From ebaabc68d84f5420fa5896d0f4b86c474e3e7dfb Mon Sep 17 00:00:00 2001
From: Mohammed Esamaldin
 <110843414+MohammedEsamaldin@users.noreply.github.com>
Date: Wed, 31 Aug 2022 20:20:28 +0200
Subject: [PATCH 1/6] Created using Colaboratory

---
 A/B _calssical_testing.ipynb | 927 +++++++++++++++++++++++++++++++++++
 1 file changed, 927 insertions(+)
 create mode 100644 A/B _calssical_testing.ipynb

diff --git a/A/B _calssical_testing.ipynb b/A/B _calssical_testing.ipynb
new file mode 100644
index 0000000..f95d606
--- /dev/null
+++ b/A/B _calssical_testing.ipynb	
@@ -0,0 +1,927 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyMc8Jq7eLNbfrTaw8Gui8go",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/MohammedEsamaldin/abtest-mlops/blob/main/A/B%20_calssical_testing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 373
+        },
+        "id": "0bNRpfA1-1Ai",
+        "outputId": "9acdcf95-2548-4b37-c080-0568e440b679"
+      },
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "ModuleNotFoundError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-38-8b40a05ce625>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'../scripts/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mDistributionPlots\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDistributionPlots\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilterwarnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'DistributionPlots'",
+            "",
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
+          ],
+          "errorDetails": {
+            "actions": [
+              {
+                "action": "open_url",
+                "actionText": "Open Examples",
+                "url": "/notebooks/snippets/importing_libraries.ipynb"
+              }
+            ]
+          }
+        }
+      ],
+      "source": [
+        "# import important packages\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline\n",
+        "import seaborn as sns\n",
+        "import sys\n",
+        "sys.path.insert(0,'../scripts/')\n",
+        "#from DistributionPlots import DistributionPlots\n",
+        "import warnings\n",
+        "warnings.filterwarnings(action=\"ignore\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n",
+        "\n",
+        "df = pd.read_csv('/content/drive/MyDrive/AdSmartABdata.csv',na_values= ['?',None])\n",
+        "df.isnull().sum().head(10)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "r8oZJu5u_Osu",
+        "outputId": "c6210fd4-5dc8-429f-e315-e03971b4097a"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "auction_id     0\n",
+              "experiment     0\n",
+              "date           0\n",
+              "hour           0\n",
+              "device_make    0\n",
+              "platform_os    0\n",
+              "browser        0\n",
+              "yes            0\n",
+              "no             0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Dropping observations where there was no response to the presented questionaire\n",
+        "dp = df.query('yes==0 and no==0')\n",
+        "df = df.drop(dp.index)\n",
+        "df"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "44K4LwyLCUdf",
+        "outputId": "d11429d3-817e-4fa2-d5ff-ddb86c29445d"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                auction_id experiment        date  hour  \\\n",
+              "2     0016d14a-ae18-4a02-a204-6ba53b52f2ed    exposed  2020-07-05     2   \n",
+              "16    008aafdf-deef-4482-8fec-d98e3da054da    exposed  2020-07-04    16   \n",
+              "20    00a1384a-5118-4d1b-925b-6cdada50318d    exposed  2020-07-06     8   \n",
+              "23    00b6fadb-10bd-49e3-a778-290da82f7a8d    control  2020-07-08     4   \n",
+              "27    00ebf4a8-060f-4b99-93ac-c62724399483    control  2020-07-03    15   \n",
+              "...                                    ...        ...         ...   ...   \n",
+              "8059  ffa08ff9-a132-4051-aef5-01a9c79367bc    exposed  2020-07-05    21   \n",
+              "8063  ffb176df-ecd2-45d3-b05f-05b173a093a7    exposed  2020-07-04     1   \n",
+              "8064  ffb79718-6f25-4896-b6b3-e58b80a6e147    control  2020-07-09     7   \n",
+              "8069  ffca1153-c182-4f32-9e90-2a6008417497    control  2020-07-10    16   \n",
+              "8071  ffdfdc09-48c7-4bfb-80f8-ec1eb633602b    exposed  2020-07-03     4   \n",
+              "\n",
+              "             device_make  platform_os                browser  yes  no  \n",
+              "2                  E5823            6  Chrome Mobile WebView    0   1  \n",
+              "16    Generic Smartphone            6          Chrome Mobile    1   0  \n",
+              "20    Generic Smartphone            6          Chrome Mobile    0   1  \n",
+              "23      Samsung SM-A202F            6               Facebook    1   0  \n",
+              "27    Generic Smartphone            6          Chrome Mobile    0   1  \n",
+              "...                  ...          ...                    ...  ...  ..  \n",
+              "8059  Generic Smartphone            6          Chrome Mobile    1   0  \n",
+              "8063  Generic Smartphone            6          Chrome Mobile    1   0  \n",
+              "8064  Generic Smartphone            6          Chrome Mobile    0   1  \n",
+              "8069  Generic Smartphone            6          Chrome Mobile    0   1  \n",
+              "8071  Generic Smartphone            6          Chrome Mobile    0   1  \n",
+              "\n",
+              "[1243 rows x 9 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9ff3a04b-4eb0-4580-b75f-b3371a0131e5\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>auction_id</th>\n",
+              "      <th>experiment</th>\n",
+              "      <th>date</th>\n",
+              "      <th>hour</th>\n",
+              "      <th>device_make</th>\n",
+              "      <th>platform_os</th>\n",
+              "      <th>browser</th>\n",
+              "      <th>yes</th>\n",
+              "      <th>no</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0016d14a-ae18-4a02-a204-6ba53b52f2ed</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-05</td>\n",
+              "      <td>2</td>\n",
+              "      <td>E5823</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile WebView</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>008aafdf-deef-4482-8fec-d98e3da054da</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-04</td>\n",
+              "      <td>16</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>00a1384a-5118-4d1b-925b-6cdada50318d</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-06</td>\n",
+              "      <td>8</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>00b6fadb-10bd-49e3-a778-290da82f7a8d</td>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-08</td>\n",
+              "      <td>4</td>\n",
+              "      <td>Samsung SM-A202F</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Facebook</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>00ebf4a8-060f-4b99-93ac-c62724399483</td>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-03</td>\n",
+              "      <td>15</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8059</th>\n",
+              "      <td>ffa08ff9-a132-4051-aef5-01a9c79367bc</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-05</td>\n",
+              "      <td>21</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8063</th>\n",
+              "      <td>ffb176df-ecd2-45d3-b05f-05b173a093a7</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-04</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8064</th>\n",
+              "      <td>ffb79718-6f25-4896-b6b3-e58b80a6e147</td>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-09</td>\n",
+              "      <td>7</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8069</th>\n",
+              "      <td>ffca1153-c182-4f32-9e90-2a6008417497</td>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-10</td>\n",
+              "      <td>16</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8071</th>\n",
+              "      <td>ffdfdc09-48c7-4bfb-80f8-ec1eb633602b</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-03</td>\n",
+              "      <td>4</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1243 rows × 9 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ff3a04b-4eb0-4580-b75f-b3371a0131e5')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-9ff3a04b-4eb0-4580-b75f-b3371a0131e5 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-9ff3a04b-4eb0-4580-b75f-b3371a0131e5');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Counting the people that are aware of the brand in both groups\n",
+        "plt.figure(figsize=(9,7))\n",
+        "axis = sns.countplot(x='experiment', hue='yes', data=df)\n",
+        "axis.set_title('brand awarnes in both groups')\n",
+        "plt.legend(['No', 'Yes'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 475
+        },
+        "id": "7VRXevU_ChPP",
+        "outputId": "1cdf2f09-769a-46b9-dc60-babe7f1ed1bf"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7fab824081d0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 648x504 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Top 5 Devices\n",
+        "t5_device = df['device_make'].value_counts().nlargest(5)\n",
+        "fig1,ax1 = plt.subplots(figsize=(10,10))\n",
+        "ax1.pie(t5_device.values, labels=t5_device.index, autopct='%1.1f%%')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 835
+        },
+        "id": "uH9ySp6CDwuG",
+        "outputId": "f61e7f71-0434-4383-c4bd-62ea371b0cfe"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "([<matplotlib.patches.Wedge at 0x7fab81e77c90>,\n",
+              "  <matplotlib.patches.Wedge at 0x7fab81e824d0>,\n",
