diff --git a/Makefile b/Makefile
index 3f55b2ca..414777ad 100644
--- a/Makefile
+++ b/Makefile
@@ -44,15 +44,15 @@ test: ### Runs all the project tests
 	$(PYTHON) -m pytest tests/
 
 package: clean ### Runs the project setup
-	echo __version__ = \"$(version)\" > src/ydata_quality/__version__.py
+	echo __version__ = \"$(version)\" > src/data_quality/__version__.py
 	echo "$(version)" > VERSION
 	$(PYTHON) setup.py sdist bdist_wheel
 
 install: ### Installs required dependencies
-	$(PIP) install dist/ydata-quality-$(version).tar.gz
+	$(PIP) install dist/data-quality-$(version).tar.gz
 
 link-local: ### Installs the lib in dev mode
-	echo __version__ = \"$(version)\" > src/ydata_quality/__version__.py
+	echo __version__ = \"$(version)\" > src/data_quality/__version__.py
 	$(PIP) install -e .
 
 upload:
diff --git a/README.md b/README.md
index 4efd6587..6310bf77 100644
--- a/README.md
+++ b/README.md
@@ -1,33 +1,30 @@
-> **`ydata-quality` is now `data-quality`.** This package has been renamed to `data-quality`. Please follow the [Migration Guide](#migration-guide) as soon as possible — the old package will no longer receive updates or bug fixes.
-
-
-[![](https://api.codacy.com/project/badge/Grade/f1662116e982402c956d2720fbd24507)](https://app.codacy.com/gh/ydataai/ydata-quality)
-![](https://img.shields.io/github/workflow/status/ydataai/ydata-quality/release)
-![](https://img.shields.io/pypi/status/ydata-quality)
-[![](https://pepy.tech/badge/ydata-quality)](https://pypi.org/project/ydata-quality/)
+[![](https://api.codacy.com/project/badge/Grade/f1662116e982402c956d2720fbd24507)](https://app.codacy.com/gh/Data-Centric-AI-Community/fg-data-quality)
+![](https://img.shields.io/github/workflow/status/Data-Centric-AI-Community/fg-data-quality/release)
+![](https://img.shields.io/pypi/status/fg-data-quality)
+[![](https://pepy.tech/badge/fg-data-quality)](https://pypi.org/project/fg-data-quality/)
 ![](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8-blue)
-[![](https://img.shields.io/pypi/v/ydata-quality)](https://pypi.org/project/ydata-quality/)
-![](https://img.shields.io/github/license/ydataai/ydata-quality)
+[![](https://img.shields.io/pypi/v/fg-data-quality)](https://pypi.org/project/fg-data-quality/)
+![](https://img.shields.io/github/license/Data-Centric-AI-Community/fg-data-quality)
 
-# YData Quality
+# Data Quality
 
-ydata_quality is an open-source python library for assessing Data Quality throughout the multiple stages of a data pipeline development. 
+data_quality is an open-source python library for assessing Data Quality throughout the multiple stages of a data pipeline development. 
 
-A holistic view of the data can only be captured through a look at data from multiple dimensions and `ydata_quality` evaluates it in a modular way wrapped into a single Data Quality engine. This repository contains the core python source scripts and walkthrough tutorials.
+A holistic view of the data can only be captured through a look at data from multiple dimensions and `data_quality` evaluates it in a modular way wrapped into a single Data Quality engine. This repository contains the core python source scripts and walkthrough tutorials.
 
 ## Quickstart
 
-The source code is currently hosted on GitHub at: https://github.com/ydataai/ydata-quality
+The source code is currently hosted on GitHub at: https://github.com/Data-Centric-AI-Community/fg-data-quality
 
-Binary installers for the latest released version are available at the [Python Package Index (PyPI).](https://pypi.org/project/ydata-quality/)
+Binary installers for the latest released version are available at the [Python Package Index (PyPI).](https://pypi.org/project/fg-data-quality/)
 ```
-pip install ydata-quality
+pip install fg-data-quality
 ```
 
 ### Comprehensive quality check in few lines of code
 
 ```python
-from ydata_quality import DataQuality
+from data_quality import DataQuality
 import pandas as pd
 
 #Load in the data
@@ -72,7 +69,7 @@ pip uninstall ydata-quality
 ### 2. Install the new package
  
 ```bash
-pip install data-quality
+pip install fg-data-quality
 ```
  
 ### 3. Update your imports
@@ -97,19 +94,19 @@ grep -r "ydata_quality" . --include="*.py"
 
 ## Examples
 
-Here you can find walkthrough tutorials and examples to familiarize with different modules of `ydata_quality`
+Here you can find walkthrough tutorials and examples to familiarize with different modules of `data_quality`
 
-- [Start Here for Quick and Overall Walkthrough](https://github.com/ydataai/ydata-quality/blob/master/tutorials/main.ipynb)
