to predict a quantity, classification models are used to predict a category.

For example, in my post on simple linear regression, we tried to predict soda sales through day’s temperature. Total sales of soda (our label) is a quantitative value and hence we used a regression model. In the example today, we are going to predict whether someone will purchase soda or not by looking at day’s temperature. Here we have two categories, whether customer will purchase or not purchase soda. This makes our label (dependent variable) categorical and suitable for logistic regression.As there were different variations of linear regression model, we also have different types of logistic regression model.