diff --git a/.gitignore b/.gitignore
index 911404f..92b0f4a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -207,7 +207,7 @@ fabric.properties
 # Android studio 3.1+ serialized cache file
 .idea/caches/build_file_checksums.ser
 
-notebooks/RetailHero-data/*
+notebooks/content/*
 notebooks/catboost_info
 notebooks/*.tmp
 
diff --git a/Readme.rst b/Readme.rst
index 8dfdb3e..1826fd5 100644
--- a/Readme.rst
+++ b/Readme.rst
@@ -11,15 +11,18 @@
 .. |Docs| image:: https://readthedocs.org/projects/scikit-uplift/badge/?version=latest
 .. _Docs: https://scikit-uplift.readthedocs.io/en/latest/
 
-.. _RU: https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb
-.. _EN: https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb
-
 .. |Open In Colab1| image:: https://colab.research.google.com/assets/colab-badge.svg
 .. _Open In Colab1: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb
 
 .. |Open In Colab2| image:: https://colab.research.google.com/assets/colab-badge.svg
 .. _Open In Colab2: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb
-   
+
+.. |Open In Colab3| image:: https://colab.research.google.com/assets/colab-badge.svg
+.. _Open In Colab3: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb
+
+.. |Open In Colab4| image:: https://colab.research.google.com/assets/colab-badge.svg
+.. _Open In Colab4: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb
+
 .. _scikit-uplift.readthedocs.io: https://scikit-uplift.readthedocs.io/en/latest/
 .. _Part 1: https://habr.com/ru/company/ru_mts/blog/485980/
 .. _Part 2: https://habr.com/ru/company/ru_mts/blog/485976/
@@ -50,9 +53,13 @@ More about uplift modelling problem read in russian on habr.com: `Part 1`_ and `
 
 * Applying any estimator adheres to scikit-learn conventions;
 
+* All approaches can be used in sklearn.pipeline (see example (`EN <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb>`__ |Open In Colab3|_, `RU <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb>`__ |Open In Colab4|_))
+
 * Almost all implemented approaches solve both the problem of classification and regression;
 
-* A lot of metrics (Such as *Area Under Uplift Curve* or *Area Under Qini Curve*) are implemented to evaluate your uplift model.
+* A lot of metrics (Such as *Area Under Uplift Curve* or *Area Under Qini Curve*) are implemented to evaluate your uplift model;
+
+* Useful graphs for analyzing the built model.
 
 Installation
 -------------
@@ -89,7 +96,7 @@ And if you now point your browser to ``_build/html/index.html``, you should see
 Quick Start
 -----------
 
-See the **RetailHero tutorial notebook** (`EN`_ |Open In Colab1|_, `RU`_ |Open In Colab2|_) for details.
+See the **RetailHero tutorial notebook** (`EN <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb>`__ |Open In Colab1|_, `RU <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`__ |Open In Colab2|_) for details.
 
 **Train and predict uplift model**
 
@@ -157,7 +164,7 @@ Important links
 
 - Official source code repo: https://github.com/maks-sh/scikit-uplift/
 - Issue tracker: https://github.com/maks-sh/scikit-uplift/issues
-
+- Release History: https://scikit-uplift.readthedocs.io/en/latest/changelog.html
 
 ===============
 
diff --git a/docs/changelog.md b/docs/changelog.md
index f5ca79a..977495d 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -8,6 +8,24 @@
 * 🔨 something that previously didn’t work as documentated – or according to reasonable expectations – should now work.
 * ❗️ you will need to change your code to have the same effect in the future; or a feature will be removed in the future.
 
+## Version 0.1.1
+
+### [sklift.viz](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html)
+
+* 💥 Add [plot_uplift_by_percentile](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html#sklift.viz.base.plot_uplift_by_percentile) by [@ElisovaIra](https://github.com/ElisovaIra).
+* 🔨 Fix bug with import [plot_treatment_balance_curve](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html#sklift.viz.base.plot_treatment_balance_curve).
+
+### [sklift.metrics](https://scikit-uplift.readthedocs.io/en/latest/api/metrics.html)
+
+* 💥 Add [response_rate_by_percentile](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html#sklift.metrics.metrics.response_rate_by_percentile) by [@ElisovaIra](https://github.com/ElisovaIra).
+* 🔨 Fix bug with import [uplift_auc_score](https://scikit-uplift.readthedocs.io/en/latest/api/metrics.html#sklift.metrics.metrics.uplift_auc_score) and [qini_auc_score](https://scikit-uplift.readthedocs.io/en/latest/metrics.html#sklift.metrics.metrics.qini_auc_score).
+* 📝 Fix typos in docstrings.
+
+### Miscellaneous
+
+* 💥 Add tutorial ["Example of usage model from sklift.models in sklearn.pipeline"](https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb).
+* 📝 Add link to Release History in main Readme.md.
+
 ## Version 0.1.0
 
 ### [sklift.models](https://scikit-uplift.readthedocs.io/en/latest/api/models.html)
@@ -19,6 +37,7 @@
 
 * 💥 Add [treatment_balance_curve](https://scikit-uplift.readthedocs.io/en/latest/api/metrics.html#sklift.metrics.metrics.treatment_balance_curve) by [@spiaz](https://github.com/spiaz).
 * ❗️ The metrics `auuc` and `auqc` are now respectively renamed to [uplift_auc_score](https://scikit-uplift.readthedocs.io/en/latest/api/metrics.html#sklift.metrics.metrics.uplift_auc_score) and [qini_auc_score](https://scikit-uplift.readthedocs.io/en/latest/metrics.html#sklift.metrics.metrics.qini_auc_score). So, `auuc` and `auqc` will be removed in 0.2.0.
+* ❗️ Add a new parameter `startegy` in [uplift_at_k](https://scikit-uplift.readthedocs.io/en/latest/metrics.html#sklift.metrics.metrics.uplift_at_k).
 
 ### [sklift.viz](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html)
 
diff --git a/docs/index.rst b/docs/index.rst
index 0973c70..343979e 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -1,6 +1,12 @@
 .. _Part 1: https://habr.com/ru/company/ru_mts/blog/485980/
 .. _Part 2: https://habr.com/ru/company/ru_mts/blog/485976/
 
+.. |Open In Colab3| image:: https://colab.research.google.com/assets/colab-badge.svg
+.. _Open In Colab3: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb
+
+.. |Open In Colab4| image:: https://colab.research.google.com/assets/colab-badge.svg
+.. _Open In Colab4: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb
+
 **************
 scikit-uplift
 **************
@@ -18,9 +24,13 @@ Features
 
 * Applying any estimator adheres to scikit-learn conventions;
 
+* All approaches can be used in sklearn.pipeline (see example (`EN <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb>`_ |Open In Colab3|_, `RU <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb>`_ |Open In Colab4|_))
+
 * Almost all implemented approaches solve both the problem of classification and regression;
 
-* A lot of metrics (Such as *Area Under Uplift Curve* or *Area Under Qini Curve*) are implemented to evaluate your uplift model.
+* A lot of metrics (Such as *Area Under Uplift Curve* or *Area Under Qini Curve*) are implemented to evaluate your uplift model;
+
+* Useful graphs for analyzing the built model.
 