+              "  <matplotlib.patches.Wedge at 0x7fab81e82d10>,\n",
+              "  <matplotlib.patches.Wedge at 0x7fab81e0d4d0>,\n",
+              "  <matplotlib.patches.Wedge at 0x7fab81e0df10>],\n",
+              " [Text(-0.9908102930560425, 0.47780222181798127, 'Generic Smartphone'),\n",
+              "  Text(0.8195688791579974, -0.7336939772927841, 'Samsung SM-G960F'),\n",
+              "  Text(0.9995816413521881, -0.45916940476436985, 'Samsung SM-G950F'),\n",
+              "  Text(1.0741603235269193, -0.2370223604653036, 'Samsung SM-G973F'),\n",
+              "  Text(1.0975024172535426, -0.0740840341951703, 'Samsung SM-A202F')],\n",
+              " [Text(-0.5404419780305686, 0.26061939371889886, '85.7%'),\n",
+              "  Text(0.4470375704498167, -0.40019671488697306, '5.4%'),\n",
+              "  Text(0.5452263498284662, -0.2504560389623835, '4.2%'),\n",
+              "  Text(0.5859056310146832, -0.12928492389016558, '2.6%'),\n",
+              "  Text(0.5986376821382959, -0.04040947319736561, '2.1%')])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x720 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Count plot of browsrs\n",
+        "plt.figure(figsize=(9,7))\n",
+        "axis = sns.countplot(x='browser', data=df)\n",
+        "axis.set_xticklabels(axis.get_xticklabels(), rotation=60, ha='right')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 700
+        },
+        "id": "GhSgwslLDKp7",
+        "outputId": "f59be957-0497-4060-ca94-d4550b057c67"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[Text(0, 0, 'Chrome Mobile WebView'),\n",
+              " Text(0, 0, 'Chrome Mobile'),\n",
+              " Text(0, 0, 'Facebook'),\n",
+              " Text(0, 0, 'Samsung Internet'),\n",
+              " Text(0, 0, 'Mobile Safari'),\n",
+              " Text(0, 0, 'Chrome'),\n",
+              " Text(0, 0, 'Mobile Safari UI/WKWebView'),\n",
+              " Text(0, 0, 'Chrome Mobile iOS')]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 648x504 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# Distribution of Hour\n",
+        "plt.figure(figsize=(9,7))\n",
+        "axis = sns.distplot(df['hour'],bins=24, color='green')\n",
+        "plt.title('Distribution fo Hour')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 475
+        },
+        "id": "b-fJ1yuIDO46",
+        "outputId": "116b8436-5850-4945-9048-f4a17e976ed9"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Distribution fo Hour')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 648x504 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#  aware people vs the total people\n",
+        "expo_yes = df.query('experiment == \"exposed\"').yes\n",
+        "expo_count = len(expo_yes)\n",
+        "expo_yes_count = expo_yes.sum(axis=0)\n",
+        "\n",
+        "cont_yes = df.query('experiment == \"control\"').yes\n",
+        "cont_count = len(cont_yes)\n",
+        "cont_yes_count = cont_yes.sum(axis=0)\n",
+        "print('Control yes count:',expo_yes_count)\n",
+        "print('Total Control:',cont_count)\n",
+        "print('Exposed yes count:',expo_yes_count)\n",
+        "print('Total Exposed:',expo_count)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9g8x9_jiEjJj",
+        "outputId": "7a795a87-f00f-439e-9cef-6ec8a8dcba10"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Control yes count: 308\n",
+            "Total Control: 586\n",
+            "Exposed yes count: 308\n",
+            "Total Exposed: 657\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import statsmodels.stats.api as sms \n",
+        "baseline_rate = cont_yes_count / cont_count\n",
+        "practical_significance = 0.01  # minimum required effect size\n",
+        "confidence_level = 0.05 \n",
+        "sensitivity = 0.8 \n",
+        "\n",
+        "effect_size = sms.proportion_effectsize(baseline_rate, baseline_rate + practical_significance)\n",
+        "sample_size = sms.NormalIndPower().solve_power(effect_size = effect_size, power = sensitivity, \n",
+        "                                               alpha = confidence_level, ratio=1)\n",
+        "print(\"Required sample size: \", round(sample_size), \" per group\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "79Y-Kf7GFXLz",
+        "outputId": "707a2eba-22fa-42a1-b502-15387cfc0b41"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Required sample size:  38932  per group\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from scipy.stats import binom\n",
+        "# Calculate the Conversions for each group\n",
+        "cv_rate_control, cv_rate_exposed = cont_yes_count / cont_count, expo_yes_count / expo_count\n",
+        "range = np.arange(200, 370)\n",
+        "cv_prob_control = binom(cont_count, cv_rate_control).pmf(range)\n",
+        "cv_prob_exposed = binom(expo_count, cv_rate_exposed).pmf(range)\n",
+        "fig, ax = plt.subplots(figsize=(12,10))\n",
+        "plt.bar(range, cv_prob_control, label=\"Control\",color='green')\n",
+        "plt.bar(range, cv_prob_exposed, label=\"Exposed\",color='pink')\n",
+        "plt.legend()\n",
+        "plt.xlabel(\"Conversions\"); plt.ylabel(\"Probability\");"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 606
+        },
+        "id": "3Z2FveHFFjXI",
+        "outputId": "11a43877-8b9c-4c9e-dbd0-57b5951879fc"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 864x720 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "std_control = np.sqrt(cv_rate_control * (1 - cv_rate_control) / cont_count)\n",
+        "std_exposed = np.sqrt(cv_rate_exposed * (1 - cv_rate_exposed) / expo_count)\n",
+        "\n",
+        "SE_control = np.sqrt(cv_rate_control * (1-cv_rate_control)) / np.sqrt(cont_count)\n",
+        "SE_exposed = np.sqrt(cv_rate_exposed * (1-cv_rate_exposed)) / np.sqrt(expo_count)\n",
+        "print(std_control, SE_control)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7wx_ssjHGJGK",
+        "outputId": "195214f3-cfb5-46f7-fb15-36e1bf5143f0"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.02055339057798332 0.02055339057798332\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from scipy.stats import norm\n",
+        "conversion_rate = np.linspace(0, 0.9, 200)\n",
+        "prob_a = norm(cv_rate_control, SE_control).pdf(conversion_rate)\n",
+        "prob_b = norm(cv_rate_exposed, SE_exposed).pdf(conversion_rate)\n",
+        "plt.figure(figsize=(10,5))\n",
+        "plt.plot(conversion_rate, prob_a, label=\"Control\",color = 'green')\n",
+        "plt.plot(conversion_rate, prob_b, label=\"Exposed\",color = 'pink')\n",
+        "plt.legend(frameon=False)\n",
+        "plt.xlabel(\"Conversion rate\"); plt.ylabel(\"Probability\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 351
+        },
+        "id": "QtQQ0LmzGT1e",
+        "outputId": "da9bd4e4-f624-4130-89c5-fe7580c488a5"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0, 0.5, 'Probability')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x360 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import scipy.stats as scs\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(12, 6))\n",
+        "x = np.linspace(.35, .6, 1000)\n",
+        "\n",
+        "yC = scs.norm(cv_rate_control, SE_control).pdf(x)\n",
+        "ax.plot(x, yC, label='Control')\n",
+        "ax.axvline(x=cv_rate_control, c='red', alpha=0.5, linestyle='--')\n",
+        "\n",
+        "yE = scs.norm(cv_rate_exposed, SE_exposed).pdf(x)\n",
+        "ax.plot(x, yE, label='Exposed')\n",
+        "ax.axvline(x=cv_rate_exposed, c='blue', alpha=0.5, linestyle='--')\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.xlabel('Converted Proportion') # conversion rate\n",
+        "plt.ylabel('PDF') # probability density function\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 388
+        },
+        "id": "P49EJyIjGpmE",
+        "outputId": "5eda82de-3318-4086-db44-4a4a6bb4e053"
+      },
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 864x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#calculate variance of sum\n",
+        "var_cont = np.sqrt(cv_rate_control * (1 - cv_rate_control) / cont_count)\n",
+        "var_exp = np.sqrt(cv_rate_exposed * (1 - cv_rate_exposed) / expo_count)\n",
+        "var_cont, var_exp"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ix8Oyi7DGt9c",
+        "outputId": "3b722af8-2e01-45d0-ad35-4e5534045c2a"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(0.02055339057798332, 0.019468837373132736)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "diff = cv_rate_exposed - cv_rate_control\n",
+        "z_score = (diff) / np.sqrt(var_cont**2 + var_exp**2)\n",
+        "print(f\"zscore is {z_score:0.5f}, with p-value {scs.norm().sf(z_score):0.5f}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hfISnCM9Hxri",
+        "outputId": "92d98361-671f-45d3-c5fa-2a0ad4fa2e0d"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "zscore is 0.64590, with p-value 0.25917\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "hypo_plots.zplot(area=0.95)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 165
+        },
+        "id": "wXyfSx9ZH3eP",
+        "outputId": "3f73c26b-0be7-4f58-8e16-07abaa47f1c0"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-37-edabb2b79c97>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhypo_plots\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marea\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.95\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m: name 'hypo_plots' is not defined"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 027b65917acde3b7b0cb3420887943328107615c Mon Sep 17 00:00:00 2001
From: Mohammed Esamaldin
 <110843414+MohammedEsamaldin@users.noreply.github.com>
Date: Wed, 31 Aug 2022 20:21:40 +0200
Subject: [PATCH 2/6] Delete A directory

---
 A/B _calssical_testing.ipynb | 927 -----------------------------------