+- [Start Here for Quick and Overall Walkthrough](https://github.com/Data-Centric-AI-Community/fg-data-quality/blob/master/tutorials/main.ipynb)
 
 To dive into any focussed module, and to understand how they work, here are tutorial notebooks:
-1. [Bias and Fairness](https://github.com/ydataai/ydata-quality/blob/master/tutorials/bias_fairness.ipynb)
-2.  [Data Expectations](https://github.com/ydataai/ydata-quality/blob/master/tutorials/data_expectations.ipynb)
-3.  [Data Relations](https://github.com/ydataai/ydata-quality/blob/master/tutorials/data_relations.ipynb)
-4.  [Drift Analysis](https://github.com/ydataai/ydata-quality/blob/master/tutorials/drift.ipynb)
-5.  [Duplicates](https://github.com/ydataai/ydata-quality/blob/master/tutorials/duplicates.ipynb)
-6.  Labelling: [Categoricals](https://github.com/ydataai/ydata-quality/blob/master/tutorials/labelling_categorical.ipynb) and [Numericals](https://github.com/ydataai/ydata-quality/blob/master/tutorials/labelling_numerical.ipynb)
-7.  [Missings](https://github.com/ydataai/ydata-quality/blob/master/tutorials/missings.ipynb)
-8.  [Erroneous Data](https://github.com/ydataai/ydata-quality/blob/master/tutorials/erroneous_data.ipynb)
+1. [Bias and Fairness](https://github.com/Data-Centric-AI-Community/fg-data-quality/blob/master/tutorials/bias_fairness.ipynb)
+2.  [Data Expectations](https://github.com/Data-Centric-AI-Community/fg-data-quality/blob/master/tutorials/data_expectations.ipynb)
+3.  [Data Relations](https://github.com/Data-Centric-AI-Community/fg-data-quality/blob/master/tutorials/data_relations.ipynb)
+4.  [Drift Analysis](https://github.com/Data-Centric-AI-Community/fg-data-quality/blob/master/tutorials/drift.ipynb)
+5.  [Duplicates](https://github.com/Data-Centric-AI-Community/fg-data-quality/blob/master/tutorials/duplicates.ipynb)
+6.  Labelling: [Categoricals](https://github.com/Data-Centric-AI-Community/fg-data-quality/blob/master/tutorials/labelling_categorical.ipynb) and [Numericals](https://github.com/Data-Centric-AI-Community/fg-data-quality/blob/master/tutorials/labelling_numerical.ipynb)
+7.  [Missings](https://github.com/Data-Centric-AI-Community/fg-data-quality/blob/master/tutorials/missings.ipynb)
+8.  [Erroneous Data](https://github.com/Data-Centric-AI-Community/fg-data-quality/blob/master/tutorials/erroneous_data.ipynb)
 
 ## Contributing
 We are open to collaboration! If you want to start contributing you only need to:
@@ -123,7 +120,7 @@ You can also join the discussions on our [Discord Community](https://discord.com
 For support in using this library, please join our Discord server. The Discord community is very friendly and great about quickly answering questions about the use and development of the library. [Click here to join our Discord community!](https://discord.com/invite/mw7xjJ7b7s)
 
 ## License
-[GNU General Public License v3.0](https://github.com/ydataai/ydata-quality/blob/master/LICENSE)
+[GNU General Public License v3.0](https://github.com/Data-Centric-AI-Community/fg-data-quality/blob/master/LICENSE)
 
 ## About
 
diff --git a/setup.py b/setup.py
index 0bf74e5d..8595fdb4 100644
--- a/setup.py
+++ b/setup.py
@@ -7,7 +7,7 @@
 long_description = (here / 'README.md').read_text(encoding='utf-8')
 version = (here / 'VERSION').read_text().rstrip("\n")
 
-setup(name='ydata-quality',
+setup(name='fg-data-quality',
       version=version,
       description='YData open-source tools for Data Quality.',
       long_description=long_description,
@@ -34,8 +34,8 @@
         'Topic :: Software Development :: Libraries :: Python Modules'
       ],
       keywords='data science ydata',
-      url='https://github.com/ydataai/ydata-quality',
-      license="https://github.com/ydataai/ydata-quality/blob/master/LICENSE",
+      url='https://github.com/Data-Centric-AI-Community/fg-data-quality',
+      license="https://github.com/Data-Centric-AI-Community/fg-data-quality/blob/master/LICENSE",
       python_requires=">=3.7, <3.9",
       packages=find_namespace_packages('src'),
       package_dir={'':'src'},
diff --git a/src/data_quality/__init__.py b/src/data_quality/__init__.py
new file mode 100644
index 00000000..d850701d
--- /dev/null
+++ b/src/data_quality/__init__.py
@@ -0,0 +1,22 @@
+"""
+YData open-source lib for Data Quality.
+"""
+from warnings import warn
+
+from .core.data_quality import DataQuality
+
+from .__version__ import __version__  # noqa: F401
+
+warn(
+    """
+    `import ydata_quality` is deprecated and will not receive more updates. 
+    Please install data-quality via `pip install fg-data-quality` and use `import data_quality` instead.
