Implementation of Linear Regression
Explore how to implement linear regression models by preparing data, handling missing values, and calculating regression parameters. Learn to apply these models to predict outcomes such as housing prices using Python and real-world datasets.
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Quick overview of linear regression
Linear regression, as you may know, plots a straight line or plane called the hyperplane that predicts the target value of data inputs by determining the dependence between the dependent ...
The goal of the hyperplane is to bisect the known data points with minimal distance between itself and each data point. This means that if you were to draw a perpendicular line (90-degree angle) from the hyperplane to every data point on the plot, the distance of each data point would be the smallest possible distance of any potential hyperplane.
In preparation for building a linear regression model, you first need to remove or fill missing values and confirm ...