 1 file changed, 927 deletions(-)
 delete mode 100644 A/B _calssical_testing.ipynb

diff --git a/A/B _calssical_testing.ipynb b/A/B _calssical_testing.ipynb
deleted file mode 100644
index f95d606..0000000
--- a/A/B _calssical_testing.ipynb	
+++ /dev/null
@@ -1,927 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "authorship_tag": "ABX9TyMc8Jq7eLNbfrTaw8Gui8go",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/MohammedEsamaldin/abtest-mlops/blob/main/A/B%20_calssical_testing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 38,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 373
-        },
-        "id": "0bNRpfA1-1Ai",
-        "outputId": "9acdcf95-2548-4b37-c080-0568e440b679"
-      },
-      "outputs": [
-        {
-          "output_type": "error",
-          "ename": "ModuleNotFoundError",
-          "evalue": "ignored",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-38-8b40a05ce625>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'../scripts/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mDistributionPlots\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDistributionPlots\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilterwarnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'DistributionPlots'",
-            "",
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
-          ],
-          "errorDetails": {
-            "actions": [
-              {
-                "action": "open_url",
-                "actionText": "Open Examples",
-                "url": "/notebooks/snippets/importing_libraries.ipynb"
-              }
-            ]
-          }
-        }
-      ],
-      "source": [
-        "# import important packages\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "%matplotlib inline\n",
-        "import seaborn as sns\n",
-        "import sys\n",
-        "sys.path.insert(0,'../scripts/')\n",
-        "#from DistributionPlots import DistributionPlots\n",
-        "import warnings\n",
-        "warnings.filterwarnings(action=\"ignore\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from google.colab import drive\n",
-        "drive.mount('/content/drive')\n",
-        "\n",
-        "df = pd.read_csv('/content/drive/MyDrive/AdSmartABdata.csv',na_values= ['?',None])\n",
-        "df.isnull().sum().head(10)\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "r8oZJu5u_Osu",
-        "outputId": "c6210fd4-5dc8-429f-e315-e03971b4097a"
-      },
-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Mounted at /content/drive\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "auction_id     0\n",
-              "experiment     0\n",
-              "date           0\n",
-              "hour           0\n",
-              "device_make    0\n",
-              "platform_os    0\n",
-              "browser        0\n",
-              "yes            0\n",
-              "no             0\n",
-              "dtype: int64"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 3
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Dropping observations where there was no response to the presented questionaire\n",
-        "dp = df.query('yes==0 and no==0')\n",
-        "df = df.drop(dp.index)\n",
-        "df"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 424
-        },
-        "id": "44K4LwyLCUdf",
-        "outputId": "d11429d3-817e-4fa2-d5ff-ddb86c29445d"
-      },
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                                auction_id experiment        date  hour  \\\n",
-              "2     0016d14a-ae18-4a02-a204-6ba53b52f2ed    exposed  2020-07-05     2   \n",
-              "16    008aafdf-deef-4482-8fec-d98e3da054da    exposed  2020-07-04    16   \n",
-              "20    00a1384a-5118-4d1b-925b-6cdada50318d    exposed  2020-07-06     8   \n",
-              "23    00b6fadb-10bd-49e3-a778-290da82f7a8d    control  2020-07-08     4   \n",
-              "27    00ebf4a8-060f-4b99-93ac-c62724399483    control  2020-07-03    15   \n",
-              "...                                    ...        ...         ...   ...   \n",
-              "8059  ffa08ff9-a132-4051-aef5-01a9c79367bc    exposed  2020-07-05    21   \n",
-              "8063  ffb176df-ecd2-45d3-b05f-05b173a093a7    exposed  2020-07-04     1   \n",
-              "8064  ffb79718-6f25-4896-b6b3-e58b80a6e147    control  2020-07-09     7   \n",
-              "8069  ffca1153-c182-4f32-9e90-2a6008417497    control  2020-07-10    16   \n",
-              "8071  ffdfdc09-48c7-4bfb-80f8-ec1eb633602b    exposed  2020-07-03     4   \n",
-              "\n",
-              "             device_make  platform_os                browser  yes  no  \n",
-              "2                  E5823            6  Chrome Mobile WebView    0   1  \n",
-              "16    Generic Smartphone            6          Chrome Mobile    1   0  \n",
-              "20    Generic Smartphone            6          Chrome Mobile    0   1  \n",
-              "23      Samsung SM-A202F            6               Facebook    1   0  \n",
-              "27    Generic Smartphone            6          Chrome Mobile    0   1  \n",
-              "...                  ...          ...                    ...  ...  ..  \n",
-              "8059  Generic Smartphone            6          Chrome Mobile    1   0  \n",
-              "8063  Generic Smartphone            6          Chrome Mobile    1   0  \n",
-              "8064  Generic Smartphone            6          Chrome Mobile    0   1  \n",
-              "8069  Generic Smartphone            6          Chrome Mobile    0   1  \n",
-              "8071  Generic Smartphone            6          Chrome Mobile    0   1  \n",
-              "\n",
-              "[1243 rows x 9 columns]"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-9ff3a04b-4eb0-4580-b75f-b3371a0131e5\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <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>auction_id</th>\n",
-              "      <th>experiment</th>\n",
-              "      <th>date</th>\n",
-              "      <th>hour</th>\n",
-              "      <th>device_make</th>\n",
-              "      <th>platform_os</th>\n",
-              "      <th>browser</th>\n",
-              "      <th>yes</th>\n",
-              "      <th>no</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>0016d14a-ae18-4a02-a204-6ba53b52f2ed</td>\n",
-              "      <td>exposed</td>\n",
-              "      <td>2020-07-05</td>\n",
-              "      <td>2</td>\n",
-              "      <td>E5823</td>\n",
-              "      <td>6</td>\n",
-              "      <td>Chrome Mobile WebView</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>16</th>\n",
-              "      <td>008aafdf-deef-4482-8fec-d98e3da054da</td>\n",
-              "      <td>exposed</td>\n",
-              "      <td>2020-07-04</td>\n",
-              "      <td>16</td>\n",
-              "      <td>Generic Smartphone</td>\n",
-              "      <td>6</td>\n",
-              "      <td>Chrome Mobile</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>20</th>\n",
-              "      <td>00a1384a-5118-4d1b-925b-6cdada50318d</td>\n",
-              "      <td>exposed</td>\n",
-              "      <td>2020-07-06</td>\n",
-              "      <td>8</td>\n",
-              "      <td>Generic Smartphone</td>\n",
-              "      <td>6</td>\n",
-              "      <td>Chrome Mobile</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>23</th>\n",
-              "      <td>00b6fadb-10bd-49e3-a778-290da82f7a8d</td>\n",
-              "      <td>control</td>\n",
-              "      <td>2020-07-08</td>\n",
-              "      <td>4</td>\n",
-              "      <td>Samsung SM-A202F</td>\n",
-              "      <td>6</td>\n",
-              "      <td>Facebook</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>27</th>\n",
-              "      <td>00ebf4a8-060f-4b99-93ac-c62724399483</td>\n",
-              "      <td>control</td>\n",
-              "      <td>2020-07-03</td>\n",
-              "      <td>15</td>\n",
-              "      <td>Generic Smartphone</td>\n",
-              "      <td>6</td>\n",
-              "      <td>Chrome Mobile</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8059</th>\n",
-              "      <td>ffa08ff9-a132-4051-aef5-01a9c79367bc</td>\n",
-              "      <td>exposed</td>\n",
-              "      <td>2020-07-05</td>\n",
-              "      <td>21</td>\n",
-              "      <td>Generic Smartphone</td>\n",
-              "      <td>6</td>\n",
-              "      <td>Chrome Mobile</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8063</th>\n",
-              "      <td>ffb176df-ecd2-45d3-b05f-05b173a093a7</td>\n",
-              "      <td>exposed</td>\n",
-              "      <td>2020-07-04</td>\n",
-              "      <td>1</td>\n",
-              "      <td>Generic Smartphone</td>\n",
-              "      <td>6</td>\n",
-              "      <td>Chrome Mobile</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8064</th>\n",
-              "      <td>ffb79718-6f25-4896-b6b3-e58b80a6e147</td>\n",
-              "      <td>control</td>\n",
-              "      <td>2020-07-09</td>\n",
-              "      <td>7</td>\n",
-              "      <td>Generic Smartphone</td>\n",
-              "      <td>6</td>\n",
-              "      <td>Chrome Mobile</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8069</th>\n",
-              "      <td>ffca1153-c182-4f32-9e90-2a6008417497</td>\n",
-              "      <td>control</td>\n",
-              "      <td>2020-07-10</td>\n",
-              "      <td>16</td>\n",
-              "      <td>Generic Smartphone</td>\n",
-              "      <td>6</td>\n",
-              "      <td>Chrome Mobile</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8071</th>\n",