+    """,
+    DeprecationWarning,
+    stacklevel=2,
+)
+
+
+__all__ = [
+    "DataQuality"
+]
diff --git a/src/ydata_quality/bias_fairness/__init__.py b/src/data_quality/bias_fairness/__init__.py
similarity index 100%
rename from src/ydata_quality/bias_fairness/__init__.py
rename to src/data_quality/bias_fairness/__init__.py
diff --git a/src/ydata_quality/bias_fairness/engine.py b/src/data_quality/bias_fairness/engine.py
similarity index 99%
rename from src/ydata_quality/bias_fairness/engine.py
rename to src/data_quality/bias_fairness/engine.py
index 94b099a1..40d0a79f 100644
--- a/src/ydata_quality/bias_fairness/engine.py
+++ b/src/data_quality/bias_fairness/engine.py
@@ -7,7 +7,7 @@
 from pandas import DataFrame, Series
 from dython.nominal import compute_associations
 
-from src.ydata_quality.core.warnings import Priority
+from src.data_quality.core.warnings import Priority
 
 from ..core import QualityEngine, QualityWarning
 from ..utils.correlations import filter_associations
diff --git a/src/ydata_quality/core/__init__.py b/src/data_quality/core/__init__.py
similarity index 100%
rename from src/ydata_quality/core/__init__.py
rename to src/data_quality/core/__init__.py
diff --git a/src/ydata_quality/core/data_quality.py b/src/data_quality/core/data_quality.py
similarity index 100%
rename from src/ydata_quality/core/data_quality.py
rename to src/data_quality/core/data_quality.py
diff --git a/src/ydata_quality/core/engine.py b/src/data_quality/core/engine.py
similarity index 100%
rename from src/ydata_quality/core/engine.py
rename to src/data_quality/core/engine.py
diff --git a/src/ydata_quality/core/warnings.py b/src/data_quality/core/warnings.py
similarity index 100%
rename from src/ydata_quality/core/warnings.py
rename to src/data_quality/core/warnings.py
diff --git a/src/ydata_quality/data_expectations/__init__.py b/src/data_quality/data_expectations/__init__.py
similarity index 100%
rename from src/ydata_quality/data_expectations/__init__.py
rename to src/data_quality/data_expectations/__init__.py
diff --git a/src/ydata_quality/data_expectations/engine.py b/src/data_quality/data_expectations/engine.py
similarity index 99%
rename from src/ydata_quality/data_expectations/engine.py
rename to src/data_quality/data_expectations/engine.py
index 04a14736..82ab736d 100644
--- a/src/ydata_quality/data_expectations/engine.py
+++ b/src/data_quality/data_expectations/engine.py
@@ -6,7 +6,7 @@
 from pandas import DataFrame
 from numpy import argmin
 
-from src.ydata_quality.core.warnings import Priority
+from src.data_quality.core.warnings import Priority
 
 from ..core import QualityEngine, QualityWarning
 from ..utils.auxiliary import test_load_json_path
diff --git a/src/ydata_quality/data_relations/__init__.py b/src/data_quality/data_relations/__init__.py
similarity index 100%
rename from src/ydata_quality/data_relations/__init__.py
rename to src/data_quality/data_relations/__init__.py
diff --git a/src/ydata_quality/data_relations/engine.py b/src/data_quality/data_relations/engine.py
similarity index 99%
rename from src/ydata_quality/data_relations/engine.py
rename to src/data_quality/data_relations/engine.py
index 58f8043b..16881daf 100644
--- a/src/ydata_quality/data_relations/engine.py
+++ b/src/data_quality/data_relations/engine.py
@@ -5,7 +5,7 @@
 
 from numpy import argwhere, ones, tril
 from pandas import DataFrame
-from src.ydata_quality.core.warnings import Priority
+from src.data_quality.core.warnings import Priority
 
 from ..core import QualityEngine, QualityWarning
 from ..utils.auxiliary import infer_dtypes, standard_normalize
diff --git a/src/ydata_quality/drift/__init__.py b/src/data_quality/drift/__init__.py
similarity index 100%
rename from src/ydata_quality/drift/__init__.py
rename to src/data_quality/drift/__init__.py
diff --git a/src/ydata_quality/drift/engine.py b/src/data_quality/drift/engine.py
similarity index 100%
rename from src/ydata_quality/drift/engine.py
rename to src/data_quality/drift/engine.py
diff --git a/src/ydata_quality/duplicates/__init__.py b/src/data_quality/duplicates/__init__.py
similarity index 100%
rename from src/ydata_quality/duplicates/__init__.py
rename to src/data_quality/duplicates/__init__.py
diff --git a/src/ydata_quality/duplicates/engine.py b/src/data_quality/duplicates/engine.py
similarity index 99%
rename from src/ydata_quality/duplicates/engine.py
rename to src/data_quality/duplicates/engine.py
index c12c2ac6..0f23b07b 100644
--- a/src/ydata_quality/duplicates/engine.py
+++ b/src/data_quality/duplicates/engine.py
@@ -6,7 +6,7 @@
 
 from pandas import DataFrame
 
-from src.ydata_quality.core.warnings import Priority
+from src.data_quality.core.warnings import Priority
 
 from ..core import QualityEngine, QualityWarning
 from ..utils.auxiliary import find_duplicate_columns
diff --git a/src/ydata_quality/erroneous_data/__init__.py b/src/data_quality/erroneous_data/__init__.py
similarity index 100%
rename from src/ydata_quality/erroneous_data/__init__.py
rename to src/data_quality/erroneous_data/__init__.py
diff --git a/src/ydata_quality/erroneous_data/engine.py b/src/data_quality/erroneous_data/engine.py
similarity index 99%
rename from src/ydata_quality/erroneous_data/engine.py
rename to src/data_quality/erroneous_data/engine.py
index 7658acf8..f120c28d 100644
--- a/src/ydata_quality/erroneous_data/engine.py
+++ b/src/data_quality/erroneous_data/engine.py
@@ -6,7 +6,7 @@
 
 from pandas import DataFrame
 
-from src.ydata_quality.core.warnings import Priority
+from src.data_quality.core.warnings import Priority
 
 from ..core import QualityEngine, QualityWarning
 from ..utils.enum import DataFrameType
diff --git a/src/ydata_quality/labelling/__init__.py b/src/data_quality/labelling/__init__.py
