Summary

In this chapter, we have discussed how starting from the problem itself is much more valuable than beginning from a technique to use. Depending on what we need to achieve, we can look at different model approaches that will help us solve the problem we need to.

We learned that classification problems are useful when we want to put elements into categories, and some approaches such as linear regression and random forest allow you create models that achieve this. We also saw how scikit-learn lets you get to a solution with very few lines of code.

We also looked at regression for predicting values, clustering to group entities into similar buckets, and anomaly detection to find elements that don't belong with others. Similar to classification, we saw how with scikit-learn, you can get going quickly. Matplotlib also comes in handy to plot out the problem in order to give you a visual representation of what the predictions look like.

All of the models built in this...