-              "      <td>ffdfdc09-48c7-4bfb-80f8-ec1eb633602b</td>\n",
-              "      <td>exposed</td>\n",
-              "      <td>2020-07-03</td>\n",
-              "      <td>4</td>\n",
-              "      <td>Generic Smartphone</td>\n",
-              "      <td>6</td>\n",
-              "      <td>Chrome Mobile</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>1243 rows × 9 columns</p>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ff3a04b-4eb0-4580-b75f-b3371a0131e5')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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-              "      </button>\n",
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-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
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-              "      width: 32px;\n",
-              "    }\n",
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-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-9ff3a04b-4eb0-4580-b75f-b3371a0131e5 button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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-              "          const element = document.querySelector('#df-9ff3a04b-4eb0-4580-b75f-b3371a0131e5');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ]
-          },
-          "metadata": {},
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Counting the people that are aware of the brand in both groups\n",
-        "plt.figure(figsize=(9,7))\n",
-        "axis = sns.countplot(x='experiment', hue='yes', data=df)\n",
-        "axis.set_title('brand awarnes in both groups')\n",
-        "plt.legend(['No', 'Yes'])"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 475
-        },
-        "id": "7VRXevU_ChPP",
-        "outputId": "1cdf2f09-769a-46b9-dc60-babe7f1ed1bf"
-      },
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.legend.Legend at 0x7fab824081d0>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 9
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 648x504 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Top 5 Devices\n",
-        "t5_device = df['device_make'].value_counts().nlargest(5)\n",
-        "fig1,ax1 = plt.subplots(figsize=(10,10))\n",
-        "ax1.pie(t5_device.values, labels=t5_device.index, autopct='%1.1f%%')"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 835
-        },
-        "id": "uH9ySp6CDwuG",
-        "outputId": "f61e7f71-0434-4383-c4bd-62ea371b0cfe"
-      },
-      "execution_count": 17,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "([<matplotlib.patches.Wedge at 0x7fab81e77c90>,\n",
-              "  <matplotlib.patches.Wedge at 0x7fab81e824d0>,\n",
-              "  <matplotlib.patches.Wedge at 0x7fab81e82d10>,\n",
-              "  <matplotlib.patches.Wedge at 0x7fab81e0d4d0>,\n",
-              "  <matplotlib.patches.Wedge at 0x7fab81e0df10>],\n",
-              " [Text(-0.9908102930560425, 0.47780222181798127, 'Generic Smartphone'),\n",
-              "  Text(0.8195688791579974, -0.7336939772927841, 'Samsung SM-G960F'),\n",
-              "  Text(0.9995816413521881, -0.45916940476436985, 'Samsung SM-G950F'),\n",
-              "  Text(1.0741603235269193, -0.2370223604653036, 'Samsung SM-G973F'),\n",
-              "  Text(1.0975024172535426, -0.0740840341951703, 'Samsung SM-A202F')],\n",
-              " [Text(-0.5404419780305686, 0.26061939371889886, '85.7%'),\n",
-              "  Text(0.4470375704498167, -0.40019671488697306, '5.4%'),\n",
-              "  Text(0.5452263498284662, -0.2504560389623835, '4.2%'),\n",
-              "  Text(0.5859056310146832, -0.12928492389016558, '2.6%'),\n",
-              "  Text(0.5986376821382959, -0.04040947319736561, '2.1%')])"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 17
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 720x720 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Count plot of browsrs\n",
-        "plt.figure(figsize=(9,7))\n",
-        "axis = sns.countplot(x='browser', data=df)\n",
-        "axis.set_xticklabels(axis.get_xticklabels(), rotation=60, ha='right')"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 700
-        },
-        "id": "GhSgwslLDKp7",
-        "outputId": "f59be957-0497-4060-ca94-d4550b057c67"
-      },
-      "execution_count": 10,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "[Text(0, 0, 'Chrome Mobile WebView'),\n",
-              " Text(0, 0, 'Chrome Mobile'),\n",
-              " Text(0, 0, 'Facebook'),\n",
-              " Text(0, 0, 'Samsung Internet'),\n",
-              " Text(0, 0, 'Mobile Safari'),\n",
-              " Text(0, 0, 'Chrome'),\n",
-              " Text(0, 0, 'Mobile Safari UI/WKWebView'),\n",
-              " Text(0, 0, 'Chrome Mobile iOS')]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 10
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 648x504 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "# Distribution of Hour\n",
-        "plt.figure(figsize=(9,7))\n",
-        "axis = sns.distplot(df['hour'],bins=24, color='green')\n",
-        "plt.title('Distribution fo Hour')"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 475
-        },
-        "id": "b-fJ1yuIDO46",
-        "outputId": "116b8436-5850-4945-9048-f4a17e976ed9"
-      },
-      "execution_count": 16,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Text(0.5, 1.0, 'Distribution fo Hour')"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 16
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 648x504 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#  aware people vs the total people\n",
-        "expo_yes = df.query('experiment == \"exposed\"').yes\n",
-        "expo_count = len(expo_yes)\n",
-        "expo_yes_count = expo_yes.sum(axis=0)\n",
-        "\n",
-        "cont_yes = df.query('experiment == \"control\"').yes\n",
-        "cont_count = len(cont_yes)\n",
-        "cont_yes_count = cont_yes.sum(axis=0)\n",
-        "print('Control yes count:',expo_yes_count)\n",
-        "print('Total Control:',cont_count)\n",
-        "print('Exposed yes count:',expo_yes_count)\n",
-        "print('Total Exposed:',expo_count)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "9g8x9_jiEjJj",
-        "outputId": "7a795a87-f00f-439e-9cef-6ec8a8dcba10"
-      },
-      "execution_count": 19,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Control yes count: 308\n",
-            "Total Control: 586\n",
-            "Exposed yes count: 308\n",
-            "Total Exposed: 657\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import statsmodels.stats.api as sms \n",
-        "baseline_rate = cont_yes_count / cont_count\n",
-        "practical_significance = 0.01  # minimum required effect size\n",
-        "confidence_level = 0.05 \n",
-        "sensitivity = 0.8 \n",
-        "\n",
-        "effect_size = sms.proportion_effectsize(baseline_rate, baseline_rate + practical_significance)\n",
-        "sample_size = sms.NormalIndPower().solve_power(effect_size = effect_size, power = sensitivity, \n",
-        "                                               alpha = confidence_level, ratio=1)\n",
-        "print(\"Required sample size: \", round(sample_size), \" per group\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "79Y-Kf7GFXLz",
-        "outputId": "707a2eba-22fa-42a1-b502-15387cfc0b41"
-      },
-      "execution_count": 20,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Required sample size:  38932  per group\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from scipy.stats import binom\n",
-        "# Calculate the Conversions for each group\n",
-        "cv_rate_control, cv_rate_exposed = cont_yes_count / cont_count, expo_yes_count / expo_count\n",
-        "range = np.arange(200, 370)\n",
-        "cv_prob_control = binom(cont_count, cv_rate_control).pmf(range)\n",
-        "cv_prob_exposed = binom(expo_count, cv_rate_exposed).pmf(range)\n",
-        "fig, ax = plt.subplots(figsize=(12,10))\n",
-        "plt.bar(range, cv_prob_control, label=\"Control\",color='green')\n",
-        "plt.bar(range, cv_prob_exposed, label=\"Exposed\",color='pink')\n",
-        "plt.legend()\n",
-        "plt.xlabel(\"Conversions\"); plt.ylabel(\"Probability\");"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 606
-        },
-        "id": "3Z2FveHFFjXI",
-        "outputId": "11a43877-8b9c-4c9e-dbd0-57b5951879fc"
-      },
-      "execution_count": 25,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 864x720 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "std_control = np.sqrt(cv_rate_control * (1 - cv_rate_control) / cont_count)\n",
-        "std_exposed = np.sqrt(cv_rate_exposed * (1 - cv_rate_exposed) / expo_count)\n",
-        "\n",
-        "SE_control = np.sqrt(cv_rate_control * (1-cv_rate_control)) / np.sqrt(cont_count)\n",
-        "SE_exposed = np.sqrt(cv_rate_exposed * (1-cv_rate_exposed)) / np.sqrt(expo_count)\n",
-        "print(std_control, SE_control)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7wx_ssjHGJGK",
-        "outputId": "195214f3-cfb5-46f7-fb15-36e1bf5143f0"
-      },
-      "execution_count": 26,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "0.02055339057798332 0.02055339057798332\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from scipy.stats import norm\n",
-        "conversion_rate = np.linspace(0, 0.9, 200)\n",
-        "prob_a = norm(cv_rate_control, SE_control).pdf(conversion_rate)\n",
-        "prob_b = norm(cv_rate_exposed, SE_exposed).pdf(conversion_rate)\n",
-        "plt.figure(figsize=(10,5))\n",
-        "plt.plot(conversion_rate, prob_a, label=\"Control\",color = 'green')\n",
-        "plt.plot(conversion_rate, prob_b, label=\"Exposed\",color = 'pink')\n",
-        "plt.legend(frameon=False)\n",
-        "plt.xlabel(\"Conversion rate\"); plt.ylabel(\"Probability\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 351