 
 **The package currently supports the following methods:**
diff --git a/docs/tutorials.rst b/docs/tutorials.rst
index db202f0..ba09bdc 100644
--- a/docs/tutorials.rst
+++ b/docs/tutorials.rst
@@ -4,6 +4,11 @@
 .. |Open In Colab2| image:: https://colab.research.google.com/assets/colab-badge.svg
    :target: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb
 
+.. _Example of usage model from sklift.models in sklearn.pipeline: https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb
+.. |Open In Colab3| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb
+.. |Open In Colab4| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb
 
 **********
 Tutorials
@@ -12,10 +17,12 @@ Tutorials
 Basic
 ########
 
-It is better to start scikit-uplift from the basic tutorial.
+It is better to start scikit-uplift from the basic tutorials.
 
 * `The overview of the basic approaches to solving the Uplift Modeling problem`_
-    * In Englsih: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb>`_ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb>`_   |Open In Colab1|
-    * In Russian: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`_ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`_   |Open In Colab2|
-
+    * In English: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb>`__ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb>`__   |Open In Colab1|
+    * In Russian: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`__ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`__   |Open In Colab2|
 
+* `Example of usage model from sklift.models in sklearn.pipeline`_
+    * In English: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb>`__ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb>`__   |Open In Colab3|
+    * In Russian: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb>`__ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb>`__   |Open In Colab4|
diff --git a/notebooks/Readme.rst b/notebooks/Readme.rst
new file mode 100644
index 0000000..c962e1a
--- /dev/null
+++ b/notebooks/Readme.rst
@@ -0,0 +1,28 @@
+.. _The overview of the basic approaches to solving the Uplift Modeling problem: https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb
+.. |Open In Colab1| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb
+.. |Open In Colab2| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb
+
+.. _Example of usage model from sklift.models in sklearn.pipeline: https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb
+.. |Open In Colab3| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb
+.. |Open In Colab4| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb
+
+**********
+Tutorials
+**********
+
+Basic
+########
+
+It is better to start scikit-uplift from the basic tutorials.
+
+* `The overview of the basic approaches to solving the Uplift Modeling problem`_
+    * In English: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb>`_ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb>`_   |Open In Colab1|
+    * In Russian: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`_ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`_   |Open In Colab2|
+
+* `Example of usage model from sklift.models in sklearn.pipeline`_
+    * In English: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb>`_ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb>`_   |Open In Colab3|
+    * In Russian: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb>`_ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb>`_   |Open In Colab4|
diff --git a/notebooks/RetailHero.ipynb b/notebooks/RetailHero.ipynb
index 837c80e..e2c424c 100644
--- a/notebooks/RetailHero.ipynb
+++ b/notebooks/RetailHero.ipynb
@@ -107,8 +107,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:07.634319Z",
-     "start_time": "2020-04-16T20:41:07.630711Z"
+     "end_time": "2020-04-28T09:24:37.492036Z",
+     "start_time": "2020-04-28T09:24:37.488584Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -126,7 +126,7 @@
     "urllib.request.urlretrieve(url, '/content/retail_hero.zip')\n",
     "\n",
     "!unzip /content/retail_hero.zip\n",
-    "!pip install scikit-uplift==0.1.0 catboost=0.22"
+    "!pip install scikit-uplift==0.1.1 catboost=0.22"
    ]
   },
   {
@@ -144,8 +144,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:10.715080Z",
-     "start_time": "2020-04-16T20:41:07.644388Z"
+     "end_time": "2020-04-28T09:24:40.783897Z",
+     "start_time": "2020-04-28T09:24:37.503470Z"
     },
     "colab": {},
     "colab_type": "code",
@@ -196,8 +196,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:11.873021Z",
-     "start_time": "2020-04-16T20:41:10.717336Z"
+     "end_time": "2020-04-28T09:24:42.100096Z",
+     "start_time": "2020-04-28T09:24:40.786498Z"
     },
     "colab": {},
     "colab_type": "code",
@@ -251,8 +251,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:14.061983Z",
-     "start_time": "2020-04-16T20:41:11.875565Z"
+     "end_time": "2020-04-28T09:24:44.483576Z",
+     "start_time": "2020-04-28T09:24:42.102707Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -313,8 +313,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:14.084652Z",
-     "start_time": "2020-04-16T20:41:14.064998Z"
+     "end_time": "2020-04-28T09:24:44.511016Z",
+     "start_time": "2020-04-28T09:24:44.486035Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -484,8 +484,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:15.254843Z",
-     "start_time": "2020-04-16T20:41:14.087893Z"
+     "end_time": "2020-04-28T09:24:45.715602Z",
+     "start_time": "2020-04-28T09:24:44.514353Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -545,8 +545,8 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:17.230517Z",
-     "start_time": "2020-04-16T20:41:15.257156Z"
+     "end_time": "2020-04-28T09:24:47.941480Z",
+     "start_time": "2020-04-28T09:24:45.719641Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -632,8 +632,8 @@
    "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:19.361999Z",
-     "start_time": "2020-04-16T20:41:17.233889Z"
+     "end_time": "2020-04-28T09:24:50.571779Z",
+     "start_time": "2020-04-28T09:24:47.944822Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -694,8 +694,8 @@
    "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:21.519463Z",
-     "start_time": "2020-04-16T20:41:19.366080Z"
+     "end_time": "2020-04-28T09:24:52.942803Z",
+     "start_time": "2020-04-28T09:24:50.576741Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -758,8 +758,8 @@
    "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:21.536036Z",
-     "start_time": "2020-04-16T20:41:21.522709Z"
+     "end_time": "2020-04-28T09:24:52.964396Z",
+     "start_time": "2020-04-28T09:24:52.945544Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -858,8 +858,8 @@
    "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:24.150095Z",
-     "start_time": "2020-04-16T20:41:21.539068Z"
+     "end_time": "2020-04-28T09:24:56.505700Z",
+     "start_time": "2020-04-28T09:24:53.019392Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -906,8 +906,8 @@
    "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:24.171639Z",
-     "start_time": "2020-04-16T20:41:24.153353Z"
+     "end_time": "2020-04-28T09:24:56.560018Z",
+     "start_time": "2020-04-28T09:24:56.508541Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
diff --git a/notebooks/RetailHero_EN.ipynb b/notebooks/RetailHero_EN.ipynb
index 6c7d5e3..45eacf9 100644
--- a/notebooks/RetailHero_EN.ipynb
+++ b/notebooks/RetailHero_EN.ipynb
@@ -18,7 +18,7 @@
     "    <b><a href=\"https://github.com/maks-sh/scikit-uplift/\">SCIKIT-UPLIFT REPO</a> | </b>\n",
     "    <b><a href=\"https://scikit-uplift.readthedocs.io/en/latest/\">SCIKIT-UPLIFT DOCS</a></b>\n",
     "    <br>\n",
-    "    <b><a href=\"https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb\">RUSSIAN VERSION</a>\n",
+    "    <b><a href=\"https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb\">RUSSIAN VERSION</a></b>\n",
     "</center>"
    ]
   },
@@ -98,8 +98,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:45:13.495345Z",
-     "start_time": "2020-04-16T20:45:13.491423Z"
+     "end_time": "2020-04-28T09:26:59.973637Z",
