We can load the data used in this chapter with the following function. It's very similar to the function we used in chapter 2, however it's adapted for this dataset.
from sklearn.preprocessing import StandardScalerdef load_data(): """Loads train, val, and test datasets from disk""" train = pd.read_csv(TRAIN_DATA) val = pd.read_csv(VAL_DATA) test = pd.read_csv(TEST_DATA) # we will use a dict to keep all this data tidy. data = dict() data["train_y"] = train.pop('y') data["val_y"] = val.pop('y') data["test_y"] = test.pop('y') # we will use sklearn's StandardScaler to scale our data to 0 mean, unit variance. scaler = StandardScaler() train = scaler.fit_transform(train) val = scaler.transform(val) test = scaler.transform(test) data[ ...
