Summary

In this chapter, we learned about the basics of classification and the difference between regression problems. Classification is about predicting a response variable with limited possible values. As for any data science project, data scientists need to prepare the data before training a model. In this chapter, we learned how to standardize numerical values and replace missing values. Then, you were introduced to the famous k-nearest neighbors algorithm and discovered how it uses distance metrics to find the closest neighbors to a data point and then assigns the most frequent class among them. We also learned how to apply an SVM to a classification problem and tune some of its hyperparameters to improve the performance of the model and reduce overfitting.

In the next chapter, we will walk you through a different type of algorithm, called decision trees.