+     "start_time": "2020-04-28T09:26:59.969856Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -111,13 +111,13 @@
    },
    "outputs": [],
    "source": [
-    "# import urllib.request\n",
+    "import urllib.request\n",
     "\n",
-    "# url = 'https://drive.google.com/u/0/uc?id=1fkxNmihuS15kk0PP0QcphL_Z3_z8LLeb&export=download'\n",
-    "# urllib.request.urlretrieve(url, '/content/retail_hero.zip')\n",
+    "url = 'https://drive.google.com/u/0/uc?id=1fkxNmihuS15kk0PP0QcphL_Z3_z8LLeb&export=download'\n",
+    "urllib.request.urlretrieve(url, '/content/retail_hero.zip')\n",
     "\n",
-    "# !unzip /content/retail_hero.zip\n",
-    "# !pip install scikit-uplift==0.1.0 catboost==0.22"
+    "!unzip /content/retail_hero.zip\n",
+    "!pip install scikit-uplift==0.1.1 catboost=0.22"
    ]
   },
   {
@@ -135,8 +135,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:45:16.518699Z",
-     "start_time": "2020-04-16T20:45:13.505025Z"
+     "end_time": "2020-04-28T09:27:03.353098Z",
+     "start_time": "2020-04-28T09:26:59.984369Z"
     },
     "colab": {},
     "colab_type": "code",
@@ -151,9 +151,9 @@
     "\n",
     "\n",
     "# reading data\n",
-    "df_clients = pd.read_csv('./RetailHero-data/clients.csv', index_col='client_id')\n",
-    "df_train = pd.read_csv('./RetailHero-data/uplift_train.csv', index_col='client_id')\n",
-    "df_test = pd.read_csv('./RetailHero-data/uplift_test.csv', index_col='client_id')\n",
+    "df_clients = pd.read_csv('/content/uplift_data/clients.csv', index_col='client_id')\n",
+    "df_train = pd.read_csv('/content/uplift_data/uplift_train.csv', index_col='client_id')\n",
+    "df_test = pd.read_csv('/content/uplift_data/uplift_test.csv', index_col='client_id')\n",
     "\n",
     "# extracting features\n",
     "df_features = df_clients.copy()\n",
@@ -187,8 +187,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:45:17.756422Z",
-     "start_time": "2020-04-16T20:45:16.521360Z"
+     "end_time": "2020-04-28T09:27:04.563554Z",
+     "start_time": "2020-04-28T09:27:03.355432Z"
     },
     "colab": {},
     "colab_type": "code",
@@ -241,8 +241,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:45:19.899785Z",
-     "start_time": "2020-04-16T20:45:17.759288Z"
+     "end_time": "2020-04-28T09:27:06.789462Z",
+     "start_time": "2020-04-28T09:27:04.570306Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -305,8 +305,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:45:19.921897Z",
-     "start_time": "2020-04-16T20:45:19.902457Z"
+     "end_time": "2020-04-28T09:27:06.813310Z",
+     "start_time": "2020-04-28T09:27:06.792837Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -476,8 +476,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:45:21.009561Z",
-     "start_time": "2020-04-16T20:45:19.925398Z"
+     "end_time": "2020-04-28T09:27:07.960855Z",
+     "start_time": "2020-04-28T09:27:06.816440Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -538,8 +538,8 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:45:22.998801Z",
-     "start_time": "2020-04-16T20:45:21.011706Z"
+     "end_time": "2020-04-28T09:27:10.062915Z",
+     "start_time": "2020-04-28T09:27:07.972200Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -627,8 +627,8 @@
    "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:45:25.105146Z",
-     "start_time": "2020-04-16T20:45:23.001150Z"
+     "end_time": "2020-04-28T09:27:12.177386Z",
+     "start_time": "2020-04-28T09:27:10.065653Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -689,8 +689,8 @@
    "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:45:27.262646Z",
-     "start_time": "2020-04-16T20:45:25.108914Z"
+     "end_time": "2020-04-28T09:27:14.459477Z",
+     "start_time": "2020-04-28T09:27:12.181749Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -753,8 +753,8 @@
    "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:45:27.280762Z",
-     "start_time": "2020-04-16T20:45:27.265440Z"
+     "end_time": "2020-04-28T09:27:14.480794Z",
+     "start_time": "2020-04-28T09:27:14.463302Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -853,8 +853,8 @@
    "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:45:30.063722Z",
-     "start_time": "2020-04-16T20:45:27.284197Z"
+     "end_time": "2020-04-28T09:27:17.097949Z",
+     "start_time": "2020-04-28T09:27:14.483691Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -901,8 +901,8 @@
    "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:45:30.101502Z",
-     "start_time": "2020-04-16T20:45:30.067156Z"
+     "end_time": "2020-04-28T09:27:17.138755Z",
+     "start_time": "2020-04-28T09:27:17.101433Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
diff --git a/notebooks/pipeline_usage_EN.ipynb b/notebooks/pipeline_usage_EN.ipynb
new file mode 100644
index 0000000..5627162
--- /dev/null
+++ b/notebooks/pipeline_usage_EN.ipynb
@@ -0,0 +1,464 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Example of usage model from sklift.models in sklearn.pipeline\n",
+    "\n",
+    "<br>\n",
+    "<center>\n",
+    "    <a href=\"https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb\">\n",
+    "        <img src=\"https://colab.research.google.com/assets/colab-badge.svg\">\n",
+    "    </a>\n",
+    "    <br>\n",
+    "    <b><a href=\"https://github.com/maks-sh/scikit-uplift/\">SCIKIT-UPLIFT REPO</a> | </b>\n",
+    "    <b><a href=\"https://scikit-uplift.readthedocs.io/en/latest/\">SCIKIT-UPLIFT DOCS</a></b>\n",
+    "    <br>\n",
+    "    <b><a href=\"https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb\">RUSSIAN VERSION</a></b>\n",
+    "\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-26T12:44:35.435852Z",
+     "start_time": "2020-04-26T12:44:35.239050Z"
+    }
+   },
+   "source": [
+    "This is a simple example on how to use [sklift.models](https://scikit-uplift.readthedocs.io/en/latest/api/models.html) with [sklearn.pipeline](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.pipeline).\n",
+    "\n",
+    "The data is taken from [MineThatData E-Mail Analytics And Data Mining Challenge dataset by Kevin Hillstrom](https://blog.minethatdata.com/2008/03/minethatdata-e-mail-analytics-and-data.html).\n",
+    "\n",
+    "This dataset contains 64,000 customers who last purchased within twelve months. The customers were involved in an e-mail test:\n",
+    "* 1/3 were randomly chosen to receive an e-mail campaign featuring Mens merchandise.\n",
+    "* 1/3 were randomly chosen to receive an e-mail campaign featuring Womens merchandise.\n",
+    "* 1/3 were randomly chosen to not receive an e-mail campaign.\n",
+    "\n",
+    "During a period of two weeks following the e-mail campaign, results were tracked. The task is to tell the world if the Mens or Womens e-mail campaign was successful.\n",
+    "\n",
+    "The full description of the dataset can be found at the [link](https://blog.minethatdata.com/2008/03/minethatdata-e-mail-analytics-and-data.html).\n",
+    "\n",
+    "Firstly, install the necessary libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:14:10.044500Z",
+     "start_time": "2020-04-28T09:14:10.037045Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install scikit-uplift==0.1.1 xgboost==1.0.2 category_encoders==2.1.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Secondly, load the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:14:14.101729Z",
+     "start_time": "2020-04-28T09:14:10.048276Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('./content/Hilstorm.csv', <http.client.HTTPMessage at 0x117d3b0f0>)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import urllib.request\n",
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "csv_path = '/content/Hilstorm.csv'\n",
+    "url = 'http://www.minethatdata.com/Kevin_Hillstrom_MineThatData_E-MailAnalytics_DataMiningChallenge_2008.03.20.csv'\n",
+    "urllib.request.urlretrieve(url, csv_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For simplicity of the example, we will leave only two user segments:\n",
+    "* those who were sent an e-mail advertising campaign with women's products;\n",
+    "* those who were not sent out the ad campaign.\n",
+    "\n",
+    "We will use the `visit` variable as the target variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:14:14.638574Z",
