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Let's start by loading and preparing the California housing dataset.
We would:
first load the data
then split it into a training set, a validation set, and a test set
finally, we scale it.
Note that this dataset contains only numerical features and there are no missing values.
Note:
For a feature scaler like StandardScaler
, fit
computes the mean and std(standard deviation) to be used for later scaling (just a computation) based on the given data, nothing is given to you. transform
uses a previously computed mean and std to autoscale the data (subtract mean from all values and then divide it by std). fit_transform
does both at the same time.
So, we would be applying the scaler.fit_transform
on the train data, and just apply scaler.transform
on the validation and test data.
Import fetch_california_housing
from sklearn.datasets
.
from << your code comes here >> import << your code comes here >>
Use fetch_california_housing()
we imported from sklearn.datasets
to load the data.
housing = << your code comes here >>()
Import train_test_split
from sklearn.model_selection
.
from << your code comes here >> import << your code comes here >>
Split the data into a training set, a validation set, and a test set using train_test_split
.
X_train_full, X_test, y_train_full, y_test = << your code comes here >>( housing.data,
housing.target.reshape(-1, 1), random_state=42)
X_train, X_valid, y_train, y_valid = << your code comes here >>( X_train_full,
y_train_full, random_state=42)
Import StandardScaler
from sklearn.preprocessing
.
from << your code comes here >> import << your code comes here >>
Use StandardScaler
to get scaler
.
scaler = << your code comes here >>()
Scale all the train, validation, and test features.
Use scaler.fit_transform
on X_train
and store it in X_train_scaled
.
X_train_scaled = << your code comes here >>(X_train)
Use scaler.transform
on X_valid
and store it in X_valid_scaled
.
X_valid_scaled = << your code comes here >>(X_valid)
Use scaler.transform
on X_test
and store it in X_test_scaled
.
X_test_scaled = << your code comes here >>(X_test)
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