add course and book links to engineering transformers

README.md

@@ -43,6 +43,17 @@ transforming parameters from the data and then transform it.
 * [Documentation](https://feature-engine.trainindata.com)
 
 
+## Pst! How did you find us?
+
+We want to share Feature-engine with more people. It'd help us loads if you tell us
+how you discovered us.
+
+Then we'd know what we are doing right and which channels to use to share the love.
+
+Please share your story by answering 1 quick question
+[at this link](https://docs.google.com/forms/d/e/1FAIpQLSfxvgnJvuvPf2XgosakhXo5VNQafqRrjNXkoW5qDWqnuxZNSQ/viewform?usp=sf_link)
+. 😃
+
 ## Current Feature-engine's transformers include functionality for:
 
 * Missing Data Imputation

docs/index.rst

@@ -41,6 +41,18 @@ therefore making it possible to save and deploy one single object (.pkl) with th
 entire machine learning pipeline. Check :ref:`**Quick Start** <quick_start>` for an
 example.
 
+Pst! How did you find us?
+-------------------------
+
+We want to share Feature-engine with more people. It'd help us loads if you tell us
+how you discovered us.
+
+Then we'd know what we are doing right and which channels to use to share the love.
+
+Please share your story by answering 1 quick question
+`at this link <https://docs.google.com/forms/d/e/1FAIpQLSfxvgnJvuvPf2XgosakhXo5VNQafqRrjNXkoW5qDWqnuxZNSQ/viewform?usp=sf_link>`_
+. 😃
+
 What is unique about Feature-engine?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/resources/blogs.rst

@@ -45,7 +45,7 @@ En Español
 
 Empezamos una nueva website con blogs en español. Chequeala:
 
-- `Entrena en Datos <http://www.entrenaendatos.com/>`_.
+- `Entrena en Datos <https://www.es.trainindata.com/>`_.
 
 Ademas, te pueden interesar:
 

docs/resources/courses.rst

@@ -23,11 +23,18 @@ selection in general in the following online courses:
 .. figure::  ../images/fetsf.png
    :width: 300
    :figclass: align-center
-   :align: center
+   :align: left
    :target: https://www.trainindata.com/p/feature-engineering-for-forecasting
 
    Feature Engineering for Time Series Forecasting
 
+.. figure::  ../images/mli_logo.png
+   :width: 300
+   :figclass: align-center
+   :align: right
+   :target: https://www.courses.trainindata.com/p/machine-learning-interpretability
+
+   Interpreting Machine Learning Models
 
 We also show how to put an end-to-end machine learning pipeline in production, using
 Feature-engine and Scikit-learn in this course:

docs/user_guide/creation/MathFeatures.rst

@@ -229,9 +229,11 @@ provided:
 More details
 ^^^^^^^^^^^^
 
-You can find creative ways to use :class:`MathFeatures()` in the
-following Jupyter notebooks.
+You can find creative ways to use :class:`MathFeatures()` in the following Jupyter notebooks.
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/creation/MathFeaturesn.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/creation/RelativeFeatures.rst

@@ -149,4 +149,7 @@ following Jupyter notebooks and Kaggle kernels.
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/creation/RelativeFeatures.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/discretisation/ArbitraryDiscretiser.rst

@@ -113,4 +113,7 @@ Check also:
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/discretisation/ArbitraryDiscretiser.ipynb>`_
 - `Jupyter notebook - Discretiser plus Mean Encoding <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/discretisation/ArbitraryDiscretiser_plus_MeanEncoder.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/discretisation/DecisionTreeDiscretiser.rst

@@ -136,4 +136,7 @@ Check also for more details on how to use this transformer:
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/discretisation/DecisionTreeDiscretiser.ipynb>`_
 - `tree_pipe in cell 21 of this Kaggle kernel <https://www.kaggle.com/solegalli/feature-engineering-and-model-stacking>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/discretisation/EqualFrequencyDiscretiser.rst