+     "start_time": "2020-04-28T09:14:14.106920Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the dataset before processing: (64000, 12)\n",
+      "Shape of the dataset after processing: (42693, 10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>recency</th>\n",
+       "      <th>history_segment</th>\n",
+       "      <th>history</th>\n",
+       "      <th>mens</th>\n",
+       "      <th>womens</th>\n",
+       "      <th>zip_code</th>\n",
+       "      <th>newbie</th>\n",
+       "      <th>channel</th>\n",
+       "      <th>visit</th>\n",
+       "      <th>treatment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>2) $100 - $200</td>\n",
+       "      <td>142.44</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Surburban</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Phone</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3) $200 - $350</td>\n",
+       "      <td>329.08</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2) $100 - $200</td>\n",
+       "      <td>180.65</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Surburban</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1) $0 - $100</td>\n",
+       "      <td>45.34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2) $100 - $200</td>\n",
+       "      <td>134.83</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Surburban</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Phone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   recency history_segment  history  mens  womens   zip_code  newbie channel  \\\n",
+       "0       10  2) $100 - $200   142.44     1       0  Surburban       0   Phone   \n",
+       "1        6  3) $200 - $350   329.08     1       1      Rural       1     Web   \n",
+       "2        7  2) $100 - $200   180.65     0       1  Surburban       1     Web   \n",
+       "4        2    1) $0 - $100    45.34     1       0      Urban       0     Web   \n",
+       "5        6  2) $100 - $200   134.83     0       1  Surburban       0   Phone   \n",
+       "\n",
+       "   visit  treatment  \n",
+       "0      0          1  \n",
+       "1      0          0  \n",
+       "2      0          1  \n",
+       "4      0          1  \n",
+       "5      1          1  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "dataset = pd.read_csv(csv_path)\n",
+    "print(f'Shape of the dataset before processing: {dataset.shape}')\n",
+    "dataset = dataset[dataset['segment']!='Mens E-Mail']\n",
+    "dataset.loc[:, 'treatment'] = dataset['segment'].map({\n",
+    "    'Womens E-Mail': 1,\n",
+    "    'No E-Mail': 0\n",
+    "})\n",
+    "\n",
+    "dataset = dataset.drop(['segment', 'conversion', 'spend'], axis=1)\n",
+    "print(f'Shape of the dataset after processing: {dataset.shape}')\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Divide all the data into a training and validation sample:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:14:15.419422Z",
+     "start_time": "2020-04-28T09:14:14.642252Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "\n",
+    "Xyt_tr, Xyt_val = train_test_split(dataset, test_size=0.5, random_state=42)\n",
+    "\n",
+    "X_tr = Xyt_tr.drop(['visit', 'treatment'], axis=1)\n",
+    "y_tr = Xyt_tr['visit']\n",
+    "treat_tr = Xyt_tr['treatment']\n",
+    "\n",
+    "X_val = Xyt_val.drop(['visit', 'treatment'], axis=1)\n",
+    "y_val = Xyt_val['visit']\n",
+    "treat_val = Xyt_val['treatment']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select categorical features:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:14:15.432871Z",
+     "start_time": "2020-04-28T09:14:15.421629Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['history_segment', 'zip_code', 'channel']\n"
+     ]
+    }
+   ],
+   "source": [
+    "cat_cols = X_tr.select_dtypes(include='object').columns.tolist()\n",
+    "print(cat_cols)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the necessary objects and combining them into a pipieline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:14:15.562848Z",
+     "start_time": "2020-04-28T09:14:15.437086Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "from category_encoders import CatBoostEncoder\n",
+    "from sklift.models import ClassTransformation\n",
+    "from xgboost import XGBClassifier\n",
+    "\n",
+    "\n",
+    "encoder = CatBoostEncoder(cols=cat_cols)\n",
+    "estimator = XGBClassifier(max_depth=2, random_state=42)\n",
+    "ct = ClassTransformation(estimator=estimator)\n",
+    "\n",
+    "my_pipeline = Pipeline([\n",
+    "    ('encoder', encoder),\n",
+    "    ('model', ct)\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>Train pipeline as usual, but adding the treatment column in the step model as a parameter `model__treatment`.</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:14:16.816735Z",
+     "start_time": "2020-04-28T09:14:15.568337Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/pipeline.py:354: UserWarning: It is recommended to use this approach on treatment balanced data. Current sample size is unbalanced.\n",
+      "  self._final_estimator.fit(Xt, y, **fit_params)\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_pipeline = my_pipeline.fit(\n",
+    "    X=X_tr,\n",
+    "    y=y_tr,\n",
+    "    model__treatment=treat_tr\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-26T18:07:44.970856Z",
+     "start_time": "2020-04-26T18:07:44.964624Z"
+    }
+   },
+   "source": [
+    "Predict the uplift and calculate the uplift@30%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:14:16.960683Z",
+     "start_time": "2020-04-28T09:14:16.819117Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "uplift@30%: 0.0661\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklift.metrics import uplift_at_k\n",
+    "\n",
+    "\n",
+    "uplift_predictions = my_pipeline.predict(X_val)\n",
+    "\n",
+    "uplift_30 = uplift_at_k(y_val, uplift_predictions, treat_val, strategy='overall')\n",
+    "print(f'uplift@30%: {uplift_30:.4f}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/pipeline_usage_RU.ipynb b/notebooks/pipeline_usage_RU.ipynb
new file mode 100644
index 0000000..5e306b5
--- /dev/null
+++ b/notebooks/pipeline_usage_RU.ipynb
@@ -0,0 +1,463 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Пример использование подходов из sklift.models в sklearn.pipeline\n",
+    "\n",
+    "<br>\n",
+    "<center>\n",
+    "    <a href=\"https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb\">\n",
+    "        <img src=\"https://colab.research.google.com/assets/colab-badge.svg\">\n",
+    "    </a>\n",
+    "    <br>\n",
+    "    <b><a href=\"https://github.com/maks-sh/scikit-uplift/\">SCIKIT-UPLIFT REPO</a> | </b>\n",
+    "    <b><a href=\"https://scikit-uplift.readthedocs.io/en/latest/\">SCIKIT-UPLIFT DOCS</a></b>\n",
+    "    <br>\n",
+    "    <b><a href=\"https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb\">ENGLISH VERSION</a></b>\n",
+    "\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "В данном ноутбуке рассмотрим простой пример применения одного из подходов прогнозирования uplift  в [sklearn.pipeline](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.pipeline).\n",
+    "\n",
+    "Данные для примера взяты из [MineThatData E-Mail Analytics And Data Mining Challenge dataset by Kevin Hillstrom](https://blog.minethatdata.com/2008/03/minethatdata-e-mail-analytics-and-data.html). Этот набор данных содержит 64 000 клиентов, которые в последний раз совершали покупки в течение двенадцати месяцев. Среди клиентов была проведена рекламная кампания с помощью email рассылки:\n",
+    "\n",
+    "* 1/3 клиентов были выбраны случайным образом для получения электронного письма, рекламирующего мужскую продукцию;\n",
+    "* 1/3 клиентов были выбраны случайным образом для получения электронного письма, рекламирующего женскую продукцию;\n",
+    "* С оставшейся 1/3 коммуникацию не проводили.\n",
+    "\n",
+    "Для каждого клиента из выборки замерили факт перехода по ссылке в письме, факт совершения покупки и сумму трат за две недели, следущими после получения письма.\n",
+    "\n",
+    "Полное описание датасета можной найти по [ссылке](https://blog.minethatdata.com/2008/03/minethatdata-e-mail-analytics-and-data.html).\n",