similarity index 100%
rename from src/ydata_quality/labelling/__init__.py
rename to src/data_quality/labelling/__init__.py
diff --git a/src/ydata_quality/labelling/engine.py b/src/data_quality/labelling/engine.py
similarity index 99%
rename from src/ydata_quality/labelling/engine.py
rename to src/data_quality/labelling/engine.py
index 85b61054..689514d1 100644
--- a/src/ydata_quality/labelling/engine.py
+++ b/src/data_quality/labelling/engine.py
@@ -5,7 +5,7 @@
 
 from pandas import DataFrame, Series
 
-from src.ydata_quality.core.warnings import Priority
+from src.data_quality.core.warnings import Priority
 
 from ..core import QualityEngine, QualityWarning
 from ..utils.auxiliary import infer_dtypes
diff --git a/src/ydata_quality/missings/__init__.py b/src/data_quality/missings/__init__.py
similarity index 100%
rename from src/ydata_quality/missings/__init__.py
rename to src/data_quality/missings/__init__.py
diff --git a/src/ydata_quality/missings/engine.py b/src/data_quality/missings/engine.py
similarity index 99%
rename from src/ydata_quality/missings/engine.py
rename to src/data_quality/missings/engine.py
index b298e7b4..65d21416 100644
--- a/src/ydata_quality/missings/engine.py
+++ b/src/data_quality/missings/engine.py
@@ -5,7 +5,7 @@
 
 from pandas import DataFrame, Series
 
-from src.ydata_quality.core.warnings import Priority
+from src.data_quality.core.warnings import Priority
 
 from ..core import QualityEngine, QualityWarning
 from ..utils.correlations import filter_associations
diff --git a/src/ydata_quality/utils/__init__.py b/src/data_quality/utils/__init__.py
similarity index 100%
rename from src/ydata_quality/utils/__init__.py
rename to src/data_quality/utils/__init__.py
diff --git a/src/ydata_quality/utils/auxiliary.py b/src/data_quality/utils/auxiliary.py
similarity index 100%
rename from src/ydata_quality/utils/auxiliary.py
rename to src/data_quality/utils/auxiliary.py
diff --git a/src/ydata_quality/utils/correlations.py b/src/data_quality/utils/correlations.py
similarity index 100%
rename from src/ydata_quality/utils/correlations.py
rename to src/data_quality/utils/correlations.py
diff --git a/src/ydata_quality/utils/enum.py b/src/data_quality/utils/enum.py
similarity index 100%
rename from src/ydata_quality/utils/enum.py
rename to src/data_quality/utils/enum.py
diff --git a/src/ydata_quality/utils/logger.py b/src/data_quality/utils/logger.py
similarity index 100%
rename from src/ydata_quality/utils/logger.py
rename to src/data_quality/utils/logger.py
diff --git a/src/ydata_quality/utils/modelling.py b/src/data_quality/utils/modelling.py
similarity index 100%
rename from src/ydata_quality/utils/modelling.py
rename to src/data_quality/utils/modelling.py
diff --git a/src/ydata_quality/__init__.py b/src/ydata_quality/__init__.py
index d850701d..b11eccf3 100644
--- a/src/ydata_quality/__init__.py
+++ b/src/ydata_quality/__init__.py
@@ -19,4 +19,4 @@
 
 __all__ = [
     "DataQuality"
-]
+]
\ No newline at end of file
diff --git a/tests/engines/test_data_relations.py b/tests/engines/test_data_relations.py
index f87d2870..83e6ab91 100644
--- a/tests/engines/test_data_relations.py
+++ b/tests/engines/test_data_relations.py
@@ -4,7 +4,7 @@
 import nbformat
 from nbconvert.preprocessors import ExecutePreprocessor
 
-from ydata_quality.data_relations.engine import DataRelationsDetector
+from data_quality.data_relations.engine import DataRelationsDetector
 
 
 @fixture(name='data_relations')
diff --git a/tutorials/bias_fairness.ipynb b/tutorials/bias_fairness.ipynb
index 98b7e318..76ba419e 100644
--- a/tutorials/bias_fairness.ipynb
+++ b/tutorials/bias_fairness.ipynb
@@ -2,11 +2,12 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "# YData Quality - Bias & Fairness Tutorial\n",
+    "# Data Quality - Bias & Fairness Tutorial\n",
     "Time-to-Value: 3 minutes\n",
     "\n",
-    "This notebook provides a tutorial for the ydata_quality package module on Bias & Fairness .\n",
+    "This notebook provides a tutorial for the data_quality package module on Bias & Fairness .\n",
     "\n",
     "**Structure:**\n",
     "\n",
@@ -16,11 +17,11 @@
     "4. Run the quality checks\n",
     "5. Assess the warnings\n",
     "6. (Extra) Detailed overview"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Bias and Fairness\n",
     "As data is increasingly used for automated decision making with heavy impact on individual lifes, a thorough analysis of data quality encompasses an understanding of the inherent biases embedded in the datasets that can cause different treatments based on sensitive attributes. There are ethical and legal obligations for Data Scientists to develop applications which are unbiased and fair.\n",
@@ -42,61 +43,33 @@
     "- Chapter 11 Bias and Fairness | Big Data and Social Science [(link)](https://textbook.coleridgeinitiative.org/chap-bias.html)\n",
     "- Fairness and Machine Learning - Limitations and Opportunities [(link)](https://fairmlbook.org/)\n",
     "- Fairness Tutorial - Moritz Hardt - MLSS 2020, Tübingen [(link)](https://www.youtube.com/watch?v=Igq_S_7IfOU)"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "from ydata_quality.bias_fairness import BiasFairness"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "from data_quality.bias_fairness import BiasFairness"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Load the example dataset\n",
     "The \"Adult Data Set\" (a.k.a. \"Census Income\") contains a set of records to predict whether an individual's income exceeds $50K/yr, based on census data. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "source": [
-    "# This is the DataFrame used in the demo from GE tutorials\n",
