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]]>We are indeed all familiar with a line of best fit, and I am sure that many of us remember doing some by hand (you know who you are) and who hasn’t played with Excel’s capabilities? In a nutshell, a linear regression is a model that relates a variable to one or more explanatory (or independent) variables . The parameters that define the model are estimated from the available data and there are a number of assumptions about the explanatory variables and you can find more information in my Data Science and Analytics with Python book. We can think of the goal of a linear regression model to draw a line though the data as exemplified in the plot below:

Let us take the case of 2 independent variables and . The linear regression model to predict our target variable is given by:

,

where and $\latex \beta_i$ are the parameters to be estimated to help us generate predictions. With the aid of techniques such as least squares can estimate the parameters and $\latex \beta_2$ by minimising the sum of the squares of the residuals, i,.e the difference between an observed value, and the fitted value provided by a model. Once we have determined the parameters, we are able to score new (unseen) data for and to predict the value of .

In the next post we will show how we can do this for the Boston House Prices dataset using a couple of variables such as number of bedrooms in the property and a crime index for the area. Remember that the aim will be to show how to build the model to be used with CoreML and not a perfect model for the prediction.

Keep in touch.

-j

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