update links (#618)

* update links

* fix broken link

* fix duplicate reference

Author: solegalli

LICENSE.md

@@ -1,6 +1,6 @@
 BSD 3-Clause License
 
-Copyright (c) 2018-2022 The Feature-engine developers.
+Copyright (c) 2018-2023 The Feature-engine developers.
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without

README.md

@@ -30,11 +30,11 @@ transforming parameters from the data and then transform it.
 
 ## Feature-engine features in the following resources
 
-* [Feature Engineering for Machine Learning, Online Course](https://courses.trainindata.com/p/feature-engineering-for-machine-learning)
+* [Feature Engineering for Machine Learning, Online Course](https://www.trainindata.com/p/feature-engineering-for-machine-learning)
 
-* [Feature Selection for Machine Learning, Online Course](https://courses.trainindata.com/p/feature-selection-for-machine-learning)
+* [Feature Selection for Machine Learning, Online Course](https://www.trainindata.com/p/feature-selection-for-machine-learning)
 
-* [Feature Engineering for Time Series Forecasting, Online Course](https://www.courses.trainindata.com/p/feature-engineering-for-forecasting)
+* [Feature Engineering for Time Series Forecasting, Online Course](https://www.trainindata.com/p/feature-engineering-for-forecasting)
 
 * [Python Feature Engineering Cookbook](https://packt.link/0ewSo)
 
@@ -50,7 +50,7 @@ transforming parameters from the data and then transform it.
 
 ## Documentation
 
-* [Documentation](http://feature-engine.readthedocs.io)
+* [Documentation](https://feature-engine.trainindata.com)
 
