Regression with gradient descent

Gradient descent can be a good alternative to ordinary least squares for optimizing the loss function of a linear model. This is particularly true when working with very large datasets. In this section, we will use gradient descent with the land temperatures dataset, mainly to demonstrate how to use it and to give us another opportunity to explore exhaustive grid searches. Let’s get started:

1. First, we will load the same libraries we have been working with so far, plus the stochastic gradient descent regressor from scikit-learn:

   import pandas as pd
   import numpy as np
   from sklearn.model_selection import train_test_split
   from sklearn.preprocessing import StandardScaler
   from sklearn.linear_model import SGDRegressor
   from sklearn.compose import TransformedTargetRegressor
   from sklearn.pipeline import make_pipeline
   from sklearn.impute import KNNImputer
   from sklearn.model_selection import GridSearchCV
   import os
   import sys
   sys.path.append(os.getcwd...