![]() ![]() Multiple Linear RegressionĪ natural extension of the Simple Linear Regression model is the multivariate one. X X X is the data we’re going to use to train our model, b b b controls the slope and a a a the interception point with the y y y axis. Where a and b are parameters, learned during the training of our model. It is given by: Y = b X + a Y = bX + a Y = b X + a Simple Linear Regression is a model that has a single independent variable X X X. In our case, we’re going to use features like living area (X) to predict the sale price (Y) of a house. Linear Regression models assume that there is a linear relationship (can be modeled using a straight line) between a dependent continuous variable Y Y Y and one or more explanatory (independent) variables X X X. Seems like it, we might start our price prediction model using the living area! Linear Regression Oh ok, but higher quality should equal higher price, right? Seems like newer houses are pricier, no love for the old and well made then? Let’s have a look at the year they are built:Įven though there are a lot of houses that were built recently, we have a much more widespread distribution. ![]() Most of the houses are within the 1,000 - 2,000 range, and we have some that are bigger. Let’s see how large are they (that’s what she said): Most houses are of average quality, but there are more “good” than “bad” ones. Let’s build a better understanding of our data. 1 const data = await prepareData ( ) Exploration ![]()
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