+    "\n",
+    "Установим необходимые библиотеки:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:23:06.767010Z",
+     "start_time": "2020-04-28T09:23:06.762871Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install scikit-uplift==0.1.1 xgboost==1.0.2 category_encoders==2.1.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-26T14:28:36.188277Z",
+     "start_time": "2020-04-26T14:28:36.106561Z"
+    }
+   },
+   "source": [
+    "Загрузим данные:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:23:10.820274Z",
+     "start_time": "2020-04-28T09:23:06.776057Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('./content/Hilstorm.csv', <http.client.HTTPMessage at 0x117e7b0b8>)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import urllib.request\n",
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "csv_path = '/content/Hilstorm.csv'\n",
+    "url = 'http://www.minethatdata.com/Kevin_Hillstrom_MineThatData_E-MailAnalytics_DataMiningChallenge_2008.03.20.csv'\n",
+    "urllib.request.urlretrieve(url, csv_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Для простоты примера оставим только два сегмента пользователей:\n",
+    "* тем, кому рассылалась по электронной почте рекламная кампания с участием женских товаров;\n",
+    "* тем, кому не рассылалась рекламная кампания.\n",
+    "\n",
+    "В качестве целевой переменной будем использовать переменную `visit`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:23:11.319111Z",
+     "start_time": "2020-04-28T09:23:10.823302Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер датасета до обработки: (64000, 12)\n",
+      "Размер датасета после обработки: (42693, 10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>recency</th>\n",
+       "      <th>history_segment</th>\n",
+       "      <th>history</th>\n",
+       "      <th>mens</th>\n",
+       "      <th>womens</th>\n",
+       "      <th>zip_code</th>\n",
+       "      <th>newbie</th>\n",
+       "      <th>channel</th>\n",
+       "      <th>visit</th>\n",
+       "      <th>treatment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>2) $100 - $200</td>\n",
+       "      <td>142.44</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Surburban</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Phone</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3) $200 - $350</td>\n",
+       "      <td>329.08</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2) $100 - $200</td>\n",
+       "      <td>180.65</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Surburban</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1) $0 - $100</td>\n",
+       "      <td>45.34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2) $100 - $200</td>\n",
+       "      <td>134.83</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Surburban</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Phone</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   recency history_segment  history  mens  womens   zip_code  newbie channel  \\\n",
+       "0       10  2) $100 - $200   142.44     1       0  Surburban       0   Phone   \n",
+       "1        6  3) $200 - $350   329.08     1       1      Rural       1     Web   \n",
+       "2        7  2) $100 - $200   180.65     0       1  Surburban       1     Web   \n",
+       "4        2    1) $0 - $100    45.34     1       0      Urban       0     Web   \n",
+       "5        6  2) $100 - $200   134.83     0       1  Surburban       0   Phone   \n",
+       "\n",
+       "   visit  treatment  \n",
+       "0      0          1  \n",
+       "1      0          0  \n",
+       "2      0          1  \n",
+       "4      0          1  \n",
+       "5      1          1  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd; pd.set_option('display.max_columns', None)\n",
+    "\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "dataset = pd.read_csv(csv_path)\n",
+    "print(f'Размер датасета до обработки: {dataset.shape}')\n",
+    "dataset = dataset[dataset['segment']!='Mens E-Mail']\n",
+    "dataset.loc[:, 'treatment'] = dataset['segment'].map({\n",
+    "    'Womens E-Mail': 1,\n",
+    "    'No E-Mail': 0\n",
+    "})\n",
+    "\n",
+    "dataset = dataset.drop(['segment', 'conversion', 'spend'], axis=1)\n",
+    "print(f'Размер датасета после обработки: {dataset.shape}')\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Разобъем все данные на обучающую и валидационную выборку:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:23:11.919143Z",
+     "start_time": "2020-04-28T09:23:11.324058Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "\n",
+    "Xyt_tr, Xyt_val = train_test_split(dataset, test_size=0.5, random_state=42)\n",
+    "\n",
+    "X_tr = Xyt_tr.drop(['visit', 'treatment'], axis=1)\n",
+    "y_tr = Xyt_tr['visit']\n",
+    "treat_tr = Xyt_tr['treatment']\n",
+    "\n",
+    "X_val = Xyt_val.drop(['visit', 'treatment'], axis=1)\n",
+    "y_val = Xyt_val['visit']\n",
+    "treat_val = Xyt_val['treatment']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select categorical features:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:23:11.936024Z",
+     "start_time": "2020-04-28T09:23:11.921716Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['history_segment', 'zip_code', 'channel']\n"
+     ]
+    }
+   ],
+   "source": [
+    "cat_cols = X_tr.select_dtypes(include='object').columns.tolist()\n",
+    "print(cat_cols)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Создадим нужные объекты и объединим их в pipieline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:23:12.019176Z",
+     "start_time": "2020-04-28T09:23:11.939728Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "from category_encoders import CatBoostEncoder\n",
+    "from sklift.models import ClassTransformation\n",
+    "from xgboost import XGBClassifier\n",
+    "\n",
+    "\n",
+    "encoder = CatBoostEncoder(cols=cat_cols)\n",
+    "estimator = XGBClassifier(max_depth=2, random_state=42)\n",
+    "ct = ClassTransformation(estimator=estimator)\n",
+    "\n",
+    "my_pipeline = Pipeline([\n",
+    "    ('encoder', encoder),\n",
+    "    ('model', ct)\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-26T18:02:52.236917Z",
+     "start_time": "2020-04-26T18:02:52.110138Z"
+    }
+   },
+   "source": [
+    "<b>Обучать pipeline будем как обычно, но колонку treatment добавим как параметр шага model: `model__treatment`.</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:23:13.244343Z",
+     "start_time": "2020-04-28T09:23:12.021559Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/pipeline.py:354: UserWarning: It is recommended to use this approach on treatment balanced data. Current sample size is unbalanced.\n",
+      "  self._final_estimator.fit(Xt, y, **fit_params)\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_pipeline = my_pipeline.fit(\n",
+    "    X=X_tr,\n",
+    "    y=y_tr,\n",
+    "    model__treatment=treat_tr\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Предскажем uplift и посчитаем uplift@30%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-28T09:23:13.383577Z",
+     "start_time": "2020-04-28T09:23:13.246513Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "uplift@30%: 0.0661\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklift.metrics import uplift_at_k\n",
+    "\n",
+    "\n",
+    "uplift_predictions = my_pipeline.predict(X_val)\n",
+    "\n",
+    "uplift_30 = uplift_at_k(y_val, uplift_predictions, treat_val, strategy='overall')\n",
+    "print(f'uplift@30%: {uplift_30:.4f}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/sklift/__init__.py b/sklift/__init__.py
index b794fd4..df9144c 100644
--- a/sklift/__init__.py
+++ b/sklift/__init__.py
@@ -1 +1 @@
-__version__ = '0.1.0'
+__version__ = '0.1.1'
diff --git a/sklift/metrics/__init__.py b/sklift/metrics/__init__.py
index de53391..097ba43 100644
--- a/sklift/metrics/__init__.py
+++ b/sklift/metrics/__init__.py
@@ -1,3 +1,9 @@
-from .metrics import uplift_curve, auuc, qini_curve, auqc, uplift_at_k, treatment_balance_curve
+from .metrics import (
+    uplift_curve, auuc, qini_curve, auqc, uplift_at_k, response_rate_by_percentile, treatment_balance_curve,
+    uplift_auc_score, qini_auc_score
+)
 
