Multiple linear regression is a technique that uses several independent variables in order to predict the outcome of a dependent variable.
- Analyze the datasets and select the best predictors.
- Implement the gradient descent algorithm.
- Test the implementation for a varying number of iteration steps, learning rate, train/test ratio to estimate their effect on the model.
- Evaluating the goodness of fit for each case and gaining a deeper grasp of the characteristics of multiple linear regression.