-    "df = pd.read_csv('../datasets/transformed/census_10k.csv')\n",
-    "df.head()"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "   age workclass  fnlwgt  education  education-num      marital-status  \\\n",
-       "0   57   Private  132704    Masters             14           Separated   \n",
-       "1   25   Private  321205  Bachelors             13       Never-married   \n",
-       "2   25   Private   38090  Bachelors             13       Never-married   \n",
-       "3   54   Private   29909       11th              7  Married-civ-spouse   \n",
-       "4   43   Private  347934    HS-grad              9           Separated   \n",
-       "\n",
-       "        occupation   relationship   race     sex  capital-gain  capital-loss  \\\n",
-       "0   Prof-specialty  Not-in-family  White    Male         10520             0   \n",
-       "1  Exec-managerial  Not-in-family  White    Male          4101             0   \n",
-       "2            Sales  Not-in-family  White    Male             0             0   \n",
-       "3    Other-service           Wife  White  Female             0             0   \n",
-       "4            Sales  Not-in-family  White  Female             0             0   \n",
-       "\n",
-       "   hours-per-week native-country income workclass2  \n",
-       "0              32  United-States   >50K    Private  \n",
-       "1              35  United-States  <=50K    Private  \n",
-       "2              44  United-States  <=50K    Private  \n",
-       "3              43  United-States  <=50K    Private  \n",
-       "4              30  United-States  <=50K    Private  "
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -233,49 +206,74 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "   age workclass  fnlwgt  education  education-num      marital-status  \\\n",
+       "0   57   Private  132704    Masters             14           Separated   \n",
+       "1   25   Private  321205  Bachelors             13       Never-married   \n",
+       "2   25   Private   38090  Bachelors             13       Never-married   \n",
+       "3   54   Private   29909       11th              7  Married-civ-spouse   \n",
+       "4   43   Private  347934    HS-grad              9           Separated   \n",
+       "\n",
+       "        occupation   relationship   race     sex  capital-gain  capital-loss  \\\n",
+       "0   Prof-specialty  Not-in-family  White    Male         10520             0   \n",
+       "1  Exec-managerial  Not-in-family  White    Male          4101             0   \n",
+       "2            Sales  Not-in-family  White    Male             0             0   \n",
+       "3    Other-service           Wife  White  Female             0             0   \n",
+       "4            Sales  Not-in-family  White  Female             0             0   \n",
+       "\n",
+       "   hours-per-week native-country income workclass2  \n",
+       "0              32  United-States   >50K    Private  \n",
+       "1              35  United-States  <=50K    Private  \n",
+       "2              44  United-States  <=50K    Private  \n",
+       "3              43  United-States  <=50K    Private  \n",
+       "4              30  United-States  <=50K    Private  "
       ]
      },
+     "execution_count": 2,
      "metadata": {},
-     "execution_count": 2
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "# This is the DataFrame used in the demo from GE tutorials\n",
+    "df = pd.read_csv('../datasets/transformed/census_10k.csv')\n",
+    "df.head()"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Create the engine\n",
     "Each engine contains the checks and tests for each suite."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "bf = BiasFairness(df=df, sensitive_features=['race', 'sex'], label='income', random_state=42)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Full Evaluation\n",
     "The easiest way to assess the data quality analysis is to run `.evaluate()` which returns a dictionary with outputs of operation performed."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "source": [
-    "results = bf.evaluate()"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "\u001b[1mWarnings:\u001b[0m\n",
       "\tTOTAL: 2 warning(s)\n",
@@ -289,25 +287,23 @@
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "results = bf.evaluate()"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "After running the data quality checks, you can check the warnings for each individual test of the Bias & Fairness module. The warnings are sorted by priority and have additional details that can provide better insights for Data Scientists."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "source": [
-    "bias_fairness_warnings = bf.get_warnings()\n",
-    "bias_fairness_warnings"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[QualityWarning(category='Bias&Fairness', test='Proxy Identification', description='Found 1 feature pairs of correlation to sensitive attributes with values higher than defined threshold (0.5).', priority=<Priority.P2: 2>, data=features\n",
@@ -318,30 +314,30 @@
        " Name: race, dtype: float64)]"
       ]
      },
+     "execution_count": 5,
      "metadata": {},
-     "execution_count": 5
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "bias_fairness_warnings = bf.get_warnings()\n",
+    "bias_fairness_warnings"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Full Test Suite\n",
-    "In this section, you will find a detailed overview of the available tests in the data expectations module of ydata_quality. These are all run with the `evaluate` method, which centralizes input arguments and produces specific outputs in the returned results dictionary, structured by test."