-        },
-        "id": "QtQQ0LmzGT1e",
-        "outputId": "da9bd4e4-f624-4130-89c5-fe7580c488a5"
-      },
-      "execution_count": 28,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Text(0, 0.5, 'Probability')"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 28
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 720x360 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import scipy.stats as scs\n",
-        "\n",
-        "fig, ax = plt.subplots(figsize=(12, 6))\n",
-        "x = np.linspace(.35, .6, 1000)\n",
-        "\n",
-        "yC = scs.norm(cv_rate_control, SE_control).pdf(x)\n",
-        "ax.plot(x, yC, label='Control')\n",
-        "ax.axvline(x=cv_rate_control, c='red', alpha=0.5, linestyle='--')\n",
-        "\n",
-        "yE = scs.norm(cv_rate_exposed, SE_exposed).pdf(x)\n",
-        "ax.plot(x, yE, label='Exposed')\n",
-        "ax.axvline(x=cv_rate_exposed, c='blue', alpha=0.5, linestyle='--')\n",
-        "\n",
-        "plt.legend()\n",
-        "plt.xlabel('Converted Proportion') # conversion rate\n",
-        "plt.ylabel('PDF') # probability density function\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 388
-        },
-        "id": "P49EJyIjGpmE",
-        "outputId": "5eda82de-3318-4086-db44-4a4a6bb4e053"
-      },
-      "execution_count": 29,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 864x432 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#calculate variance of sum\n",
-        "var_cont = np.sqrt(cv_rate_control * (1 - cv_rate_control) / cont_count)\n",
-        "var_exp = np.sqrt(cv_rate_exposed * (1 - cv_rate_exposed) / expo_count)\n",
-        "var_cont, var_exp"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ix8Oyi7DGt9c",
-        "outputId": "3b722af8-2e01-45d0-ad35-4e5534045c2a"
-      },
-      "execution_count": 31,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(0.02055339057798332, 0.019468837373132736)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 31
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "diff = cv_rate_exposed - cv_rate_control\n",
-        "z_score = (diff) / np.sqrt(var_cont**2 + var_exp**2)\n",
-        "print(f\"zscore is {z_score:0.5f}, with p-value {scs.norm().sf(z_score):0.5f}\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "hfISnCM9Hxri",
-        "outputId": "92d98361-671f-45d3-c5fa-2a0ad4fa2e0d"
-      },
-      "execution_count": 36,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "zscore is 0.64590, with p-value 0.25917\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "hypo_plots.zplot(area=0.95)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 165
-        },
-        "id": "wXyfSx9ZH3eP",
-        "outputId": "3f73c26b-0be7-4f58-8e16-07abaa47f1c0"
-      },
-      "execution_count": 37,
-      "outputs": [
-        {
-          "output_type": "error",
-          "ename": "NameError",
-          "evalue": "ignored",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-37-edabb2b79c97>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhypo_plots\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marea\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.95\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-            "\u001b[0;31mNameError\u001b[0m: name 'hypo_plots' is not defined"
-          ]
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file

From 4e4a75a98a4b619140afedeb3374b6eef64cbc69 Mon Sep 17 00:00:00 2001
From: Mohammed Esamaldin
 <110843414+MohammedEsamaldin@users.noreply.github.com>
Date: Wed, 31 Aug 2022 20:22:42 +0200
Subject: [PATCH 3/6] Created using Colaboratory

---
 EDA_Notebooks/AB_classical_testing.ipynb | 927 +++++++++++++++++++++++
 1 file changed, 927 insertions(+)
 create mode 100644 EDA_Notebooks/AB_classical_testing.ipynb

diff --git a/EDA_Notebooks/AB_classical_testing.ipynb b/EDA_Notebooks/AB_classical_testing.ipynb
new file mode 100644
index 0000000..f535c28
--- /dev/null
+++ b/EDA_Notebooks/AB_classical_testing.ipynb
@@ -0,0 +1,927 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyMc8Jq7eLNbfrTaw8Gui8go",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/MohammedEsamaldin/abtest-mlops/blob/main/EDA_Notebooks/AB_classical_testing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 373
+        },
+        "id": "0bNRpfA1-1Ai",
+        "outputId": "9acdcf95-2548-4b37-c080-0568e440b679"
+      },
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "ModuleNotFoundError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-38-8b40a05ce625>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'../scripts/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mDistributionPlots\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDistributionPlots\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilterwarnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'DistributionPlots'",
+            "",
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
+          ],
+          "errorDetails": {
+            "actions": [
+              {
+                "action": "open_url",
+                "actionText": "Open Examples",
+                "url": "/notebooks/snippets/importing_libraries.ipynb"
+              }
+            ]
+          }
+        }
+      ],
+      "source": [
+        "# import important packages\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline\n",
+        "import seaborn as sns\n",
+        "import sys\n",
+        "sys.path.insert(0,'../scripts/')\n",
+        "#from DistributionPlots import DistributionPlots\n",
+        "import warnings\n",
+        "warnings.filterwarnings(action=\"ignore\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n",
+        "\n",
+        "df = pd.read_csv('/content/drive/MyDrive/AdSmartABdata.csv',na_values= ['?',None])\n",
+        "df.isnull().sum().head(10)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "r8oZJu5u_Osu",
+        "outputId": "c6210fd4-5dc8-429f-e315-e03971b4097a"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "auction_id     0\n",
+              "experiment     0\n",
+              "date           0\n",
+              "hour           0\n",
+              "device_make    0\n",
+              "platform_os    0\n",
+              "browser        0\n",
+              "yes            0\n",
+              "no             0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Dropping observations where there was no response to the presented questionaire\n",
+        "dp = df.query('yes==0 and no==0')\n",
+        "df = df.drop(dp.index)\n",
+        "df"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "44K4LwyLCUdf",
+        "outputId": "d11429d3-817e-4fa2-d5ff-ddb86c29445d"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                auction_id experiment        date  hour  \\\n",
+              "2     0016d14a-ae18-4a02-a204-6ba53b52f2ed    exposed  2020-07-05     2   \n",
+              "16    008aafdf-deef-4482-8fec-d98e3da054da    exposed  2020-07-04    16   \n",
+              "20    00a1384a-5118-4d1b-925b-6cdada50318d    exposed  2020-07-06     8   \n",
+              "23    00b6fadb-10bd-49e3-a778-290da82f7a8d    control  2020-07-08     4   \n",
+              "27    00ebf4a8-060f-4b99-93ac-c62724399483    control  2020-07-03    15   \n",
+              "...                                    ...        ...         ...   ...   \n",
+              "8059  ffa08ff9-a132-4051-aef5-01a9c79367bc    exposed  2020-07-05    21   \n",
+              "8063  ffb176df-ecd2-45d3-b05f-05b173a093a7    exposed  2020-07-04     1   \n",
+              "8064  ffb79718-6f25-4896-b6b3-e58b80a6e147    control  2020-07-09     7   \n",
+              "8069  ffca1153-c182-4f32-9e90-2a6008417497    control  2020-07-10    16   \n",
+              "8071  ffdfdc09-48c7-4bfb-80f8-ec1eb633602b    exposed  2020-07-03     4   \n",
+              "\n",
+              "             device_make  platform_os                browser  yes  no  \n",
+              "2                  E5823            6  Chrome Mobile WebView    0   1  \n",
+              "16    Generic Smartphone            6          Chrome Mobile    1   0  \n",
+              "20    Generic Smartphone            6          Chrome Mobile    0   1  \n",
+              "23      Samsung SM-A202F            6               Facebook    1   0  \n",
+              "27    Generic Smartphone            6          Chrome Mobile    0   1  \n",
+              "...                  ...          ...                    ...  ...  ..  \n",
+              "8059  Generic Smartphone            6          Chrome Mobile    1   0  \n",
+              "8063  Generic Smartphone            6          Chrome Mobile    1   0  \n",
+              "8064  Generic Smartphone            6          Chrome Mobile    0   1  \n",
+              "8069  Generic Smartphone            6          Chrome Mobile    0   1  \n",
+              "8071  Generic Smartphone            6          Chrome Mobile    0   1  \n",
+              "\n",
+              "[1243 rows x 9 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9ff3a04b-4eb0-4580-b75f-b3371a0131e5\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>auction_id</th>\n",
+              "      <th>experiment</th>\n",
+              "      <th>date</th>\n",
+              "      <th>hour</th>\n",
+              "      <th>device_make</th>\n",
+              "      <th>platform_os</th>\n",
+              "      <th>browser</th>\n",
+              "      <th>yes</th>\n",
+              "      <th>no</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0016d14a-ae18-4a02-a204-6ba53b52f2ed</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-05</td>\n",
+              "      <td>2</td>\n",
+              "      <td>E5823</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile WebView</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>008aafdf-deef-4482-8fec-d98e3da054da</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-04</td>\n",
