Are you looking to accurately predict the next values based on available data? Well, look no further! By defining a model and training it on the available data, you can make precise predictions. But how do you know that a Regression model is the right choice for your needs? In scenarios where the output values are continuous numerical values ranging from almost 0 to infinity, Regression is the go-to model.
Using your human analytical abilities, you might notice trends in the data, like how price correlates with size. But can you create mathematical models that predict values with such accuracy? Understanding some basic terminologies and notations is crucial before diving into creating these models.
Just human analysis won’t cut it. The key lies in creating mathematical models that predict values accurately. But before we get there, let’s delve into some terminologies and notations you need to know.
Before jumping into modeling, it’s important to understand key terminologies. The data you use for training is called the Training Set, with ‘x’ representing the input and ‘y’ representing the output. When combined as a single training set, ‘(x,y) => (2104,460)’ plays a crucial role in building accurate models.