-   ],
-   "metadata": {}
+    "In this section, you will find a detailed overview of the available tests in the data expectations module of data_quality. These are all run with the `evaluate` method, which centralizes input arguments and produces specific outputs in the returned results dictionary, structured by test."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "source": [
-    "# Results object structure\n",
-    "list(results.keys())"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "['performance_discrimination',\n",
@@ -350,30 +346,30 @@
        " 'sensitive_representativity']"
       ]
      },
+     "execution_count": 6,
      "metadata": {},
-     "execution_count": 6
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "# Results object structure\n",
+    "list(results.keys())"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Performance Discrimination\n",
     "The \"Performance Discrimination\" inspects the disparities in the classifying performance across values of sensitive attributes. A model is trained on the full data but the performance metrics are broken down per each sub-group, enabling the Data Scientist to understand the performance of a baseline classifier throughout each partition of sub-group data."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "source": [
-    "performances = bf.performance_discrimination()\n",
-    "performances"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "{'race': Amer-Indian-Eskimo    1.000000\n",
@@ -387,29 +383,30 @@
        " dtype: float64}"
       ]
      },
+     "execution_count": 7,
      "metadata": {},
-     "execution_count": 7
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "performances = bf.performance_discrimination()\n",
+    "performances"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Proxy Identification\n",
     "The \"Proxy Identification\" test aims to detect associations between sensitive and non-sensitive attributes in the data, to signal Data Scientists for unwanted proxies. The leakage of protected features by non-protected ones may give Data Scientists a false confidence that by disregarding sensitive attributes that they will not be available in the data (directly or indirectly)."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "source": [
-    "bf.proxy_identification(th=0.2)"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "features\n",
@@ -422,14 +419,18 @@
        "Name: association, dtype: float64"
       ]
      },
+     "execution_count": 8,
      "metadata": {},
-     "execution_count": 8
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "bf.proxy_identification(th=0.2)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Sensitive Attributes Predictability\n",
     "The \"Sensitive Attributess Predictability\"  test builds a predictive model with sensitive attributes as targets to grasp how easy it is to build a baseline that can predict protected features based on non-protected ones. Similar to the \"Proxy Identification\" mechanism with more complex relationships (based on a model, not on association measures). The leakage of protected features by non-protected ones may give Data Scientists a false confidence that by disregarding sensitive attributes that they will not be available in the data (directly or indirectly).\n",
@@ -437,19 +438,14 @@
     "The performance metric values are calculated as a ratio of _(real - min)/(max - min)_. The best performance (max) is defined as a perfect prediction, where the predictions equal the true outcomes. The baseline performance (min) is defined as the performance achieved by a naive model, where the predictions are the mode for classification or mean for regression.\n",
     "\n",
     "The values returned by the test indicate the percentage of achievable performance achieved by a baseline model trained to predict a sensitive attribute."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "source": [
-    "sens_pred = bf.sensitive_predictability()\n",
-    "sens_pred"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "race     0.12168\n",
@@ -457,22 +453,22 @@
        "dtype: object"
       ]
      },
+     "execution_count": 9,
      "metadata": {},
-     "execution_count": 9
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "sens_pred = bf.sensitive_predictability()\n",
+    "sens_pred"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "source": [
-    "\n",
-    "bf.sensitive_predictability(adjusted_metric=False)"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "race    0.049889\n",
@@ -480,29 +476,30 @@
        "dtype: object"
       ]
      },
+     "execution_count": 10,
      "metadata": {},
-     "execution_count": 10
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "\n",
+    "bf.sensitive_predictability(adjusted_metric=False)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Sensitive Attributes Representativity\n",
     "The \"Sensitive Attributes Representativity\" calculates the distribution of categorical features to assess if any sub-group of a sensitive attribute is underrepresented. It is meant for Data Scientists to validate the data for sampling bias, i.e. the systematic over/under representation of some members of a population in relation to the others. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "source": [
-    "bf.sensitive_representativity()"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "{'race': White                 0.8537\n",
@@ -516,11 +513,14 @@
        " Name: sex, dtype: float64}"
       ]
      },
+     "execution_count": 11,
      "metadata": {},
-     "execution_count": 11
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "bf.sensitive_representativity()"
+   ]
   }
  ],
  "metadata": {
@@ -528,8 +528,8 @@
    "hash": "cdc2bce73c2a9ac283f602628cabf735dbe06c4ee87a7849fc5f3d1177c8f304"
   },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.10 64-bit ('.venv': venv)"
+   "display_name": "Python 3.8.10 64-bit ('.venv': venv)",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -552,4 +552,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/tutorials/main.ipynb b/tutorials/main.ipynb
index 7ce5103f..4f73418f 100644
--- a/tutorials/main.ipynb
+++ b/tutorials/main.ipynb
@@ -2,9 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "# ydata-quality\n",