+              "      <td>16</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>00a1384a-5118-4d1b-925b-6cdada50318d</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-06</td>\n",
+              "      <td>8</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>00b6fadb-10bd-49e3-a778-290da82f7a8d</td>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-08</td>\n",
+              "      <td>4</td>\n",
+              "      <td>Samsung SM-A202F</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Facebook</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>00ebf4a8-060f-4b99-93ac-c62724399483</td>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-03</td>\n",
+              "      <td>15</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8059</th>\n",
+              "      <td>ffa08ff9-a132-4051-aef5-01a9c79367bc</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-05</td>\n",
+              "      <td>21</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8063</th>\n",
+              "      <td>ffb176df-ecd2-45d3-b05f-05b173a093a7</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-04</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8064</th>\n",
+              "      <td>ffb79718-6f25-4896-b6b3-e58b80a6e147</td>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-09</td>\n",
+              "      <td>7</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8069</th>\n",
+              "      <td>ffca1153-c182-4f32-9e90-2a6008417497</td>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-10</td>\n",
+              "      <td>16</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8071</th>\n",
+              "      <td>ffdfdc09-48c7-4bfb-80f8-ec1eb633602b</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-03</td>\n",
+              "      <td>4</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1243 rows × 9 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ff3a04b-4eb0-4580-b75f-b3371a0131e5')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-9ff3a04b-4eb0-4580-b75f-b3371a0131e5 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-9ff3a04b-4eb0-4580-b75f-b3371a0131e5');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Counting the people that are aware of the brand in both groups\n",
+        "plt.figure(figsize=(9,7))\n",
+        "axis = sns.countplot(x='experiment', hue='yes', data=df)\n",
+        "axis.set_title('brand awarnes in both groups')\n",
+        "plt.legend(['No', 'Yes'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 475
+        },
+        "id": "7VRXevU_ChPP",
+        "outputId": "1cdf2f09-769a-46b9-dc60-babe7f1ed1bf"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7fab824081d0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 648x504 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Top 5 Devices\n",
+        "t5_device = df['device_make'].value_counts().nlargest(5)\n",
+        "fig1,ax1 = plt.subplots(figsize=(10,10))\n",
+        "ax1.pie(t5_device.values, labels=t5_device.index, autopct='%1.1f%%')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 835
+        },
+        "id": "uH9ySp6CDwuG",
+        "outputId": "f61e7f71-0434-4383-c4bd-62ea371b0cfe"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "([<matplotlib.patches.Wedge at 0x7fab81e77c90>,\n",
+              "  <matplotlib.patches.Wedge at 0x7fab81e824d0>,\n",
+              "  <matplotlib.patches.Wedge at 0x7fab81e82d10>,\n",
+              "  <matplotlib.patches.Wedge at 0x7fab81e0d4d0>,\n",
+              "  <matplotlib.patches.Wedge at 0x7fab81e0df10>],\n",
+              " [Text(-0.9908102930560425, 0.47780222181798127, 'Generic Smartphone'),\n",
+              "  Text(0.8195688791579974, -0.7336939772927841, 'Samsung SM-G960F'),\n",
+              "  Text(0.9995816413521881, -0.45916940476436985, 'Samsung SM-G950F'),\n",
+              "  Text(1.0741603235269193, -0.2370223604653036, 'Samsung SM-G973F'),\n",
+              "  Text(1.0975024172535426, -0.0740840341951703, 'Samsung SM-A202F')],\n",
+              " [Text(-0.5404419780305686, 0.26061939371889886, '85.7%'),\n",
+              "  Text(0.4470375704498167, -0.40019671488697306, '5.4%'),\n",
+              "  Text(0.5452263498284662, -0.2504560389623835, '4.2%'),\n",
+              "  Text(0.5859056310146832, -0.12928492389016558, '2.6%'),\n",
+              "  Text(0.5986376821382959, -0.04040947319736561, '2.1%')])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x720 with 1 Axes>"
+            ],
+            "image/png": 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9h5DcpuIpe+NaYJLvECIisse+H8QTuruceKPiKXskiCdGo4XiRUSiahCpJfBEvFDxlD31E6DEdwgREdlrl4V3mxNJO3PO+c4gERHEE2cCf/OdQ0RE9tks4Oj6utqk7yCSWzTiKd0Srtn5P75ziIhIj5gEXOY7hOQeFU/pri8B+/sOISIiPeamIJ4Y5DuE5BYVT9mtIJ6oIbVYvIiIZI9K4Pu+Q0hu2W3xNLNqM/u9mb1tZrPN7Hkz+7feDmZml5tZt9cbM7Nvmtk8M5tjZq+Y2dG9lOsb3dyv3swG9kYGD34AlPsOISIiPe58XWgk6bTL4mlmBvwFeMo5N9o5NxH4JDCsN0OZWb5zboZz7jfd3P9Y4GzgCOfcocCpwJIezmRmFgO6VTyzRRBPnAR8yncOERHpFUZqcEEkLXY34jkVaHPOzdj6gHNusXPuZwBmlmdm3zezmeFI42Xh41PM7Akz+5OZLTCze8ISi5lNNLMnw9HTR8xsSPj4E2b2EzObBVxjZjeY2VfCbfuZ2f8zs1fN7CUzG7NNziHAWudca5hxrXNuefjcejP7bjgKOsvMjghf9y0zuzzcp9zMHguPPdfMPhI+HpjZG2b2G+A1UnfrKQmPdU+4fev7mx++39Iuua7qcsyDwmP2N7O/hH9eL5jZoeHjN5jZ3eGfw9tmdvXWg5jZv5vZv8LXvc3M8rr597tPgnjC0AVFIiLZbnIQT3zYdwjJDbsrnuOAl3ax/WJgk3PuSOBI4FIzGxVuOxz4InAwMBo43swKgJ8B54ajp3cD/93leIXOuUnOuR9u8zr3AD93zh0GHAes2Gb7o8BwM3vTzG41s8nbbH/XOTcBeBr4FXAucAzvL4TeAvybc+4I4GTgh1uLMqkLam51zo1zzl0IbHHOTXDOfSbcfmC4fSywGfhCl9ddGx7zF8BXwse+A7wcjsx+A+g6qnsQMA04CrjezArMbCxwHnB8+B46gc+QHp8GJqTptURExJ+bg3gi33cIyX579I/MzH4OnEBqFPRI4HTgUDM7N9ylL6mi1gb8yzm3NHzeK0AAbAQOAf4R9ro8Plgi79vBa1YAQ51zfwZwzrVsu49zrtHMJgInkiqO95lZ3Dn3q3CXB8OPc4Fy51wD0GBmrWZWCTQBN5nZSUASGApUh89Z7Jx7YRd/LEucc8+Gv/8dcDXvT1vcH36cDXws/P0JwDlh7sfNbICZ9Qm3JcJR21YzWx1mOAWYCMwM/8xKgNW7yNMjwuWT/qu3X0dERDLCQcClpAZKRHrN7ornPMKSBOCcmx5eMDMrfMiAq5xzj3R9kplNAVq7PNQZvpYB85xzx+7k9Zq6H/2DnHOdwBPAE2Y2F/gcqdFNumRJbpMrGeb6DFAFTHTOtZtZPVDczUzbrsDf9fOtr7X1/e/Ozv7Mfu2c+3o3nt+TLgNG7XYvERHJFjcE8cTv6utqG3wHkey1u6n2x4FiM7uiy2Ndz2F8BLginELHzA4ws7JdHO8NoCq8GIhwKnncrgKEo5NLzeyj4XOKtjmPEjM70My6rjE5AVi8m/fWVV9gdVg6TwZG7mLf9q3vNzRi6/shNTX9zG5e62nCqfKwoK91zm3exf6PAeea2aDwOf3NbFf59lkQT5Sj5ZNERHLNIOBrvkNIdttl8XSp+2l+FJhsZu+Y2b+AX/P+P8w7gdeBl8zsNeA2djGy55xrI3V+5c1m9irwCqlzNnfnfOBqM5sDPAcM3mZ7OfBrM3s93Odg4IZuHHere4BJ4UjpZ4EFu9j3dmCOmd0Tfv4GMN3M5gP92P00xQ3AxDBnHamR2Z1yzr1OqgQ+Gj7nH6QupupNXyb1BUhERHLLtUE8MdR3CMleulf7PjCzAHjIOXeI5yg9JognqoC3gArfWURExItf1NfVfmH3u4nsOd25SLb1bVQ6RURy2cVBPNGr63VL7lLx3AfOufosG+0cSeqiIhERyV2F5NjNUiR9VDylq6+T+oIjIiK5TaOe0itUPAWA8AvMhb5ziIhIRigkNRgh0qNUPGWrr6HRThEReZ9GPaXHqXgKQTwxGLjEdw4REckoRWjUU3qYiqcAXMv7d2oSERHZSqOe0qNUPHNcEE/0Ay73nUNERDJSEanBCZEeoeIp09G6nSIisnMXB/FEH98hJDuoeOawIJ4oAa7xnUNERDJaH+BS3yEkO6h45rbzgYG+Q4iISMa7Oogn8n2HkOhT8cxtV/sOICIikTACONd3CIk+Fc8cFcQTpwDjfOcQEZHI0EVGss9UPHOXRjtFRGRPHBnEEyf6DiHRpuKZg4J4YjRwtu8cIiISOV/2HUCiTcUzN12J/u5FRGTPfSgcvBDZKyofOSaIJ8qBi3znEBGRSIqhpZVkH6h45p7PAX19hxARkci6MIgnCnyHkGhS8cw9V/gOICIikVYNfMh3CIkmFc8cEsQTR6MllEREZN993ncAiSYVz9yicztFRKQnnBbEE4HvEBI9Kp45Irwv+yd95xARkawQAy7xHUKiR8Uzd5wD9PEdQkREssaFun+77CkVz9yhaXYREelJNUCt7xASLSqeOSCIJ0YBU3znEBGRrHOB7wASLSqeueFCwHyHEBGRrHNWEE/08x1CokPFM8sF8YSRWjReRESkpxUCH/cdQqJDxTP7HQeM8B1CRESy1md8B5DoUPHMfp/wHUBERLLaiUE8Mdx3CIkGFc8sFk6zn+s7h4iIZDVDgxzSTSqe2e14UstdiIiI9CbdoES6RcUzu+knUBERSYdJQTwxxncIyXwqnlkqiCdipO5WJCIikg4a7JDdUvHMXiegaXYREUmfj/gOIJlPxTN7aV01ERFJp6OCeKLadwjJbCqe2etjvgOIiEhOMeBDvkNIZlPxzEJBPHE4mmYXEZH0U/GUXVLxzE5n+Q4gIiI56dQgnijxHUIyV77vANIrVDz30OaZf6Hx1UfBoKAqYOBZX2TdIz+nZclrxIpKARh41pcorB79gee1LJ7D+sfveO/z9nVLqfrwf1B6wLGs+ev3aV+zmJIxR9Jv8ucA2Pjc/1I4cCSlBxybvjcnIpI+pcApwEO+g0hmUvHMMkE80R842neOKOloWMvm2X+l5uJbiRUUseYvdTTNfwqAflMupOygE3b63OKRh1Jz4c8A6NzSwPLbL6V41OG0rX6HWH4RNRfdwqr//RbJ1iaS7a20LX+DyuO0zrKIZLUPo+IpO6Gp9uwzDcjzHSJykp24jjZcshPX0Upeef89PkTzG89SPHoisYJiLJZPsqMV55K4ZAdYjE1P/46+J3ymF8KLiGSUs8NbNotsR8Uz+2iafQ/lVwykz1H/xrJfXMjSW87HikopGXUEABuf/i3L776S9Y/dgeto3+VxmuY/RdnYyQAUDBxOXklfVvzqGkr3O4qODStwzlE0eL9efz8iIp4NAQ73HUIyk6bas0j4E+Y03zmiprOlkeaFLzL08ruIFZWx5oE6Guf9k8rJnyOvrB90drDukZ+x6cU/UXn8p3Z4jI7G9bSvqX+vsAL0P/Xz7/1+9Z++Q/9pV7LpuftoW/0OxcEEKiac0evvTUTEk1OAl3yHkMyjEc/sciRQ5TtE1LTUv0J+32rySvtiefmUHnAsrcvmk1/eHzPD8gsoH38qbSve3Okxmhc8TekBx2J52/8s17zwBQoH74drb6F94wqqPhqn+Y1nSba39ObbEhHxaarvAJKZVDyzy2m+A0RRfp8q2pa/QbK9BeccLYtfpWDAcDoa1wPgnKP5zRcoGDhyp8doev39afauXGcHm2c9QJ+jz8F1tJJaXxlwSejs6I23IyKSCU4I4okC3yEk82iqPbts33xkt4pqDqT0wONZ8asvYrEYhdVjqDjsDFb98XqSzZsAR+Gg0fSfNh2A1hULaXzlYQaceTUAHZtW0dmwhqIRh2x37IaXEpQfcgqxgmIKqkbhOlpZftd0SsZMIlZcns63KSKSTuXAUcCzvoNIZjHnnO8M0gOCeCIf2EDqP7uIiIhv19XX1f6X7xCSWTTVnj2OQKVTREQyxym+A0jmUfHMHppmFxGRTHKMbp8p21LxzB4n+Q4gIiLSRRFwvO8QkllUPLNAEE/EgJ3f11FERMSPE30HkMyi4pkdxgOVvkOIiIhs42jfASSzqHhmB02zi4hIJjrKdwDJLCqe2eFY3wFERER2oF8QT+zvO4RkDhXP7DDRdwAREZGd0HS7vEfFM+KCeKIPoJ8mRUQkU2m6Xd6j4hl9h/PeDcBFREQyjkY85T0qntGnaXYREclkE4J4otB3CMkMKp7RN8l3ABERkV0oBCb4DiGZQcUz+jTiKSIime4I3wEkM6h4RpguLBIRkYg4xHcAyQwqntGmC4tERCQKxvkOIJlBxTPaxvsOICIi0g0a8RRAxTPqDvIdQEREpBsGBvHEIN8hxD8Vz2hT8RQRkajQqKeoeEaciqeIiESFzvMUFc+oCuKJcmCo7xwiIiLdpBFPUfGMMI12iohIlGjEU1Q8I0zFU0REouRA3wHEPxXP6FLxFBGRKBkYniYmOUzFM7r0k6OIiERN4DuA+KXiGV1jfAcQERHZQ6N8BxC/VDyja6TvACIiInso8B1A/FLxjKDwHJn+vnOIiIjsIY145jgVz2jSaKeIiERR4DuA+KXiGU0jfAcQERHZC4HvAOKXimc0DfMdQEREZC9oqj3HqXhGk26VKSIiUVSptTxzm4pnNKl4iohIVFX5DiD+qHhGk4qniIhE1SDfAcQfFc9oqvYdQEREZC+peOYwFc9oGuA7gIiIyF7SVHsOU/GMJi0eLyIiUaURzxym4hkxQTxRAFT4ziEiIrKXNOKZw1Q8o0ejnSIiEmUa8cxhKp7Ro+IpIiJRpuKZw1Q8o0fFU0REokzfx3KYimf06D+siIhEWZnvAOKPimf0qHiKiEiUqXjmMBXP6OnrO4CIiMg+UPHMYSqe0VPsO4CIiMg+UPHMYSqe0VPkO4CIiMg+KA7iCfWPHKW/+OhR8RQRkajTqGeOUvGMHk21i4hI1JX6DiB+qHhGj0Y8RUQk6jTimaNUPKNHxVNERKJOI545SsUzejTVLiIiUaf+kaP0Fx89GvEUEZGoU//IUfqLj54C3wFERET2kfpHjtJffPR0+g4gIiKyj9Q/clS+7wCyx1Q8RXpRnmtrubZzRqJp04CiEWuH5sdig4ubSwcXthb1rejIKx6Mxap8ZxTJAs53APFDxTN6OnwHEMlmnVZYfF/BeUffOTK+8bJjBraUrI8NqJ2ZXHz0HEdpKwM684oam0sHL