Now that you have the instruments to interpret the results of data mining models, it is time to move on to executing the data modeling strategy you defined with Andy. Here, you will look at classification models, first of all understanding why they were developed and in what kinds of problems they can be useful.

You will then look at three of the most common models employed within this field, which are logistic regression, support vector machines, and random forest, carefully evaluating what the assumptions are to be satisfied in order for the model to be useful.

One note of warning before leaving you again with Andy—some of the models we are going to see here as classification models are actually sometimes employed, with slight modifications, as regression models. You should therefore not be too rigid in classifying those models into your memory. The same holds for these models being supervised, since unsupervised versions of the same models are also available. For instance, we will see here support vector machines as a supervised method for classification, but you will often find that they are employed as an unsupervised technique.

Rather than scaring or confusing you, this should just stimulate you in developing critical thinking and carefully evaluating possible alternatives when dealing with your real data mining problems. 

But, let Andy speak, and I hope you will have better luck with classification models than you had with your linear regressions.