-    "> The *ydata_quality* package aims to be for Data Quality what *sklearn* is for machine learning, *matplotlib* for visualization or *pandas* for data manipulation.\n",
+    "# data-quality\n",
+    "> The *data_quality* package aims to be for Data Quality what *sklearn* is for machine learning, *matplotlib* for visualization or *pandas* for data manipulation.\n",
     "\n",
     "Assess data quality throughout the multiple stages of a data pipeline development. Once you have a dataset available, running `DataQuality(df=my_df).evaluate()` provides a comprehensive overview of the details and intricacies of the data, through the perspective of the multiple modules available in the package.\n",
     "\n",
@@ -13,33 +14,25 @@
     "2. Analyze its quality issues; \n",
     "3. Apply strategies to mitigate them;\n",
     "4. Check the new quality analysis on the post-processed (cleaned) data."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Quick Start\n",
     "Load a DataFrame and evaluate your data using `DataQuality`.\n",
     "For more advanced analysis, we can provide additional arguments but we will get there in a minute."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "source": [
-    "from ydata_quality import DataQuality\n",
-    "import pandas as pd\n",
-    "\n",
-    "df = pd.read_csv(f'../datasets/transformed/census_10k.csv') # load data\n",
-    "dq = DataQuality(df=df) # create the main class that holds all quality modules\n",
-    "results = dq.evaluate() # run the tests"
-   ],
+   "execution_count": null,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "\u001b[1mWarnings:\u001b[0m\n",
       "\tTOTAL: 5 warning(s)\n",
@@ -57,17 +50,25 @@
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "from data_quality import DataQuality\n",
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(f'../datasets/transformed/census_10k.csv') # load data\n",
+    "dq = DataQuality(df=df) # create the main class that holds all quality modules\n",
+    "results = dq.evaluate() # run the tests"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "From the report, we get multiple warnings with different priorities (lower is of higher importance). The warnings were generated automatically by the default tests implemented in each module. From the report, we get an overall sense for each quality warning but this doesn't tell the whole story. To investigate the details, let's pick a QualityWarning and analyze it."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Warnings\n",
     "The warnings contain details for issues detected during the data quality analysis. For any given issue, the warning contains the information required by the Data Scientist to fully grasp the quality issue and how it is impacting the current dataset.\n",
@@ -75,38 +76,38 @@
     "Warnings can be fetched with `.get_warnings()` as a list of `QualityWarning`'s (best for coding) and are also summarized in the `.evaluate()` which print the overall status (best for analysis, visualization).\n",
     "\n",
     "Warnings are generated automatically for some tests but can be created by Data Scientists as well and added to existing engines."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "source": [
-    "dq.get_warnings(test='Duplicate Columns')[0].data"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "{'workclass': ['workclass2']}"
       ]
      },
+     "execution_count": 11,
      "metadata": {},
-     "execution_count": 11
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "dq.get_warnings(test='Duplicate Columns')[0].data"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "From the warning details, we know that the 'workclass2' feature is an exact copy of the original 'workclass'. For a typical machine learning pipeline, the duplicated feature 'workclass2' could be dropped as it is not adding any value (all info is already present) and may cause a toll in performance (e.g. due to collinearity effects)."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Modules\n",
     "A full picture of data quality requires multiple perspectives, which we deliver in a modular way: Bias & Fairness, Data Expectations, Data Relations, Drift Analysis, Erroneous Data, Labelling and Missing. All of the engines are integrated into a single `DataQuality` class that allows you to run everything at once, providing a holistic perspective of your data. From the main `DataQuality` you can access the individual engines with the `.engines` property.\n",
@@ -114,21 +115,16 @@
     "Some of the modules will not run unless you provide specific arguments (e.g. target feature name for Labelling). By default, `DataQuality` will only contain the engines which have valid arguments and will drop all of those who are not sufficiently specified on the initialization.\n",
     "\n",
     "Since we didn't specify any sensitive features, the Bias&Fairness engine didn't run but we can define it now as a standalone as well."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "source": [
-    "from ydata_quality.bias_fairness import BiasFairness\n",
-    "bf = BiasFairness(df=df, sensitive_features=['race', 'sex'], label='income')\n",
-    "bf_results = bf.evaluate()"
-   ],
+   "execution_count": null,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "\u001b[1mWarnings:\u001b[0m\n",
       "\tTOTAL: 2 warning(s)\n",
@@ -142,26 +138,25 @@
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "from data_quality.bias_fairness import BiasFairness\n",
+    "bf = BiasFairness(df=df, sensitive_features=['race', 'sex'], label='income')\n",
+    "bf_results = bf.evaluate()"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "From the report, we know that we may have a proxy feature leaking information about a sensitive attribute (cf. PROXY IDENTIFICATION) and severe under-representation of feature values of a sensitive attribute. To investigate, we can fetch the warnings with the `get_warnings` method filtering for a specific test."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "source": [
-    "# Looks like the 'relationship' and 'sex' features are highly correlated. Even if we removed the feature 'sex' from the data, \n",
-    "# the 'relationship' feature could still leak information about the original sensitive attribute\n",
-    "bf.get_warnings(test='Proxy Identification')"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[QualityWarning(category='Bias&Fairness', test='Proxy Identification', description='Found 1 feature pairs of correlation to sensitive attributes with values higher than defined threshold (0.5).', priority=<Priority.P2: 2>, data=features\n",