-__all__ = [uplift_curve, auuc, qini_curve, auqc, uplift_at_k, treatment_balance_curve]
\ No newline at end of file
+__all__ = [
+    uplift_curve, auuc, qini_curve, auqc, uplift_at_k, response_rate_by_percentile, treatment_balance_curve,
+    uplift_auc_score, qini_auc_score
+]
diff --git a/sklift/metrics/metrics.py b/sklift/metrics/metrics.py
index c54b2c9..efbf185 100644
--- a/sklift/metrics/metrics.py
+++ b/sklift/metrics/metrics.py
@@ -12,7 +12,7 @@ def uplift_curve(y_true, uplift, treatment):
     area under the Uplift Curve, see :func:`uplift_auc_score`.
 
     Args:
-        y_true (1d array-like): Ground truth (correct) labels.
+        y_true (1d array-like): Correct (true) target values.
         uplift (1d array-like): Predicted uplift, as returned by a model.
         treatment (1d array-like): Treatment labels.
 
@@ -48,7 +48,7 @@ def uplift_curve(y_true, uplift, treatment):
     num_ctrl = num_all - num_trmnt
     y_ctrl = stable_cumsum(y_true_ctrl)[threshold_indices]
 
-    curve_values = (np.divide(y_trmnt, num_trmnt, out=np.zeros_like(y_trmnt), where=num_trmnt != 0) -\
+    curve_values = (np.divide(y_trmnt, num_trmnt, out=np.zeros_like(y_trmnt), where=num_trmnt != 0) -
                     np.divide(y_ctrl, num_ctrl, out=np.zeros_like(y_ctrl), where=num_ctrl != 0)) * num_all
 
     if num_all.size == 0 or curve_values[0] != 0 or num_all[0] != 0:
@@ -67,7 +67,7 @@ def qini_curve(y_true, uplift, treatment):
     area under the Qini Curve, see :func:`qini_auc_score`.
 
     Args:
-        y_true (1d array-like): Ground truth (correct) labels.
+        y_true (1d array-like): Correct (true) target values.
         uplift (1d array-like): Predicted uplift, as returned by a model.
         treatment (1d array-like): Treatment labels.
 
@@ -120,7 +120,7 @@ def uplift_auc_score(y_true, uplift, treatment):
     """Compute Area Under the Uplift Curve from prediction scores.
 
     Args:
-        y_true (1d array-like): Ground truth (correct) labels.
+        y_true (1d array-like): Correct (true) target values.
         uplift (1d array-like): Predicted uplift, as returned by a model.
         treatment (1d array-like): Treatment labels.
 
@@ -136,7 +136,7 @@ def auuc(y_true, uplift, treatment):
     """Compute Area Under the Uplift Curve from prediction scores.
 
