This is a popular classification algorithm where the dependent variable (outcome) is categorical. Even though it has the word regression in its name, it is a classification technique. Using this technique, we can train a model on some training data and the same model we can later use on new data to classify it into different categories. So, if you want to classify data into categories such as 1/0, Yes/No, True/False, Has Disease/No Disease, Sick/Not Sick and so on, logistic regression is a good classifier model to try in these cases. As per these examples, logistic regression is typically used for binary classification, but it can also be used for multiclass classification too.
The approach used by this algorithm is quite simple. We apply the data from the dataset onto a mathematical optimization function and this function will later make the data fall either in a 0 category or 1 category. Later on when we get a new piece of data we apply the same function to that new...