28oH76+oWJEa2PZkFhL8YDS9oKygc7yhmKmr6siu6dBlBylL5DRo/+sIr3sXVc97IfNV6x5/N2fHP7rvhUv/uisynEzaq2qb6NbM+2ltgVT5i4uqlmx+DBbQUnX5zmsc0vJwCWN5cPWbK4Y0dhYNtRtKR1U1FrYp18yVliDWYWv9ySSYfS9LEeZcxrtjpIgnrgLuMh3DpFccFP+nU9+Ov/xyZtisY2fH1w19/XCwhMwM4DCdtd84mtu7rSXku0jVjM2BgN2d7y2gop1jWVDVjZUjNjYWD6so6lsSF5LUb+KjvySarDqrccWyQHjps+Y+rrvEJJ+Kp4RE8QTtwOX+s4hkiseK/zyc2NiK44DeKqk+NUvVVdVtJmN7rpPLOk6D3vbvXbWTLdx3LsuyE8yck9fpzNWsKW5tHp5Q/mwdQ0VI1qaymrclpKq0raCsoHO8odiVthT7ylXbWhczW/+WUdD8wYw4/ixtZw8/pwP7LNyw7v87onvsXTtIs4+6iJOPewTADRs2cgdj17PltZGzj7yQg4bdQIAt/3925x34jVUlg1M+/uJuIOmz5j6hu8Qkn6aao8eneMpkkZnt9004aWiy94ssbYDTtrSctjz9UvavlE14MlHykqPwawIIBmzvJf3s8Ne3i/1nGCle6t2ZnLJkW+6qpI2DjbY7UhmXrK9pKJx6ZiKxqVjWPnCB7Y5LNlS3G95Y9mw1Q0Vwxsayoclm0urC9oK+1Z25hXVYFbZC28968Qsj48dcznDqw6gpa2Zm++/nIOGTWRIv+C9fcqKK/j48Vfyav2zH3ju7EWPc8LYs5kw6kRuffgbHDbqBObWP8ewgfupdO4dTbXnKBXP6CSKPOwAACAASURBVNF/VpE02kJR6VltNxU/VviVjTGjshAKf7Bm3eTPb9z8zkVDBm3alJc3Ydvn1A+2MT//UN4YgP6b3appLyXfnDzXlfRrZLztxU0gDBcraVlfU9KyvqZq3Zzttrfnl2xqKqtZ3lA+fGNDxfC2prIhsZai/uUd+aWDnMWGYKala4C+ZQPoW5Y6I6K4sJTBlSPZ2LT2A8WzoqQfFSX9eO3dFz/w3LxYPm0drXQk24lZjM5kJ/+cez+Xn3FjOt9CNtH3shyl4hk9bb4DiOSad1zNiGvar5z104JbjjBLrT94QHv7qKffXeZurez79G2VfQ5xZv129Nz1faz63il51fdOgeI21zh5jnvp9JeTyWFrGWfQIyOVBR1b+lZueqtv5aa3ttuWtLy25pJByxorhq1rKB/R3Fg+1DWXVJW0F5T3T8YKajDLyXtmr2tYydJ1iwgGje3W/pP2m8qvHr+JZ+cn+OjRl/L0vAc46oBTKSzQXYz3UrvvAOKHimf0NPoOIJKL/po8btKU5KtPnpP39OStjxnY9I2bTvzk5oa1Fw+pfvatwoLjd3WMlkIrf2SSHfvIpBixpOuYuNC9fNYst/nApW5MfpJhvZE75joLy5tXjCpvXjFq8KqZH9jmgNbCylVN5TWrGspHNDRUDGtvKh1c0FpU2bczr6g6W9csbW3fwp2P3sA5x36BksKybj2npKicK868CYDm1gYefeVePj/tP/n9kz+kubWBqYd+nNGDx/Vm7GzT4DuA+KHiGT2bfQcQyVVfbr9i8kR78/kgturYro8PSCYH/mXZioEPl5XO/kbVgKoOsxG7O1YyZvkzD7TDZx6Y+nzMcvdm7czk8okLXXVJO90bhttHBhS3bawuXr+xesD67S8w7sgramgqHbK8oWL4xsby4S2NZTXWUjKgrD2/tMpZXk0U1yzt7OzgjkdvYNL+pzBh9Il7dYyHZ/+WaUd8hlmLHmf04EM4fPRJ3PHoDVxZe3MPp81aDhXPnBW5Lxqi4iniU23bTeNnF12+qNja99t225lNzRNPbt6y5cuDBj7xVEnxCXtSzN6qsQN++pG8AwAGbnIrzpyVXHjiPFfet4nxBgU9+R66K7+ztaJvQ/2BfRvqt9uWtFhHS/HAdxvKh61tqBje2Fg+LNlcMqi4rbCifzJWOCQT1yx1znHPkz9gcOUITjn043t1jNWblrKxaS0H1Exg2bq3KCvqg2G0d7T2cNqs1jh9xtSk7xDih5ZTipggnjgP+F/fOURy2f62tP7Rwv/oZ0bfne0zt7DwzUuHDGpvisX2af61tMVtOnmOm3fqy0lq1jPO2PlrZpK2goq1jWU1qxoqhm9sKB/e3lw2OL+lqF+fjvySQb7WLH1rxVx+/OAXqek/6r3rrT581MWsb1wNwIkHf4jNzev53v1X0NLWjJlRVFDCNz9x93tT8nf94z/50FEXMajvMBq2bOD2R65jS1sTtZMu4PDRJ6X7LUXV0ukzpg7vjQOb2TeBT5O6eCkJXOace3HXz8osZnYgcBupc8CLgKedc583synAP4FLnXN3hvtOAF4Gvuqc+8FOjvcT4OPAcOdcMnzsM8DXSE18NABXOOdeDbedAfwPkAfc6ZyrCx+/B5hE6vzcf5H6s203swuA7wPLwpec45z77E7fn4pntATxxJnA33znEMl158SemvmDghmTzHa+VFISkj/sX/nMb/pUTMCsz76+Zl6naz/qTTfnzFnJpv2XsV+eo2Zfj+lDas3Swcsayoetb6gYvqWxbChbSgaWtReUDdCapTnh9ekzpvb4CbFmdizwI2CKc67VzAYChc655T39Wr3JzB4BbnXOPRB+Pt45Nzcsnj8DVjjnTg+33QxMA363o+JpqZ+w3gFWAF93zv0zfPw4YL5zboOZnQnc4Jw72szygDeB04ClwEzgU865183sLODh8NC/B55yzv0iLJ6TnHNXduf9aao9ejb5DiAi8H/Jk46cknzliQ/lvTBlZ/vEIPbV9RtPOn9Tw8oLh1S/vrQg/5h9ec3OPCt4fqxNfH5sarTuwCVufu3M5KrD33JDijo4cF+OnU6pNUuX7FfRuARWPv+Bbak1S/svaywfuqahfHhDQ/nwzubSQUVthX37as3SrNFbp4wNAdY651oBnHNrt24ws+uADwElwHOkRuucmT1BasTwRKAM+CzwdWA8cJ9z7ltmVgb8ARhGahTwv5xz95lZPanCtdbMJgE/cM5NMbMbgBHA6PDjT5xzPw1zfBv4d2ANsASYvYPCOIRU6SN8H3O7bFsM9DGzamA1cAa7HoyaAswD7gM+RWrEFOfcc132eSF8bwBHAYucc2+Hef8X+AjwunPuvdcxs391ec4eUfGMHp3jKZIhrmq/avIRsYX/GmrrjtrVfoM7Owc/vHT54PvLy/71nwP7D+s065GRyjeG29g3hueNBahe75aeNSv51nHzXZ8+zYy3iH59T61Zum5oScu6oVVrd7RmaenGprKaFQ3lwzY2VIxobSobkt9S1K9Ma5ZGSm8NoDwKXGdmbwL/j1RxfDLcdotz7j8BzOy3wNnAX8Ntbc65SWZ2DfAAMBFYD7xlZj8mVd6WO+dqw+d353SXg4CTgQrgDTP7BTABOAc4jNR52y8Bs3fw3B8Dj5vZc+F7+qVzbmOX7X8iNXX+cniMXZ1g/Cng3vB93WRmBc65bZeyupj3RzKHkirEWy0Fju66s5kVAOcD13R5+DwzOyH8/f845365s0CR/MKU41Q8RTKG2bTWmw+aXXT5O0XWMWp3e3+ssemoaU3NjVdVVz05s7joxJ4sSav627Bfnp437JenQ/kWt3Hqq27eKa8kY4M3cIilvvllhYKO5srKTYsqKzct2m5b0vJbm0sHLW8sH7quoXxEU2P5ULqsWToUsxIPkWV763rjoM65RjObSGr08mTgPjOLO+d+BZxsZv8BlAL9SY0Cbi2eD4Yf5wLznHMrAMzsbWB4+PgPw2nth5xzT3cjTiIceW01s9VANXA88IBzrgVoMbO/7uiJzrlfhtPtZ5AabbzMzA7rsssfSI1gHkSqVB63o+NY6pSVs4BrnXMNZvYiqWn5h7rsczKp4nnCjo6xE7eSmmbv+udwn6bas5em2kUySCOlfT7SduPahwvjDWa7L3hlzpXfvXL15FnFRa9fUV2V1xKL9fgUeWOJVT54jB3/4DEx8jtc6zEL3KwzZyebx6zgwJijuqdfL1PEXEdRedPyUeVNy3e0ZqlrLapc1VRWs2pzxYjNjeXDO5pKqwtbiyr7dOYVDcZiuu9l+qzd/S57xznXCTwBPGFmc4HPhdPFt5KaFl8SToV3Xfl/64hhkg+OHiaBfOfcm2Z2BKkSd6OZPRaOnnYAW3943PZOAl2P08ke9q3wvNS7gbvN7DXgkC7bVppZO6nzMK8hLJ5mNpz3y/QMUhf7VAJzw2v5SoEthMXTzA4F7gTOdM5t/WFgGamyvdUw3r9oCDO7HqgCLtuT99OVimfE1NfVbgriiRa2/0cuIp4scCNGf6Pj4hdvyr/rqF1dbNTVpJbWg19YvLTzxgH9nvxTRfkkUueR9biOfCt65hCb9MwhMXDOjXvXzTtrpltz2DtuWGEH2y0Jla0MrLh1Y3Vx627XLN3QUDG8pamsJq+leEBplNcszWC9UjzDq8GTzrmF4UMTSJ0TufX75VozKwfOJTVd3d3j1gDrnXO/M7ONwCXhpnpS0/IPk5pC351ngdvM7Luk+tfZwO07eL0zgMfCK8YHAwNIlb+Duux2HTDIOde5dYEI59yS8D1vPc7vgUucc/eGn5cB71jqbmUDgfuB851zb3Y57kxgfzMbFb7mJ0mtEoCZXUJqxPSUrVfH7w39R4qmlUDgO4SIvO/ezlOOnhJ75clpebMn737vlDzIu37dhskXbWpYesGQQa+vzs8/sjczYmbzRtq4eSNTn9asc4vPmpmsP3a+qyxv4RBLXTiRk7q5ZumahooRTanbjg4qaius6JeMFQ4lVWak+9b00nHLgZ9Z6gK0DmAR8Hnn3EYzuwN4jdT3z5m7OMaOjAe+b2ZJUksJXRE+/h3gLjP7L1KjrLvknJtpZg8Cc4BVpKbwdzSLeTrwP2bWEn7+1XCU873iuc3FQdsJy+UZwOVdntNkZs+QusjqNFKF9tawuHY45yY55zrM7ErgEVJfD+52zs0LDzGDVJF/PnzO/VvPm90TWk4pgoJ44jng2N3uKCJpZSSTzxddNXuwbdirAvm7PhXPf79/5Zik2aCezrY7fZrculNfcfNPfjWZP2gTh1pqWk66obWgYm1TWc3KhorhmxrKh3c0lw3OC9csrcZiWXtqwz74+PQZU7s94phNzKw8PBe1FHiKVDF+yXeudFLxjKAgnvg/4GO+c4jI9vrQuGlW0RUbC61z5N48f1PMNl1RPejVuUWFJ/pYZB2goMO1HP+6mzNtdrJ11CoOijmy8p7t6dAZK2xuLq1enlqzdMSWxrKacM3S8gHO8nJ1zdKTp8+Y+oTvED6E098Hk5r+/7Vz7rueI6WdimcEBfHELcB03zlEZMfG29sLHyz8Vo0Ze33e5jMlxXOuGVRV1hazMT2ZbU+Zc8nx77jXa2e6tYcsdiMLOtnt1fvSPeGapSsay4eubigf0dRQPqyjubS6qK2wT2W4Zmkk7lK1Fw6ZPmPqvN3vJtlIxTOCgnjiG8B/+84hIjv3uby/P/+dgt/s0ykxbdD27aoBz/2trPRYzIp6Ktu+GL7avVM7M7n46DfcgNJWxtn7V/VKD2vPL93YWFazvDE1hd/aVDYkr6Wof0VHQckgR2xwhNcs7T99xtQNvkOIHyqeERTEExeSWmZBRDLYLwtufvLkvFe7fbHRzrxVkF9/4ZDqDRvy8g7viVw9pW+jWzPtpeSCKXNd0YDNHGoZuNrGivZ2vr5iBWs7OzDgE5WVnN+v/3b7/au5ie+uXk2Hc/TLy+M3I0ayvqODq5cvY3NnJ1cPrOLUitRqWdOXLeX66moG5Rek+d28b+uapQ3lw9Y1lg9vaigfypaSqpL2gooByVh+TQavWbpp+oypuvtUDlPxjKAgnjiD9+8yICIZKkay88Wi6a9U2aaJPXG82yr7PPPzyr4HO7Ptm5Nnhe2u+cTX3NxpLyXbR6xmbCx1xax3azo6WNPRwcHFxTQlOzm3vp6fDR3GfkXvDyBv7uzk0+8u5vZhw6kpKGBdRwcD8vP57Yb19I3lcVpFBZcvXcKvR4zkn40NzGtp4cqBmXva69Y1SxvLhq5qqBje2Fg+vL2ptLqgtaiysjOvqNrzmqVzps+Yetjud5NspeWUomm57wAisntJYnmntX5v9MyiLywpsM7hu3/Grl22cfMJH9/cuO6SIYOeWVhYuCd3Gul1bQVW+tjhdvRjh8eIJV3nYW+7V8+a6TaOe9cF+Un26kKrnlCVn09VfupbXVksj9FFRazu6PhA8Uxs3sxp5RXUFKRGMAeE+xdgtDhHm3PEzOhwjt9s2MCtQ/fqFtVpE65ZOri4dePggeu3P5WyI6+ooamsZnlD+bCta5bGthQPKO/ILx0YXvDUm8tqLe7FY0sEaMQzgoJ4og+6g5FIZEywRW/8ufC64WY9t0TRP0pLXvqPQQMHdJh5K3XdNWqlW1T7r+TSSQtdVUkbBxvdW2S/py1rb+P8d9/lwWAU5Xnvd6vvrl5Fh3Msam2lKZnk/H79+UjfvjR0dvLVFctZ19HJtVVVLGprpTwW49/6Zu9McdJiHVuKq5Y1lg9d11AxovH9NUv79E/GCmp6YM3Sn02fMfXqHgkrkaTiGVFBPLEatMSJSFR8Pu+hZ79R8Pvje/KYrUbLV6oGvvhEaclxmPk74XAP9N/sVp0xO/nGSa+50n6NjDdIy0VTTckkn3t3MZcNGMhpFR+8s+mNq1byWksLdw8fQWsyyafeXcyMYcMJCt9f6WhTZyfXLl/GT4cO4+bVq9jcmeSC/v2ZUJKpp1L2jtaCijVN5UNXNZQP29RQMaK9qXRwQWtRZcUerFn65ekzpv6o14NKxlLxjCgtIi8SPb8vuPHJ4/Je3+eLjbY1r7Bw4aWDB7U05MXG9/Sxe1Nxm2ucPMfNPf3lZHLYWsZZ6r7SPa7dOb6wdCnHl5VxQf/tT4+9Y906WlySq8LzNr+1cgUnlJVxRkWf9/a5efUqTi4vZ3FbOwVmnF5RwTXLlnHH8H0+gyJrdMYKm5tKq5c3bl2ztHyobSkeULrNmqXnTJ8x9X7fWcUfneMZXYtQ8RSJlPPbv378v2JfeHmANfTo1enj2tr2f/bdpe7H/Sqf+mXfisOisv5jS6GVPzLJjn1kUoxY0nVMXOhePmuW23zgUjcmP0mPnEjpnOPbK1cwuqhwh6UTYGp5OTeG0+3tzjFnyxY+1+XK9/q2NlZ2dHBUaRlvtK6nyPIwoGXvb1edlfKSbaV9Gpfs16dxCax8/gPbUmuWDljWXFL1Fkz1lFAygUY8IyqIJ64jdZ9YEYmQAWxa+2LR9LZ8S9b0xvFX5eWtumjIoLffLSiI9A+mY5a7N2tnJpdPXOiqS9oZu7fHmd3czPlL3uWAwiK23gfqiwOrWNHRDsAnK/sBcNf6dfx50yZiwLl9K/lsl5L6peXLuGZgFUFhIes6Orhq2TIakp1cNXAgp3cZFZXdckDF2AXzm3wHEX9UPCMqiCc+DdzjO4eI7LmjbP7r9xX+12iz3lv38oHyspnXD+xf02k2tLdeI10GbnIrzpyVXHjiPFfet4nxBpE4n1W2s2zsgvmZvSSA9DoVz4gK4omjgBd95xCRvXNl3p+f+UrBH3t1SaRms6arq6tmvVhcdEIvL5GTNqUtbtPJc9y8U19OUrOecQaROK1AAPjn2AXzNc+e41Q8IyqIJ/oD63znEJG998fC7zx1ZOyNk3r7dWYXFS24YnCV2xKL7fWUdSbK63TtR73p5pw5K9m0/zL2z3MM8Z1Jdum2sQvmX+47hPil4hlhQTyxDsi4O5iISPfk09E+q+iK+ZXWdGhvv1YndH53QL9n7qson4RZWW+/ng8HLnHza2cmVx3+lhtS1MGBvvPIdq4du2D+j32HEL9UPCMsiCeeBHp9tEREes8gNqx5vuiqjjxLpmW0bml+3rILhlQvW5Wff1Q6Xs+X6vVu6Vmzkm8f/7qrqNjCeNMqLpng7LEL5id8hxC/VDwjLIgnfgpc5TuHiOyb42Kvzbun4Kb9zSjc/d494/cV5c/fPKDf6KRZdxb9jrTyLW7jKa+4eae8koxVb2S8wb7efUf2zgFjF8xf6DuE+KXiGWFBPHExcKfvHCKy776Sf9/TV+Y/cGI6X3NzzDZ9oXrQq68WFZ6ImZfbWKZbfodrPXaBm3vG7GTzmBUcGHNkffHOEO1A6dgF8zt8BxG/VDwjLIgnJgEzfecQkZ7xQOG3nj4s9nZayyfAc8XFc6+uHljcGovtn+7X9so5N+5d9/pZM92aw95xwwo72M93pCw2d+yC+b1+LrNkPhXPCAviiWKgEciKZVJEcl0BHW2ziy57s49tOSTdr90O7ddVDXj2obLSYzDrtfVFM1nNOrf4rJnJ+mPnu8ryFg4xfW3tSXePXTD/Yt8hxD8Vz4gL4onXYe/v6iEimWUI61Y+U3R1LM/cIB+v/3ZB/uILh1SvW5+Xd4SP188UfZrculNfcfNPfjWZP2gThxqU+s4UcVeMXTB/hu8Q4p+KZ8QF8cS9wCd95xCRnnNS7NU5vy64eayZvzv03NG3z7M/69f3IGc2wFeGTFHQ4VqOn+fmnvFSsiVYxUExR5XvTBE0aeyC+bN9hxD/VDwjLogn4sB3fecQkZ71zfzfPXVp/t+8Lpe2IRZbf+ngQa+/UVTYq3dYihJzLjn+HTevdqZbd8hiN7Kgk1G+M0VAK6l7tLf7DiL+qXhGXBBPnAn8zXcOEel5icKvPzMutth76XustOTlrw4a2K/dLPCdJdMMX+3eqZ2ZXHz0G25AaSvjDGK+M2Wgf41dMP9o3yEkM6h4RlwQTwwE1vjOISI9r5D21peKLnur3FoO9p2lDVr/Y9DA5x8rLTkOs7StNxollY1uzekvJRdMmeuKBmzmUIOcvEhrB34+dsH8K32HkMyg4pkFgnhiETDGdw4R6XnDbM3ypwq/WBgzN9B3FoD5hQVvXTJ4UNPmvDwtjbMLhe2u+aTX3NxpLyXbh6/m4Fhu3974wrEL5v/KdwjJDCqeWSCIJ34L/LvvHCLSO06LzXrl9oIfHWKWGbd9dOB+2q/vM3f27XMoZn1958l0saTrnPCWe+2sWW7jwe+6ID/JSN+Z0uyQsQvmz/MdQjKDzkXJDi/4DiAivecfyUkTftN52nO+c2xlYNds2HTiY0uWt45sb8+YXJkqGbO8l/aPHXbjp/Imf/pr+SO/dmHeoqfG2RPNhcxzkO2jPxuB+b1xYDP7ppnNM7M5ZvaKmUXuPFIzO9DMngjzzzez28PHp5iZM7NLuuw7IXzsK7s43rVmtsDM5prZq2b2IzMrCLedF/5ZzTOzm7s858fh679iZm+a2cbw8ZFm9lL4+Dwzu7zLc+rD19j6vOO6+54z4qdn2Wf6wi+S5a7vuPCkY2OvP3tAbNnxvrNsNaizc9BDS1cMeqisdNa3qgYM7jQb5jtTFLwz2Pa75cN5+wEM2OxWTpudfPOk11xpv0bGGxT5ztfDnhu7YH6ypw9qZscCZwNHOOdazWwgEMVzj38K/Ng59wCAmY3vsu014BO8f2vsTwGv7uxAYTE8HTjGObfRUudiXwuUmFkf4PvAROfcGjP7tZmd4px7zDn3pS7HuAo4PPx0BXBs+OdbDrxmZg8655aH2092zq3d0zesEc/sMAdo8B1CRHrXh9tuPKLZFb3hO8e2zm5qnvT84qX9j2ve8iTOdab79V3Ssei6RSz+8eLttq39+1oWfmMhC7+1kHdufoe2tW0AtK5oZdH1i1j4rYU0L2pOHafT8c733iHZ2uM9aafW9bHBvz8576TLr8qfdMG1eW13nRZ7fslAnnWpkcJs8EwvHXcIsNY51wrgnFu