     Args:
-        y_true (1d array-like): Ground truth (correct) labels.
+        y_true (1d array-like): Correct (true) target values.
         uplift (1d array-like): Predicted uplift, as returned by a model.
         treatment (1d array-like): Treatment labels.
 
@@ -160,7 +160,7 @@ def qini_auc_score(y_true, uplift, treatment):
     """Compute Area Under the Qini Curve (aka Qini coefficient) from prediction scores.
 
     Args:
-        y_true (1d array-like): Ground truth (correct) labels.
+        y_true (1d array-like): Correct (true) target values.
         uplift (1d array-like): Predicted uplift, as returned by a model.
         treatment (1d array-like): Treatment labels.
 
@@ -176,7 +176,7 @@ def auqc(y_true, uplift, treatment):
     """Compute Area Under the Qini Curve (aka Qini coefficient) from prediction scores.
 
     Args:
-        y_true (1d array-like): Ground truth (correct) labels.
+        y_true (1d array-like): Correct (true) target values.
         uplift (1d array-like): Predicted uplift, as returned by a model.
         treatment (1d array-like): Treatment labels.
 
@@ -200,12 +200,12 @@ def uplift_at_k(y_true, uplift, treatment, strategy, k=0.3):
     """Compute uplift at first k percentage of the total sample.
 
     Args:
-        y_true (1d array-like): Ground truth (correct) labels.
+        y_true (1d array-like): Correct (true) target values.
         uplift (1d array-like): Predicted uplift, as returned by a model.
         treatment (1d array-like): Treatment labels.
         k (float or int): If float, should be between 0.0 and 1.0 and represent the proportion of the dataset
             to include in the computation of uplift. If int, represents the absolute number of samples.
-        strategy (string, ['overall', 'by_group']): Determines the calculating strategy. Defaults to 'first'.
+        strategy (string, ['overall', 'by_group']): Determines the calculating strategy.
 
             * ``'overall'``:
                 The first step is taking the first k observations of all test data ordered by uplift prediction
@@ -237,7 +237,7 @@ def uplift_at_k(y_true, uplift, treatment, strategy, k=0.3):
                          )
 
     n_samples = len(y_true)
-    order = np.argsort(uplift)[::-1]
+    order = np.argsort(uplift, kind='mergesort')[::-1]
     _, treatment_counts = np.unique(treatment, return_counts=True)
     n_samples_ctrl = treatment_counts[0]
     n_samples_trmnt = treatment_counts[1]
@@ -247,8 +247,8 @@ def uplift_at_k(y_true, uplift, treatment, strategy, k=0.3):
     if (k_type == 'i' and (k >= n_samples or k <= 0)
        or k_type == 'f' and (k <= 0 or k >= 1)):
         raise ValueError(f'k={k} should be either positive and smaller'
-                         ' than the number of samples {n_samples} or a float in the '
-                         '(0, 1) range')
+                         f' than the number of samples {n_samples} or a float in the '
+                         f'(0, 1) range')
 
     if k_type not in ('i', 'f'):
         raise ValueError(f'Invalid value for k: {k_type}')
@@ -289,6 +289,86 @@ def uplift_at_k(y_true, uplift, treatment, strategy, k=0.3):
     return score_trmnt - score_ctrl
 
 
+def response_rate_by_percentile(y_true, uplift, treatment, group, strategy, bins=10):
+    """Compute response rate (target mean in the control or treatment group) at each percentile.
+    
+    Args:
+        y_true (1d array-like): Correct (true) target values.
+        uplift (1d array-like): Predicted uplift, as returned by a model.
+        treatment (1d array-like): Treatment labels.
+        group (string, ['treatment', 'control']): Group type for computing response rate: treatment or control.
+            * ``'treatment'``:
+                Values equal 1 in the treatment column. 
+            * ``'control'``:
+                Values equal 0 in the treatment column. 
+        strategy (string, ['overall', 'by_group']): Determines the calculating strategy. 
+            * ``'overall'``:
+                The first step is taking the first k observations of all test data ordered by uplift prediction
+                (overall both groups - control and treatment) and conversions in treatment and control groups
+                calculated only on them. Then the difference between these conversions is calculated.
+            * ``'by_group'``:
+                Separately calculates conversions in top k observations in each group (control and treatment)
+                sorted by uplift predictions. Then the difference between these conversions is calculated
+        bins (int): Determines the number of bins (and relative percentile) in the test data.  
+        
+    Returns:
+        array: Response rate at each percentile for control or treatment group
+        array: Variance of the response rate at each percentile 
+    """
+    
+    group_types = ['treatment', 'control']
+    strategy_methods = ['overall', 'by_group']
+    
+    n_samples = len(y_true)
+    check_consistent_length(y_true, uplift, treatment)
+    
+    if group not in group_types:
+        raise ValueError(f'Response rate supports only group types in {group_types},'
+                         f' got {group}.') 
+
+    if strategy not in strategy_methods:
+        raise ValueError(f'Response rate supports only calculating methods in {strategy_methods},'
+                         f' got {strategy}.')
+    
+    if not isinstance(bins, int) or bins <= 0:
+        raise ValueError(f'bins should be positive integer.'
+                         f' Invalid value bins: {bins}')
+          
+    if bins >= n_samples:
+        raise ValueError(f'Number of bins = {bins} should be smaller than the length of y_true {n_samples}')
+    
+    if bins == 1:
+        warnings.warn(f'You will get the only one bin of {n_samples} samples'
+                      f' which is the length of y_true.' 
+                      f'\nPlease consider using uplift_at_k function instead',
+                      UserWarning)
+    
+    y_true, uplift, treatment = np.array(y_true), np.array(uplift), np.array(treatment)
+    order = np.argsort(uplift, kind='mergesort')[::-1]
+    
+    if group == 'treatment':
+        trmnt_flag = 1
+    else:  # group == 'control'
+        trmnt_flag = 0
+    
+    if strategy == 'overall':
+        y_true_bin = np.array_split(y_true[order], bins)
+        trmnt_bin = np.array_split(treatment[order], bins)
+        
+        group_size = np.array([len(y[trmnt == trmnt_flag]) for y, trmnt in zip(y_true_bin, trmnt_bin)])
+        response_rate = np.array([np.mean(y[trmnt == trmnt_flag]) for y, trmnt in zip(y_true_bin, trmnt_bin)])
+
+    else:  # strategy == 'by_group'
+        y_bin = np.array_split(y_true[order][treatment[order] == trmnt_flag], bins)
+        
+        group_size = np.array([len(y) for y in y_bin])
+        response_rate = np.array([np.mean(y) for y in y_bin])
+
+    variance = np.multiply(response_rate, np.divide((1 - response_rate), group_size))
+                            
+    return response_rate, variance          
+
+
 def treatment_balance_curve(uplift, treatment, winsize):
     """Compute the treatment balance curve: proportion of treatment group in the ordered predictions.
 
