[DOC] update Creation user guide

docs/user_guide/creation/CyclicalFeatures.rst

@@ -34,7 +34,7 @@ Cyclical encoding
 
 The trigonometric functions sine and cosine are periodic and repeat their values every
 2 pi radians. Thus, to transform cyclical variables into (x, y) coordinates using these
-functions, first we need to normalize them to 2 pi radians.
+functions, first we need to normalise them to 2 pi radians.
 
 We achieve this by dividing the variables' values by their maximum value. Thus, the two
 new features are derived as follows:
@@ -51,9 +51,9 @@ In Python, we can encode cyclical features by using the Numpy functions `sin` an
     X[f"{variable}_sin"] = np.sin(X["variable"] * (2.0 * np.pi / X["variable"]).max())
     X[f"{variable}_cos"] = np.cos(X["variable"] * (2.0 * np.pi / X["variable"]).max())
 
-We can also use Feature-engine to automate this process.
+We can also use feature-engine to automate this process.
 
-Cyclical encoding with Feature-engine
+Cyclical encoding with feature-engine
 -------------------------------------
 
 :class:`CyclicalFeatures()` creates two new features from numerical variables to better
@@ -69,7 +69,7 @@ Finding the max_value
 ~~~~~~~~~~~~~~~~~~~~~
 
 :class:`CyclicalFeatures()` attempts to automate the process of cyclical encoding by
-automatically determining the value used to normalize the feature between
+automatically determining the value used to normalise the feature between
 0 and 2 * pi radians, which coincides with the cycle of the periodic functions sine and
 cosine.
 
@@ -86,7 +86,7 @@ Applying cyclical encoding
 --------------------------
 
 We'll start by applying cyclical encoding to a toy dataset to get familiar with how to
-use Feature-engine for cyclical encoding.
+use feature-engine for cyclical encoding.
 
 In this example, we'll encode the cyclical features **days of the week** and **months**.
 Let's create a toy dataframe with the variables "days" and "months":
@@ -131,6 +131,8 @@ The maximum values used for the transformation are stored in the attribute
 
     cyclical.max_values_
 
+Below we see the maximum values of each variable:
+
 .. code:: python
 
     {'day': 7, 'months': 12}
@@ -141,7 +143,7 @@ Let's have a look at the transformed dataframe:
 
     print(X.head())
 
-We see that the new variables were added at the right of our dataframe.
+We see that the new variables were added at the right of our dataframe:
 
 .. code:: python
 
@@ -166,7 +168,7 @@ the feature creation, we can set the parameter to `True`:
     print(X.head())
 
 The resulting dataframe contains only the cyclical encoded features; the original variables
-are removed:
+were removed:
 
 .. code:: python
 
@@ -200,7 +202,7 @@ Understanding cyclical encoding
 -------------------------------
 
 We now know how to convert cyclical variables into (x, y) coordinates of a circle by using
-the sine and cosine functions. Let’s now carry out some visualizations to better understand
+the sine and cosine functions. Let’s now carry out some visualisations to better understand
 the effect of this transformation.
 
 Let's create a toy dataframe:
@@ -266,7 +268,7 @@ These are the sine and cosine features that represent the hour:
 
 
 Let's now plot the hour variable against its sine transformation. We add perpendicular
-lines to flag the hours 0 and 22.
+lines to flag the hours 0 and 22:
 
 .. code:: python
 
@@ -369,10 +371,10 @@ functions and cyclical encoding.
 Feature-engine vs Scikit-learn
 ------------------------------
 
-Let's compare the implementations of cyclical encoding between Feature-engine and Scikit-learn.
+Let's compare the implementations of cyclical encoding between feature-engine and scikit-learn.
 We'll work with the Bike sharing demand dataset, and we'll follow the implementation of
-Cyclical encoding found in the `Time related features documentation <https://scikit-learn.org/stable/auto_examples/applications/plot_cyclical_feature_engineering.html#trigonometric-features>`_
-from Scikit-learn.
+cyclical encoding found in the `Time related features documentation <https://scikit-learn.org/stable/auto_examples/applications/plot_cyclical_feature_engineering.html#trigonometric-features>`_
+from scikit-learn.
 
 Let's load the libraries and dataset:
 
@@ -409,7 +411,7 @@ In the following output we see the bike sharing dataset:
     3     14.395      0.75        0.0     13
     4     14.395      0.75        0.0      1
 
-To apply cyclical encoding with Scikit-learn, we can use the `FunctionTransformer`:
+To apply cyclical encoding with scikit-learn, we can use the `FunctionTransformer`:
 
 .. code:: python
 
@@ -476,7 +478,7 @@ and hour:
 
     [17379 rows x 6 columns]
 
-With Feature-engine, we can do the same as follows:
+With feature-engine, we can do the same as follows:
 
 .. code:: python
 
@@ -533,10 +535,10 @@ the variable hour by 23, instead of 24, because the values of these variables va
     {'month': 12, 'weekday': 6, 'hour': 23}
 
 Practically, there isn't a big difference between the values of the dataframes returned by
-Scikit-learn and Feature-engine, and I doubt that this subtle difference will incur in a big
+scikit-learn and feature-engine, and I doubt that this subtle difference will incur in a big
 change in model performance.
 
-However, if you want to divide the varibles weekday and hour by 7 and 24 respectively, you can
+However, if you want to divide the variables weekday and hour by 7 and 24 respectively, you can
 do so like this:
 
 .. code:: python
@@ -574,39 +576,21 @@ the user, with automation, we can only go that far.
 Additional resources
 --------------------
 
-For tutorials on how to create cyclical features, check out the following courses:
-
-.. figure::  ../../images/feml.png
-   :width: 300
-   :figclass: align-center
-   :align: left
-   :target: https://www.trainindata.com/p/feature-engineering-for-machine-learning
-
-   Feature Engineering for Machine Learning
-
-.. figure::  ../../images/fetsf.png
-   :width: 300
-   :figclass: align-center
-   :align: right
-   :target: https://www.trainindata.com/p/feature-engineering-for-forecasting
-
-   Feature Engineering for Time Series Forecasting
-
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-
 For a comparison between one-hot encoding, ordinal encoding, cyclical encoding and spline
 encoding of cyclical features check out the following
 `sklearn demo <https://scikit-learn.org/stable/auto_examples/applications/plot_cyclical_feature_engineering.html>`_.
 
 Check also these Kaggle demo on the use of cyclical encoding with neural networks:
 
 - `Encoding Cyclical Features for Deep Learning <https://www.kaggle.com/code/avanwyk/encoding-cyclical-features-for-deep-learning>`_.
+
+
+For tutorials about this and other feature engineering methods check out these resources:
+
+- `Feature Engineering for Machine Learning <https://www.trainindata.com/p/feature-engineering-for-machine-learning>`_, online course.
+- `Feature Engineering for Time Series Forecasting <https://www.trainindata.com/p/feature-engineering-for-forecasting>`_, online course.
+- `Python Feature Engineering Cookbook <https://www.packtpub.com/en-us/product/python-feature-engineering-cookbook-9781835883587>`_, book.
+
+Both our book and courses are suitable for beginners and more advanced data scientists
+alike. By purchasing them you are supporting `Sole <https://linkedin.com/in/soledad-galli>`_,
+the main developer of feature-engine.