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Update data_standardization_and_pipelines.rst missing import and code… #10

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21 changes: 11 additions & 10 deletions docs/unit02/data_standardization_and_pipelines.rst
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Expand Up @@ -278,19 +278,19 @@ KNN `lecture notes <knn.html#k-nn-in-sklearn>`_.

.. code-block:: python3

data = pd.read_csv("../Diabetes-Pima/diabetes.csv")
>>>cdata = pd.read_csv("../Diabetes-Pima/diabetes.csv")
# Glucose, BMI, Insulin, Skin Thickness, Blood Pressure contains values which are 0
data.loc[data.Glucose == 0, 'Glucose'] = data.Glucose.median()
data.loc[data.BMI == 0, 'BMI'] = data.BMI.median()
data.loc[data.Insulin == 0, 'Insulin'] = data.Insulin.median()
data.loc[data.SkinThickness == 0, 'SkinThickness'] = data.SkinThickness.median()
data.loc[data.BloodPressure == 0, 'BloodPressure'] = data.BloodPressure.median()
>>> data.loc[data.Glucose == 0, 'Glucose'] = data.Glucose.median()
>>> data.loc[data.BMI == 0, 'BMI'] = data.BMI.median()
>>> data.loc[data.Insulin == 0, 'Insulin'] = data.Insulin.median()
>>> data.loc[data.SkinThickness == 0, 'SkinThickness'] = data.SkinThickness.median()
>>> data.loc[data.BloodPressure == 0, 'BloodPressure'] = data.BloodPressure.median()

# x are the dependent variables and y is the target variable
X = data.drop('Outcome',axis=1)
y = data['Outcome']
>>> X = data.drop('Outcome',axis=1)
>>> y = data['Outcome']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=1)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=1)

Recall from the notes that we found the optimal ``n_neighbors`` to be 13 using
GridSearchCV in our previous lecture. We'll hard code the 13 value for now, but
Expand All @@ -305,7 +305,8 @@ Here we create a pipeline with two steps: the ``StandardScaler`` and the
``KNeighborsClassifier``:

.. code-block:: python3

>>> from sklearn.pipeline import make_pipeline, Pipeline
>>> from sklearn.neighbors import KNeighborsClassifier
>>> pipe_line = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=13))

With the ``pipe_line`` object created, we now call ``fit()`` to execute each transformation
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