@@ -169,22 +164,23 @@
        " Name: association, dtype: float64)]"
       ]
      },
+     "execution_count": 13,
      "metadata": {},
-     "execution_count": 13
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "# Looks like the 'relationship' and 'sex' features are highly correlated. Even if we removed the feature 'sex' from the data, \n",
+    "# the 'relationship' feature could still leak information about the original sensitive attribute\n",
+    "bf.get_warnings(test='Proxy Identification')"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "source": [
-    "# From observing the data, we see that some relationship status (e.g. Husband, Wife) are gender-specific, thus impacting the correlation.\n",
-    "df[['relationship', 'sex']].value_counts().sort_index()"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "relationship    sex   \n",
@@ -202,23 +198,29 @@
        "dtype: int64"
       ]
      },
+     "execution_count": 14,
      "metadata": {},
-     "execution_count": 14
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "# From observing the data, we see that some relationship status (e.g. Husband, Wife) are gender-specific, thus impacting the correlation.\n",
+    "df[['relationship', 'sex']].value_counts().sort_index()"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Data Cleaning\n",
     "After the data quality issues have been detected, we can build a data processing pipeline with the guidance from the warnings raised above."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def improve_quality(df: pd.DataFrame):\n",
     "    \"Clean the data based on the Data Quality issues found previously.\"\n",
@@ -232,30 +234,25 @@
     "    return df\n",
     "\n",
     "clean_df = improve_quality(df.copy())"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Cleaned Data - DataQuality\n",
     "To check the impact of our data cleaning pipeline, we create a new DataQuality class now based on the improved version of the original data.\n",
     "Given that we removed the duplicated column and we erased the exact feature value duplicates, those warnings are not raised in the new DataQuality engine."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
-   "source": [
-    "better_dq = DataQuality(df=clean_df) # main class on cleaned data\n",
-    "results = better_dq.evaluate() # run the tests"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "\u001b[1mWarnings:\u001b[0m\n",
       "\tTOTAL: 3 warning(s)\n",
@@ -270,28 +267,27 @@
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "better_dq = DataQuality(df=clean_df) # main class on cleaned data\n",
+    "results = better_dq.evaluate() # run the tests"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Cleaned Data - Specific Module\n",
     "For the specific analysis of Bias & Fairness, we see that the previous QualityWarning of \"Proxy Identification\" has disappeared. To check the new association results, we lower the threshold and observe that the association measure between 'relationship' and 'sex' features has dropped from 0.65 to 0.48."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
-   "source": [
-    "# Specific analysis for Bias & Fairness with improved dataframe\n",
-    "better_bf = BiasFairness(df=clean_df, sensitive_features=['race', 'sex'], label='income')\n",
-    "_ = better_bf.evaluate()"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "\u001b[1mWarnings:\u001b[0m\n",
       "\tTOTAL: 1 warning(s)\n",
@@ -304,18 +300,18 @@
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "# Specific analysis for Bias & Fairness with improved dataframe\n",
+    "better_bf = BiasFairness(df=clean_df, sensitive_features=['race', 'sex'], label='income')\n",
+    "_ = better_bf.evaluate()"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
-   "source": [
-    "# Lowering the threshold detects more possible proxies\n",
-    "better_bf.proxy_identification(th=0.45)"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "features\n",
@@ -324,43 +320,47 @@
        "Name: association, dtype: float64"
       ]
      },
+     "execution_count": 18,
      "metadata": {},
-     "execution_count": 18
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "# Lowering the threshold detects more possible proxies\n",
+    "better_bf.proxy_identification(th=0.45)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## The End\n",
-    "That's it. In this quick tutorial, you learned how to use `ydata_quality` to assess the Data Quality of your dataset, both with the `DataQuality` main aggregator or through a specific module engine (e.g. `BiasFairness`). We introduced `QualityWarning`'s and how they provide a high-level measure of severity (cf. Priority) and contain the original data that raised the alarm. Based on the data quality insights, we defined a data cleaning pipeline and observed how it solved the warnings we aimed for."
-   ],
-   "metadata": {}
+    "That's it. In this quick tutorial, you learned how to use `data_quality` to assess the Data Quality of your dataset, both with the `DataQuality` main aggregator or through a specific module engine (e.g. `BiasFairness`). We introduced `QualityWarning`'s and how they provide a high-level measure of severity (cf. Priority) and contain the original data that raised the alarm. Based on the data quality insights, we defined a data cleaning pipeline and observed how it solved the warnings we aimed for."
+   ]
   }
  ],
  "metadata": {
-  "orig_nbformat": 4,
+  "interpreter": {
+   "hash": "cdc2bce73c2a9ac283f602628cabf735dbe06c4ee87a7849fc5f3d1177c8f304"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.10 64-bit ('.venv': venv)",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python",
-   "version": "3.8.10",
-   "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
     "version": 3
    },
-   "pygments_lexer": "ipython3",
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
    "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.10 64-bit ('.venv': venv)"
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
   },
-  "interpreter": {
-   "hash": "cdc2bce73c2a9ac283f602628cabf735dbe06c4ee87a7849fc5f3d1177c8f304"
-  }
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}