Model

Code Implementation here

The Google Colab contains the entire code implementation and the best practices to be followed to implement the Linear regression

For this example the data taken is the Insurance Premium data set, the data preparation involved encoding the categorical columns and scaling the data.

After the data preparation, a Linear Regression model is trained on the training data. During the training, the model learns the slope and intercept to approximately fit the training data.

The result of this training yields many different statistical metrics. The ability to understand these statistical metrics allows linear models to be Interpretable. We will go through the statistical metric in more detail in the next section.