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LICENSE

-Copyright 2019 Moez Ali
+Copyright 2019-2020, Moez Ali <moez.ali@queensu.ca>
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
 
 The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-© 2019 GitHub, Inc.
+© 2020 GitHub, Inc.
\ No newline at end of file

README.md

@@ -2,10 +2,10 @@
 PyCaret is end-to-end open source machine learning library for python programming language. Its primary objective is to reduce the cycle time of hypothesis to insights by providing an easy to use high level unified API. PyCaret's vision is to become defacto standard for teaching machine learning and data science. Our strength is in our easy to use unified interface for both supervised and unsupervised learning. It saves time and effort that citizen data scientists, students and researchers spent on coding or learning to code using different interfaces, so that now they can focus on business problem.
 
 ## Current Release
-The current release is beta 0.0.20 (as of 20/01/2020). A full release is targetted in the first week of February 2020.
+The current release is beta 0.0.21 (as of 23/01/2020). A full release is targetted in the first week of February 2020.
 
 ## Features Currently Available
-As per beta 0.0.20 following modules are generally available:
+As per beta 0.0.21 following modules are generally available:
 * pycaret.datasets <br/>
 * pycaret.classification (binary and multiclass) <br/>
 * pycaret.regression <br/>
@@ -13,6 +13,7 @@ As per beta 0.0.20 following modules are generally available:
 * pycaret.arules <br/>
 * pycaret.anamoly <br/>
 * pycaret.clustering <br/>
+* pycaret.preprocess <br/>
 
 ## Future Release
 Full public release is targetted to be released in first week of Feb 2020.
@@ -30,7 +31,7 @@ pip install pycaret
 ```
 
 ## Quick Start
-As of beta 0.0.20 classification, regression, nlp, arules, anomaly and clustering modules are available.
+As of beta 0.0.21 classification, regression, nlp, arules, anomaly and clustering modules are available.
 
 ### Classification / Regression
 
@@ -195,11 +196,11 @@ Contributions are most welcome. To make contribution please reach out moez.ali@q
 
 ## License
 
-Copyright 2020 PyCaret / Copyright 2020 Moez Ali
+Copyright 2019-2020 Moez Ali <moez.ali@queensu.ca>
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
 
 The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-© 2019 GitHub, Inc.
+© 2020 GitHub, Inc.
\ No newline at end of file

setup.py

+# Copyright (C) 2019-2020 Moez Ali <moez.ali@queensu.ca>
+# License: MIT, moez.ali@queensu.ca
+
+"""
+  
+PyCaret - An end-to-end open source machine learning library
+
+Module Author:
+--------------
+pycaret.anomaly, Moez Ali <moez.ali@queensu.ca>
+pycaret.classification, Moez Ali <moez.ali@queensu.ca>
+pycaret.clustering, Moez Ali <moez.ali@queensu.ca>
+pycaret.datasets, Moez Ali <moez.ali@queensu.ca>
+pycaret.nlp, Moez Ali <moez.ali@queensu.ca>
+pycaret.preprocess, Fahad Akbar <M.akbar@queensu.ca>
+pycaret.regression, Moez Ali <moez.ali@queensu.ca>
+
+All modules except pycaret.preprocess licensed under:
+Copyright (C) 2019-2020 Moez Ali <moez.ali@queensu.ca>
+
+pycaret.preprocess licensed under:
+Copyright (C) 2019-2020 Fahad Akbar <M.akbar@queensu.ca>
+
+
+"""
+
 from setuptools import setup
 
 def readme():
@@ -8,7 +34,7 @@ def readme():
 
 setup(
     name="pycaret",
-    version="0.0.20",
+    version="0.0.21",
     description="A Python package for supervised and unsupervised machine learning.",
     long_description=readme(),
     long_description_content_type="text/markdown",