diff --git a/sklift/viz/__init__.py b/sklift/viz/__init__.py
index f00ab0c..10601ab 100644
--- a/sklift/viz/__init__.py
+++ b/sklift/viz/__init__.py
@@ -1,3 +1,3 @@
-from .base import plot_uplift_preds, plot_uplift_qini_curves
+from .base import plot_uplift_preds, plot_uplift_qini_curves, plot_uplift_by_percentile, plot_treatment_balance_curve
 
-__all__ = [plot_uplift_preds, plot_uplift_qini_curves]
+__all__ = [plot_uplift_preds, plot_uplift_qini_curves, plot_uplift_by_percentile, plot_treatment_balance_curve]
diff --git a/sklift/viz/base.py b/sklift/viz/base.py
index b1307cc..2d2bb75 100644
--- a/sklift/viz/base.py
+++ b/sklift/viz/base.py
@@ -1,6 +1,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
-from ..metrics import uplift_curve, auuc, qini_curve, auqc, treatment_balance_curve
+from ..metrics import uplift_curve, auuc, qini_curve, auqc, response_rate_by_percentile, treatment_balance_curve
 
 
 def plot_uplift_preds(trmnt_preds, ctrl_preds, log=False, bins=100):
@@ -89,12 +89,12 @@ def plot_uplift_qini_curves(y_true, uplift, treatment, random=True, perfect=Fals
         axes[0].plot(x_up_perfect, y_up_perfect, label='Perfect', color='red')
         axes[1].plot(x_qi_perfect, y_qi_perfect, label='Perfect', color='red')
 
-    axes[0].legend()
+    axes[0].legend(loc='upper left')
     axes[0].set_title(f'Uplift curve: AUUC={auuc(y_true, uplift, treatment):.2f}')
     axes[0].set_xlabel('Number targeted')
     axes[0].set_ylabel('Relative gain: treatment - control')
 
-    axes[1].legend()
+    axes[1].legend(loc='upper left')
     axes[1].set_title(f'Qini curve: AUQC={auqc(y_true, uplift, treatment):.2f}')
     axes[1].set_xlabel('Number targeted')
     axes[1].set_ylabel('Number of incremental outcome')
@@ -102,6 +102,82 @@ def plot_uplift_qini_curves(y_true, uplift, treatment, random=True, perfect=Fals
     return axes
 
 
+def plot_uplift_by_percentile(y_true, uplift, treatment, strategy, bins=10):
+    """Plot Uplift score at each percentile, 
+    Treatment response rate (target mean in the treatment group) 
+    and Control response rate (target mean in the control group) at each percentile.
+    
+    Args:
+        y_true (1d array-like): Correct (true) target values.
+        uplift (1d array-like): Predicted uplift, as returned by a model.
+        treatment (1d array-like): Treatment labels.
+        strategy (string, ['overall', 'by_group']): Determines the calculating strategy. Defaults to 'first'.
+            * ``'overall'``:
+                The first step is taking the first k observations of all test data ordered by uplift prediction
+                (overall both groups - control and treatment) and conversions in treatment and control groups
+                calculated only on them. Then the difference between these conversions is calculated.
+            * ``'by_group'``:
+                Separately calculates conversions in top k observations in each group (control and treatment)
+                sorted by uplift predictions. Then the difference between these conversions is calculated
+        bins (int): Determines the number of bins (and relative percentile) in the test data. 
+        
+    Returns:
+        Object that stores computed values.
+    """
+    
+    strategy_methods = ['overall', 'by_group']
+    
+    n_samples = len(y_true)
+    check_consistent_length(y_true, uplift, treatment)
+    
+    if strategy not in strategy_methods:
+        raise ValueError(f'Response rate supports only calculating methods in {strategy_methods},'
+                         f' got {strategy}.')
+    
+    if not isinstance(bins, int) or bins <= 0:
+        raise ValueError(f'bins should be positive integer.'
+                         f' Invalid value bins: {bins}')
+          
+    if bins >= n_samples:
+        raise ValueError(f'Number of bins = {bins} should be smaller than the length of y_true {n_samples}')
+    
+    if bins == 1:
+        warnings.warn(f'You will get the only one bin of {n_samples} samples'
+                      f' which is the length of y_true.' 
+                      f'\nPlease consider using uplift_at_k function instead',
+                      UserWarning)
+        
+    rspns_rate_trmnt, var_trmnt = response_rate_by_percentile(y_true, uplift,
+                                                              treatment, group='treatment',
+                                                              strategy=strategy, bins=bins)
+    
+    rspns_rate_ctrl, var_ctrl = response_rate_by_percentile(y_true, uplift,
+                                                            treatment, group='control',
+                                                            strategy=strategy, bins=bins)
+
+    uplift_score, uplift_variance = np.subtract(rspns_rate_trmnt, rspns_rate_ctrl), np.add(var_trmnt, var_ctrl)
+    
+    percentiles = [p * 100 / bins for p in range(1, bins + 1)]
+    
+    _, axes = plt.subplots(ncols=1, nrows=1, figsize=(8, 6))
+    
+    axes.errorbar(percentiles, uplift_score, yerr=np.sqrt(uplift_variance), 
+                  linewidth=2, color='red', label='uplift')
+    axes.errorbar(percentiles, rspns_rate_trmnt, yerr=np.sqrt(var_trmnt),
+                  linewidth=2, color='forestgreen', label='treatment\nresponse rate')
+    axes.errorbar(percentiles, rspns_rate_ctrl, yerr=np.sqrt(var_ctrl),
+                  linewidth=2, color='orange', label='control\nresponse rate')
+    axes.fill_between(percentiles, rspns_rate_ctrl, rspns_rate_trmnt, alpha=0.1, color='red')
+    
+    axes.set_xticks(percentiles)
+    axes.legend(loc='upper right')
+    axes.set_title('Uplift by percentile')
+    axes.set_xlabel('Percentile')
+    axes.set_ylabel('Uplift = treatment response rate - control response rate')
+    
+    return axes
+
+
 def plot_treatment_balance_curve(uplift, treatment, random=True, winsize=0.1):
     """Plot Treatment Balance curve.
 
@@ -132,6 +208,6 @@ def plot_treatment_balance_curve(uplift, treatment, random=True, winsize=0.1):
     axes.legend()
     axes.set_title('Treatment balance curve')
     axes.set_xlabel('Percentage targeted')
-    axes.set_ylabel('Balance: treatment / (treatment + control')
+    axes.set_ylabel('Balance: treatment / (treatment + control)')
 
     return axes