I've basically built a complete multivariate linear regression model from the ground up using just NumPy. Instead of using a library like scikit-learn, you wrote the core functions yourself: one to handle the prediction math, another to perform gradient descent (which is how the model learns by slowly reducing its errors), and a function to normalize the data. I am using this custom-built model on the 'Red Wine Quality' dataset. The goal is to train it to predict the 'total sulfur dioxide' content based on the wine's other chemical properties. The script trains the model, tests it on unseen data, and then evaluates how well it did by calculating the error and plotting the results.