Summary
In this chapter, we learned the difference between supervised and unsupervised learning. We discussed the data classification problem and how to solve it. We understood how to preprocess data using various methods. We also learned about label encoding and how to build a label encoder. We discussed logistic regression and built a logistic regression classifier. We understood what a Naïve Bayes classifier is and learned how to build one. We also learned how to build a confusion matrix.
We discussed Support Vector Machines and understood how to build a classifier based on that. We learned about regression and understood how to use linear and polynomial regression for single-and multivariable data. We then used a Support Vector Regressor to estimate housing prices using input attributes.
In the next chapter, we will learn about predictive analytics and how to build a predictive engine using ensemble learning.