@@ -130,4 +130,7 @@ Check also for more details on how to use this transformer:
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/discretisation/EqualFrequencyDiscretiser.ipynb>`_
 - `Jupyter notebook - Discretiser plus Weight of Evidence encoding <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/discretisation/EqualFrequencyDiscretiser_plus_WoEEncoder.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/discretisation/EqualWidthDiscretiser.rst

@@ -131,4 +131,7 @@ Check also for more details on how to use this transformer:
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/discretisation/EqualWidthDiscretiser.ipynb>`_
 - `Jupyter notebook - Discretiser plus Ordinal encoding <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/discretisation/EqualWidthDiscretiser_plus_OrdinalEncoder.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/discretisation/GeometricWidthDiscretiser.rst

@@ -154,4 +154,7 @@ Check also for more details on how to use this transformer:
 - `Jupyter notebook - Geometric Discretiser <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/discretisation/GeometricWidthDiscretiser.ipynb>`_
 - `Jupyter notebook - Geometric Discretiser plus Mean encoding <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/discretisation/GeometricWidthDiscretiser_plus_MeanEncoder.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/encoding/CountFrequencyEncoder.rst

@@ -115,4 +115,7 @@ functionality and example plots with the encoded variables:
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/encoding/CountFrequencyEncoder.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/encoding/DecisionTreeEncoder.rst

@@ -113,4 +113,7 @@ functionality and example plots with the encoded variables:
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/encoding/DecisionTreeEncoder.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/encoding/MeanEncoder.rst

@@ -158,4 +158,7 @@ functionality and example plots with the encoded variables:
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/encoding/MeanEncoder.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/encoding/OneHotEncoder.rst

@@ -173,4 +173,7 @@ For more details into :class:`OneHotEncoder()`'s functionality visit:
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/encoding/OneHotEncoder.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/encoding/OrdinalEncoder.rst

@@ -128,4 +128,7 @@ functionality and example plots with the encoded variables:
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/encoding/OrdinalEncoder.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/encoding/RareLabelEncoder.rst

@@ -219,4 +219,7 @@ functionality and example plots with the encoded variables:
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/encoding/RareLabelEncoder.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/encoding/WoEEncoder.rst

@@ -167,4 +167,7 @@ functionality and example plots with the encoded variables:
 - `WoE in categorical variables <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/encoding/WoEEncoder.ipynb>`_
 - `WoE in numerical variables <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/discretisation/EqualFrequencyDiscretiser_plus_WoEEncoder.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/imputation/AddMissingIndicator.rst

@@ -103,5 +103,7 @@ how to select the variables automatically.
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/imputation/AddMissingIndicator.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/imputation/ArbitraryNumberImputer.rst

@@ -115,4 +115,7 @@ You will also see how to navigate the different attributes of the transformer.
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/imputation/ArbitraryNumberImputer.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/imputation/CategoricalImputer.rst

@@ -63,4 +63,7 @@ range proximity rule.
 
 Check also this `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/imputation/CategoricalImputer.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/imputation/DropMissingData.rst

@@ -134,4 +134,7 @@ In the following Jupyter notebook you will find more details on the functionalit
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/imputation/DropMissingData.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/imputation/EndTailImputer.rst

@@ -100,4 +100,7 @@ string.
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/imputation/EndTailImputer.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/imputation/MeanMedianImputer.rst

@@ -98,4 +98,7 @@ mean or median values of the variables.
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/imputation/MeanMedianImputer.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/outliers/ArbitraryOutlierCapper.rst

@@ -95,4 +95,7 @@ notebook:
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/outliers/ArbitraryOutlierCapper.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/outliers/OutlierTrimmer.rst

@@ -122,4 +122,7 @@ notebook:
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/outliers/OutlierTrimmer.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.

docs/user_guide/outliers/Winsorizer.rst

@@ -122,4 +122,7 @@ notebook:
 
 - `Jupyter notebook <https://nbviewer.org/github/feature-engine/feature-engine-examples/blob/main/outliers/Winsorizer.ipynb>`_
 
-All notebooks can be found in a `dedicated repository <https://github.com/feature-engine/feature-engine-examples>`_.
+For more details 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.
+- `Python Feature Engineering Cookbook <https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301>`_, book.