recommendations you have probably learned about regression already in high school mathematics classthis was probably called ordinary least squares (olsregression then this centuries old technique is fast to run and can be effectively used for many real-world problems in this we will start by reviewing ols regression and showing you how it is available in both numpy and scikit-learn in various modern problemswe run into limitations of the classical methods and start to benefit from more advanced methodswhich we will see later in this this is particularly true when we have many featuresincluding when we have more features than examples (which is something that ordinary least squares cannot handle correctlythese techniques are much more modernwith major developments happening in the last decade they go by names such as lassoridgeor elastic nets we will go into these in detail finallywe will start looking at recommendations this is an important area in many applications as it is significant added-value to many applications this is topic that we will start exploring here and will see in more detail in the next predicting house prices with regression let us start with simple problempredicting house prices in boston we can use publicly available dataset we are given several demographic and geographical attributessuch as the crime rate or the pupil-teacher ratioand the goal is to predict the median value of house in particular area as usualwe have some training datawhere the answer is known to us