 
 ## Current Feature-engine's transformers include functionality for:
@@ -196,11 +196,11 @@ Name: var_A, dtype: int64
 ```
 
 Find more examples in our [Jupyter Notebook Gallery](https://nbviewer.org/github/feature-engine/feature-engine-examples/tree/main/) 
-or in the [documentation](http://feature-engine.readthedocs.io).
+or in the [documentation](https://feature-engine.trainindata.com).
 
 ## Contribute
 
-Details about how to contribute can be found in the [Contribute Page](https://feature-engine.readthedocs.io/en/latest/contribute/index.html)
+Details about how to contribute can be found in the [Contribute Page](https://feature-engine.trainindata.com/en/latest/contribute/index.html)
 
 Briefly:
 

docs/index.rst

@@ -93,9 +93,9 @@ If you’re using Anaconda, you can install the
 Feature-engine features in the following resources
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-- `Feature Engineering for Machine Learning <https://courses.trainindata.com/p/feature-engineering-for-machine-learning>`_, Online Course.
-- `Feature Selection for Machine Learning <https://courses.trainindata.com/p/feature-selection-for-machine-learning>`_, Online Course.
-- `Feature Engineering for Time Series Forecasting <https://www.courses.trainindata.com/p/feature-engineering-for-forecasting>`_, Online Course.
+- `Feature Engineering for Machine Learning <https://www.trainindata.com/p/feature-engineering-for-machine-learning>`_, Online Course.
+- `Feature Selection for Machine Learning <https://www.trainindata.com/p/feature-selection-for-machine-learning>`_, Online Course.
+- `Feature Engineering for Time Series Forecasting <https://www.www.trainindata.com/p/feature-engineering-for-forecasting>`_, Online Course.
 - `Python Feature Engineering Cookbook <https://packt.link/0ewSo>`_, book.
 - `Feature Selection in Machine Learning with Python <https://leanpub.com/feature-selection-in-machine-learning>`_, book.
 
@@ -211,8 +211,8 @@ Can't get something to work? Here are places where you can find help.
 
 1. The :ref:`**User Guide** <user_guide>` in the docs.
 2. `Stack Overflow <https://stackoverflow.com/search?q=feature_engine>`_. If you ask a question, please mention "feature_engine" in it.
-3. If you are enrolled in the `Feature Engineering for Machine Learning course <https://courses.trainindata.com/p/feature-engineering-for-machine-learning>`_ , post a question in a relevant section.
-4. If you are enrolled in the `Feature Selection for Machine Learning course <https://courses.trainindata.com/p/feature-selection-for-machine-learning>`_ , post a question in a relevant section.
+3. If you are enrolled in the `Feature Engineering for Machine Learning course <https://www.trainindata.com/p/feature-engineering-for-machine-learning>`_ , post a question in a relevant section.
+4. If you are enrolled in the `Feature Selection for Machine Learning course <https://www.trainindata.com/p/feature-selection-for-machine-learning>`_ , post a question in a relevant section.
 5. Join our `gitter community <https://gitter.im/feature_engine/community>`_. You an ask questions here as well.
 6. Ask a question in the repo by filing an `issue <https://github.com/feature-engine/feature_engine/issues/>`_ (check before if there is already a similar issue created :) ).
 

docs/resources/blogs.rst

@@ -15,7 +15,7 @@ Feature engineering
 - `Feature-engine: A new open-source Python package for feature engineering <https://trainindata.medium.com/feature-engine-a-new-open-source-python-package-for-feature-engineering-29a0ab88ea7c/>`_.
 - `Practical Code Implementations of Feature Engineering for Machine Learning with Python <https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd>`_.
 - `Streamlining Feature Engineering Pipelines with Feature-engine <https://towardsdatascience.com/streamlining-feature-engineering-pipelines-with-feature-engine-e781d551f470?gi=e0fa6e5c0c1a/>`_.
-- `Feature Engineering for Machine Learning: A comprehensive Overview <https://trainindata.medium.com/feature-engineering-for-machine-learning-a-comprehensive-overview-a7ad04c896f8>`_.
+- `Feature Engineering for Machine Learning: A comprehensive Overview <https://www.blog.trainindata.com/feature-engineering-for-machine-learning/>`_.
 - `Variance Stabilizing Transformations <https://www.blog.trainindata.com/variance-stabilizing-transformations-in-machine-learning/>`_.
 
 Feature selection
@@ -24,7 +24,7 @@ Feature selection
 - `Feature selection in machine learning with Python <https://www.blog.trainindata.com/feature-selection-machine-learning-with-python/>`_.
 - `Recursive feature elimination with Python <https://www.blog.trainindata.com/recursive-feature-elimination-with-python/>`_.
 - `Population Stability Index and feature selection in Python <https://www.blog.trainindata.com/population-stability-index-and-feature-selection-python/>`_
-- `Feature Selection for Machine Learning: A comprehensive Overview <https://trainindata.medium.com/feature-selection-for-machine-learning-a-comprehensive-overview-bd571db5dd2d>`_.
+- `Feature Selection for Machine Learning: A comprehensive Overview <https://www.blog.trainindata.com/feature-selection-for-machine-learning/>`_.
 
 
 Videos
@@ -49,7 +49,6 @@ Empezamos una nueva website con blogs en español. Chequeala:
 
 Ademas, te pueden interesar:
 
-- `Ingeniería de variables para machine learning <https://www.udemy.com/course/ingenieria-de-variables-para-machine-learning/?referralCode=CE398C784F17BD87482C>`_, Curso Online.
 - `Ingeniería de variables, MachinLenin <https://www.youtube.com/watch?v=NhCxOOoFXds>`_, charla con video online.
 
 More resources will be added as they appear online. If you know of a good resource, let us know.

docs/resources/courses.rst

@@ -8,23 +8,23 @@ selection in general in the following online courses:
    :width: 300
    :figclass: align-center
    :align: left
-   :target: https://courses.trainindata.com/p/feature-engineering-for-machine-learning
+   :target: https://www.trainindata.com/p/feature-engineering-for-machine-learning
 
    Feature Engineering for Machine Learning
 
 .. figure::  ../images/fsml.png
    :width: 300
    :figclass: align-center
    :align: right
-   :target: https://courses.trainindata.com/p/feature-selection-for-machine-learning
+   :target: https://www.trainindata.com/p/feature-selection-for-machine-learning
 
    Feature Selection for Machine Learning
 
 .. figure::  ../images/fetsf.png
    :width: 300
    :figclass: align-center
    :align: center
-   :target: https://www.courses.trainindata.com/p/feature-engineering-for-forecasting
+   :target: https://www.trainindata.com/p/feature-engineering-for-forecasting
 
    Feature Engineering for Time Series Forecasting
 

docs/user_guide/imputation/RandomSampleImputer.rst

@@ -153,4 +153,4 @@ You will also find a lot of information on why the seed matters in this notebook
 And finally, there is also a lot of information about this and other imputation techniques
 in this online course:
 
-- `Feature Engineering for Machine Learning <https://www.udemy.com/feature-engineering-for-machine-learning/>`_
+- `Feature Engineering for Machine Learning course <https://www.trainindata.com/p/feature-engineering-for-machine-learning>`_

docs/user_guide/selection/RecursiveFeatureElimination.rst

@@ -145,3 +145,12 @@ If we now print the transformed data, we see that the features above were remove
     2  0.050680 -0.005671 -0.034194  0.044451  0.002864 -0.045599
     3 -0.044642 -0.036656  0.024991 -0.011595  0.022692  0.012191
     4 -0.044642  0.021872  0.015596 -0.036385 -0.031991  0.003935
+
+
+
+More details
+^^^^^^^^^^^^
+
+More details on recursive feature elimination in this article:
+
+- `Recursive feature elimination with Python <https://www.blog.trainindata.com/recursive-feature-elimination-with-python/>`_

docs/user_guide/selection/index.rst

@@ -148,6 +148,6 @@ Additional Resources
 
 More details about feature selection can be found in the following resources:
 
-- `Feature Selection Online Course <https://courses.trainindata.com/p/feature-selection-for-machine-learning>`_
+- `Feature Selection Online Course <https://www.trainindata.com/p/feature-selection-for-machine-learning>`_
 - `Feature Selection book <https://leanpub.com/feature-selection-in-machine-learning/>`_
 - `Train in data's blog <https://www.blog.trainindata.com/>`_

docs/user_guide/timeseries/forecasting/ExpandingWindowFeatures.rst

@@ -321,7 +321,7 @@ You can find examples of window features and its considerations in
 `Train in Data’s github repository <https://github.com/trainindata/feature-engineering-for-time-series-forecasting/tree/main/08-Window-Features>`_.
 
 For tutorials on how to create window features for forecasting, check the course
-`Feature Engineering for Time Series Forecasting <https://www.courses.trainindata.com/p/feature-engineering-for-forecasting>`_.
+`Feature Engineering for Time Series Forecasting <https://www.trainindata.com/p/feature-engineering-for-forecasting>`_.
 
 Check out the additional transformers to create rolling window features
 (:class:`WindowFeatures`) or lag features, by lagging past values of the time

docs/user_guide/timeseries/forecasting/LagFeatures.rst

@@ -397,7 +397,7 @@ You can find examples of autocorrelation, partial autocorrelation and cross-corr
 plots in `Train in Data’s github repository <https://github.com/trainindata/feature-engineering-for-time-series-forecasting/tree/main/07-Lag-Features>`_.
 
 For tutorials on how to create lag features for forecasting, check the course
-`Feature Engineering for Time Series Forecasting <https://www.courses.trainindata.com/p/feature-engineering-for-forecasting>`_.
+`Feature Engineering for Time Series Forecasting <https://www.trainindata.com/p/feature-engineering-for-forecasting>`_.
 
 Lags from the target vs lags from predictor variables
 -----------------------------------------------------