7tRCZ2XVmNtPMXjOz283MwsefCEf3ZoWji0ea2f1mttDMbgz3KTOzRDha+JqZnRc+Xh+WW8xskpk9Ef7+BjO7Ozz222Z29daAZvZtM3vDzJ4xs3t3MlI5BFi69RPn3Nwu2xYDxWZWHb6HM4CHd/Fn8k3gCufcxvBYbc65OufcZmA0sNA5t/Vi5P8HnLODY3wKuLfL81vDx4vooc6o4pkF6utqO9F0u0jWa6Go5My275Ylna33nWVbJc6V3rZqzeTfrVi1qDSZfD2dr73u0XUU1ex4oLB4ZDFjrh/D/jfuT58j+7DyDysBWP/EeoZ8ZgjBtQFrH04N2qx/fD2Vx1YSK/LzrXFLkVU8Mil27JcvzT/+U1/LK//+x2IvzxthT3bE3i8mEdRbxfNRYHg4NXyrmU3usu0W59yRzrlDgBJSI6NbtTnnJgEzgAeA6cAhwAWWulnCGcBy59xh4fP/3o0sBwHTgKOA682swMyOJFXsDgPOBCbt5Lk/Bh43s4fN7EtmVrnN9j8BHweOA14itSbqdsIRzXLn3Ds7eZ1FwIFmFphZPvBRYPg2xxgJjAIe7/LYcDObAywBbu4y2gnwz3Ca/cWdvOYOqXhmj976zy0iGWSxGzzsyvar650j7SOL3XFYa9uBzy9eetCnNzU8iXONvf167evbaXi1gX4n9dvh9vKx5e8VydIxpXSs7wDA8oxkW5JkWxLLMzqbOtn8ymYqj9/2+74fyZjlzzwwdvh3PpM3+dNfyx/2jc/mvfHsWHuipaB3zpfsJa3Av3rjwC71b2si8HlSSwreZ2YXhJtPNrMXzWwuMBUY1+WpD4Yf5wLznHMrwlG9t0kVsbnAaWZ2s5md6Jzb1I04CedcazjtvBqoBo4HHnDOtTjnGoC/7uR9/JLUba//CEwBXjCzrj9F/YFU8XxvJLI7zGxaWArrzew459wG4ArgPuBpoB62+xrySeBPrsushXNuiXPuUGA/4HNmVt1l/5OdcxOcc3t0bq2KZ/Z42ncAEUmPvyWPPuIPnVMy9ofNGMS+vn7D5EeWLm8Y0t6xR6Mhe2rF71cw+LzBYLvfd8NTGyg/tByA/qf0Z81f17D0jqVUfaiK1Q+upursKizWjQN5sGioHfg/H82b8tmv5I+dfkXe8oeOtKc2ljHbQSbfDWj22AXzdzhC1xOcc53OuSecc9cDVwLnmFkxcCtwrnNuPHAHH1xPdWueJB8cPUwC+c65N4EjSBXQG83sunB7B+93pm3XZ+16nE728PoZ59xy59zdzrmPhK9zSJdtK0n9HZ8GPLb18XAkcuuFPZeH0+mNZjYqfN4jzrkJpM4TLQwf+6tz7mjn3LHAG8Cb20T5JDspt+FI52vAiXvy3nZExTN7PAds8R1CRNLjax2fn/x2cvDzvnPsSk1H55BHly4/+ttr178Qc25FTx9/8yubye+TT0lQstt9Nz63kS3vbGHgmanlUAsHFDL666MZ8+0xWKHRvqGd4ppilty2hHdvfZfWlb3Wl/bZmkqr+c2peSd9/ur8iRd+Ka/516fEnlvWn+ccbPadbRu99sNReDX4/l0emkB4TmT4+drwgphz9/C4NUCzc+53pC7GOSLcVE9qhBV2fG7ktp4FPmRmxWGOs3e0k5md0eWq88HAAGDZNrtdB3xtByORE8JfM8KHvwv8Yut0fXhe6Hsl2cwGhR/7AV/g/YuWMLODgH7A810eG2ZmJV2ecwKpwrpPdFV7lqivq20N4omnSJ1nIiI54Oy2mw6dXXT5whJr23/3e/vziYbGY85sbNo8fXDVUy////buPD6q8m7/+OebkIQloqCCaKljXVEQFETFBa0VaadarVrrr0u0rXtrra11rI8aq7Wjti5d1GLr0k196tNW7VStG6KIXtA0pQAAIABJREFUgCIaEdCqA4Ql7IEQss3cvz/OiQYlksBk7lmu9+s1ryRnZs65kj/gmvuc+z4VFUdilpFBj8Z3Gln32jrWv74e1+pINaVY9LtFDD1vk0vXaJjTwIrHVrDHFXtQUvbxQ9f9Xx2DvzyYVU+tYuD4gZTtVEbdw3UMPX/ox16baxp72/aJsTYuMbaE0pRrHfu2e/Xzr6Q37L2YvUsdQzzH68lR+Urg12HJaiO4hvHccDb33QSjc8uAmd3c7wjgZjNLE4w0XhBuvxb4g5ldB0ze0k6cczPN7FGCyb91BCOomzttPwG43cyawp8vc84tC4tg+766unLNnUA/YLqZNQMNBAX4tfD5281sZPj9T8PR3XZfBR50my7uPgz4pZk5gnMKv/jI5KetogXkC0gklrgU+KXvHCKSPZ+xJQueLv/R9iVGblycuAUv965487uDd65oLinJaFlumNvAqidWsfsPNl2bfeOCjSz8zUIiP4xQscvHJyBtmLeBdbPWMeT/DWHpX5fSf3R/ynYqY+lflrL7xfm9zvt+i9zc6Ix03aj33K4VbeyT5cOngZ2HzZubcxPhssXMKp1zDWbWF5hCUIxn+c7lm0Y8C8tTvgOISHa953bd/dLWC1+5teyOg81y//Kpw5qah7+8oLbt2p0GTv5nZb9DCU/lZVLd3+vos0cf+h/Un2UPLSPdnGbRbxcBULZjGbtfEhRK5xzLH1vO0AuCkc0Bxwyg9ne1uJRj16pdMx0r6+YNtWHzhpYOAxi82tV+4ZX0u0e85fpvt5ER1vP//08v5tIZmmRm+xOc7r5fpTOgEc8CE4klloD30ysikmW3lv32+VNKp47f8itzR7JXr4VnDxm8YmWv0tFbfrVkSuVGt/a42W7OcbPTJYPXMsKC09aZ9j/D5s39WQ/sV/KcimeBicQS9wPf9J1DRLLNuSnll0z/dMmKw3wn6a57t99u6q0DdtjHme3sO0ux6dXmmg+f52omvppu3HMp+5Y4Bm/5XV1y0LB5c2dnaF9SQFQ8C0wklvg68CffOUQk+/qxcf2sivOXV1jrnr6zdNfakpI15+4y6M255WVHEt5pRrLMOXfAQvfWF2a6FSPfd58qb2OvrdxT7bB5c3N/ZpZ4oeJZYCKxxGCC+6vqH26RIrSPLXr/yfLLdzSjv+8sW2Ny3z6zLx200/at4XqE4s9uK92CL8xMJw+b53aobGK4QWkX3zpp2Ly55/VoOMlbKp4FKBJLzAIO8p1DRPw4vXTyjJt6TTrELD8/gLZAc2zQTtOe6ttnHGblvvMI9N/gVh3/mpt77Bvpsp3rGWHQ9xNe/qVh8+Y++gnPSxFT8SxAkVjiGqDadw4R8eeOstue/0LpjLyabPRR88vK3vv2kEHr60tLR2751ZIt5a1u47i3XM3EWenmSB37lTg6XpvbBOw4bN7cRl/5JLepeBagSCwxgmDRWhEpWs5Nrbh45m62aqzvJNvCgfvtDtu/OGmH/iNceEcWyR3mXHrE+25OdKZbNXyB270sxfxh8+Z+3ncuyV0qngUqEku8A1t9YbiIFIDt2FD/asUFq8utLe+vl1xZWrLi27sMfvu98rIjfGeRzg1c5855/ntv/n7Lr5RilfOLDctW+4fvACLi13r6bX9yy0/TztHgO8u22imV3vmRxUuPuHH5yld6ObfIdx7ZLLe6vyV8h5DcpuJZuFQ8RYS3XGTPq9rOftM5CuL01hc2NI55aUHtjkc2bpyMc22+88gmXq6pqlnqO4TkNhXPwvUysMR3CBHx78+p4w97Jn3wFN85MqWPc33vrFtxzF+W1r3bL52e4zuPfODvvgNI7lPxLFDJeNQBj/jOISK54ZzWS4+qczu84jtHJh3Y3LLvSwtqh329ft3zOLfedx5R8ZQtU/EsbPpHQEQAcJSUTGi+aa9WV7rQd5ZMKoGSy1evHf+fRUsadmtte9l3niI2u6aq5j3fIST3qXgWtsnACt8hRCQ31FO5w6kt1c3OscF3lkwbkkoNeaJ2yWHXrFg1vcQ5XWeYfRrokC5R8SxgyXi0DXjQdw4RyR1vuD33vq7tG6/7ztFTTmvYcOjUBbWVYzY2PY9zad95ioQD/uo7hOQHFc/C9yffAUQkt9yT+vy4KakRz/vO0VMqndvu3mXLx/9h2fK5vdPp+b7zFIEXaqpq3vUdQvKDimeBS8ajM4F5vnOISG45q/XyI1e6/rN85+hJY5uaD5i2oHbPL69vmIxzuoVjz7nHdwDJHyqexUGjniKyiTQlpcc33xRpdaW1vrP0pF7Q69qVq49J1C5dvXNbW0HN6s8R64G/+Q4h+UPFszj8GQpj8WgRyZw19B94RstVDc6x0XeWnvbptrZPPbtoyZjLVq15yZzTpMvMeaimqkajydJlKp5FIBmPLgQK9nouEdl6s9w++93Y9tWCPuXe0TfXrR/3wsLFZQc0N7+Ac/pAvu10ml26RcWzeOh0u4hs1l2pk454OT2sYO5stCXbp9M7PLik7qjf1q14ozztNClm682rqaqZ5juE5BcVz+LxMBT+6TQR2Tpfa/nJuDWucrbvHNl09MamkdMWLBr6+YYNk3Gu2XeePHSv7wCSf1Q8i0QyHl0HPOQ7h4jkphSlvY5vvmm3NldSVIuvl0P5TStWHfOPxcuW7JBKFVXx3kZtwB99h5D8o+JZXO7wHUBEctdKdtj5ay0/WeMcRTf6t1dr6x4vLFw86sI1a18059b4zpMHHq+pqlnmO4TkHxXPIhKu6anlRESkU9Pd/vvf1nbqTN85fLlg7bojn1u4OLVnS8tU31lynCYVyVZR8Sw+d/oOICK57fbUqUe+mt4745ONIretZ8SdDYy6q4Exkxo6fd3MxSl6/XQdD7/VCsD8lSlGT2rgwDsbmLaoDYC2tONzf9xAY2vmJ6bvmE7v9M/Fy474Zd2KWb2cW5DxA+S/5cC/fIeQ/KTiWXweAHQaSUQ+0RktVx1e7/q+ken9PlfVl9nnV/LKuZWbfT6Vdlz+dBMT9uz1wbbfvdrK7RN78++v9eUX01oAuHNmK18/sIy+ZZbpiB+Y0Ljx4GkLageNb2ycjHOtPXag/PPnmqqaNt8hJD+peBaZZDy6EbjPdw4RyW1t9Cqb0HzTLilnWb2O79czWjh1WBmD+n1YKMtKoLEVGlsdZSWwtsnx2NutfHNkWY/n6e1cn9/UrTzmgSV1ycpUuqbHD5j7HDDJdwjJXyqexelOdCcjEdmCOgYOqmqNrXCOlkzszwwm/KmR0ZMamPTqx3e5eF2af8xr44JDNi2UF40t54YXmqn6ZxM/OaqC655v5idHVVBiPTfa+VHDW1r2nrqw9oCz1q6bgnP1WTtw7vl3TVXNfN8hJH+peBahZDz6DvCM7xwikvteTI8YcVfqxOkZ2dfZ/Zh1XiWPf60vv53ZwpQFm56tveTJJm783McL5ae3L2HyWf2Y9u1+9C0zatenGbZTCd/4x0bOeLiRt1elMhFvi0qg5Idr1h799KIlTUNbW4t14fRbfAeQ/Ga6Y1hxisQSJwKP+s4hIvnh0fIrXziw5P2jMrW/6slNVJYbPxpX8cG2PW5fT/t/SSsbHX3LjEkn9ubk/T4cAT3j4UauP7aC+2a3MmHPXkR2KOEnzzbxly/3zVS0LvtHZb8Z1+408FMps12zfnA/ZtdU1RzkO4TkN414Fq9/AW/5DiEi+eG0luqx612fOVv7/g0tjvXN7oPv//NuiuGDSjd5zfvf347kJcHjtP3LuCO6ael8PtnGrpUl7L1jKY2tUGLBo9HTtJ9TGjaMnbqgtv/YjU3P41x2hl390minbDMVzyKVjEcdcLPvHCKSH1ooqzih+caBKWcrtub9dRscR967gZF3NTD29xuI7t2LiXv14q5XWrjrlS1fQuqc4/oXmrlqfDBCeu7oMr7/RBPRvzbyo8PLtyZSRvRzrvIPy5aPv2/p8rf7pNPzvAXpeUuAB32HkPynU+1FLBJLlAHvA7v5ziIi+eHYktdev6fs5v3N6Pkp5XkmBamf7Tjgxb9tVzkGs36+82TYFTVVNXHfIST/acSziCXj0VbgVt85RCR/PJc+aOS9qYnFOrHmE5VC6dWr1oz/d+3SNYPb2grp7k/r0M1HJENUPGUSsNZ3CBHJHz9t++bR89JDX/SdI1cNbWv71NOLlhwSW7V6Wolzy33nyYA7a6pqinkJKckgFc8il4xH16NPsiLSTV9quW7MBlcx13eOXPa1dQ2Hv7CwtmJEU/ML5O91bU3ozJhkkIqnANxO8I+LiEiXNFPee2LLjf3Tzlb5zpLL+qfd9n9dWnfUnXUrairS6f/6zrMV7q2pqqnzHUIKh4qnkIxH69BtNEWkmxa5Qbud33rJQucohqWEtsmRG5sOnLagdvdow4bJOJcvH/RT9ODqJ2Z2pZnNMbM3zGy2mR3aU8fqKWa2r5lNDvPPNbNJ4fZjzMyZ2Xc6vHZUuO1Hn7C/S81snpnVmNnrZnaLmZWFz50R/q3mmNmNHd5zlpmtCDPM/sgxq8zsnfBR1WF7MjxG+3vGZfpv0xkVT2l3A9DsO4SI5Jf/pA856C+p43S9ZxeUQVl8xapj/rl46bIBqdRrvvN0wUM1VTXv98SOzexw4IvAwc65A4HPAYt64lg97FfArc65Uc65YcCvOzz3JvCVDj+fCbze2Y7M7HxgAnCYc24EcAiwHOhjZjsSfAg4zjl3ALCLmR3X4e0PhRlGOed+H+5vIHANcCgwFrjGzAZ0eM+xHd7z0tb9+t2n4ikAJOPRRQQTjUREuuV/2r49/r/pXbP2H1e+27O1LTJl4eKDvrd67YvmXK5eqpACftaD+x8CrHTONQM451Y655YAmNnVZjbTzN40s0lmwT1Uw5HFW83slXB08RAz+3s4mnd9+Jp+ZpYIRwvfNLMzwu1JM9sp/H6MmU0Ov682s3vCfb9nZhe3BzSzq8xsvpm9aGYPdDJSOQSobf/BOVfT4bkFQG8zGxz+DhOBxz/hb3IlcIFzbm24rxbnXNw5tw74DPCOc659Hd2ngVO38Dc+AXjKObfaObcGeCrM4JWKp3R0A9DoO4SI5J8TW342qtGVz/edI5+cW7/uyMkLF9veLS1TfWfZjPtqqmp68u52/wGGmtnbZnaHmY3v8NxvnHOHOOeGA30IRkbbtTjnxgB3AY8AFwHDgbPCUcGJwBLn3Mjw/U90Ict+BCWtfVSwzMwOISh2I4HPA2M6ee+twLNm9riZ/cDMdvjI8w8DpwPjgFl0cmbRzPoDlc65zkaY/wvsa2YRM+sFnAwM7fD8qeFp+IfNrH37bmw6ilzLput2PxeeZp/eyTF7hIqnfCAZjy4Dfus7h4jkn41U9P1Cy8/7ph1rfGfJJwPT6YF/X7zsiFvqVszq5dwC33lCGwlO0fYY51wDMBo4F1gBPGRmZ4VPH2tm082sBvgscECHtz4afq0B5jjnloajpu8RFLEa4Hgzu9HMjnLOdWUZqIRzrtk5t5Lg1PZg4AjgEedck3NuPfBYJ7/HvcAw4G/AMcDLZlbR4SX/S1A8zwQe6EIWAMzshLAUJs1sXDhieQHwEPACkIQPrq1+DIiElyw8BdzfxcO0n2rP6rW1Kp7yUTcC632HEJH8k3RDhl7c+r33nCPtO0u+Ob5x48EvL1g0+NgNjZNxztPd5z9we01VzeKePohzLuWcm+ycuwb4LsGoXW/gDuC08DrHu4HeHd7WPmKYZtPRwzTQyzn3NnAwQQG93syuDp9v48PO03F/HfcJQZnr1c3fY4lz7h7n3JfC4wzv8NwyoBU4HnimfbuZDe0wsef88HR6g5ntEb7vSefcKILrRMvDbY855w51zh0OzAfeDrevar9kAfg9QaEHWMymo6KfCrd5peIpm0jGo6uA23znEJH89K/04aMfTh39gu8c+ajC0ftXy1ce89CSZQu2S6Xf8BRjFdDjt8YMZ4Pv3WHTKMJrIsOfV5pZJXBaN/e7K9DonPszwWScg8OnknxYyLZ0bSTAVOBEM+sd5vji5l5kZhM7zDrfBdiRj5e7q4HLnXMfrP7gnFvUYWLPXeHmnwN3tp+uD68L/aAkm9mg8OsA4EKCkomZDelwrJOA9vV1nwQmmNmA8D0Twm1edavVS9H4JcGnzwFbeqGIyEdd1nb++DEl86ftUVJ3uO8s+Wj/lta9pi6sdbcN2H7KPdv3H4nZ9lk8/A1ZuktRJfDrsGS1EVzDeK5zbq2Z3U0w0rcM6O6tR0cAN5tZmmCk8YJw+7XAH8zsOmDylnbinJtpZo8CbwB1BCOom/u7TABuN7P2JbIuc84tM7P9OuyrqxPv7gT6AdPNrBloICjA7Ssg3G5mI8PvfxqO7gJcbGYnEfwdVwNnhcddHf6+Mzu8Z3UXs/QYy9+bKUhPisQSVxBMNhIR6bZ+bGx4teL8Zb2tdS/fWfLZ8tLS5d8aMujdBWVl2SjxC4B9a6pqtLQeYGaVzrkGM+sLTCEoxrN858p3OtUunbkNWOg7hIjkpw30qYy23FDm3GZHiaSLBqVSg/5Vu/Twn61YObPUudotv2ObXKXSuYlJZjabYDb6/6l0ZoZGPKVTkVjiTOCvvnOISP46peSFmbeU3TnaTAMd26rRbMP3B+/8ysu9K47ErDTDu38dOLimqkYTw6RHqXjKJ4rEEi8Buk5LRLbar8p+Pfmk0mnH+M5RKGZVlM+9YJdBNJaUDMvgbifWVNV4n3gihU+fQGVLLgH06UREttrFrd8dX+t2yuoi1YXs4OaWYS8tqN3nq+vWP0+wHua2ekalU7JFxVM+UTIenQH8xXcOEclnZhOb48OaXa/3fCcpFKVQeuWqNeOfqF2ybpe2thnbsCsHXJ6pXCJbouIpXRFDt9IUkW3QQN/+X2q53pxjne8shWS3ttSuTy1aMvbKlatfLgkWK++uh2qqal7NeDCRTqh4yhYl49HFwE2+c4hIfpvnPr3HFW3fmeucLt/JtK+ubzjsxQW1fUc1NU/Bua5OEGoALuvJXCIfpeIpXXUTsMh3CBHJbw+mPnvok+kxU3znKETbOdf/T0vrjr572fK3KtLpd7rwlmtqqmp6eokmkU2oeEqXJOPRjcD3fecQkfx3QeslRy11A7t7RxrposOamodPW1AbOWl9w2Sc29jJy14Hbs9mLhHQckrSTZFY4lHgRN85RCS/9aeh/pWKC9aWW2p331kK2ftlvRacvcvglat6lY7usNkBR9RU1UzzlUuKl0Y8pbu+hyYaicg2Wkfl9qe0/LTVOTKxHJB0Yo/Wtt0nL1o8+ger10w151aGm3+v0im+qHhKtyTj0QVAte8cIpL/5rg99rqmrarGd45i8K369UdMWbi49IDm5icIVioR8ULFU7bGrcBs3yFEJP/9MXXC4c+mRk32naMY7JBOD3hwSd2fa6pqVvvOIsVL13jKVonEEqOB6UCm7xcsIkXGSKenV1z02iCrH73lV8s2eILq+s/7DiHFTSOeslWS8eiraEakiGSAo6RkQvNNn2l1pVqyredsAM73HUJExVO2xVXAu75DiEj+W8t2A05vuabROU1e7CFXUl2/wHcIERVP2WrJeLQRqAK6epcMEZFOzXZ77XtD29d0/XjmTQZ+5TuECKh4yjZKxqNTgV/4ziEiheHuVHTc1NQBz/vOUUDWAWdRXa8JHZITVDwlE64CtCSKiGTEN1qvOHKV2+413zkKxPd1il1yiWa1S0ZEYomRwAyg3HcWEcl/A6lfNaPioqZelt7Nd5Y89k+q60/xHUKkI414SkYk49HX0cLyIpIhq9l+xzNb/medczT5zpKnlgPn+g4h8lEqnpJJNwEv+Q4hIoVhpttv2C/bTn/Fd448dQ7V9St8hxD5KBVPyZhkPJoCvkmwXpyIyDb7TeqUI2ek953iO0eeuZfq+kd9hxDZHBVPyahkPPou8F3fOUSkcJzZ8j/j1rp+r/vOkSfeAb7vO4RIZ1Q8JeOS8eh9wH2eY4hIgUhR2mtC8027plzJUt9ZclwT8BWq69f7DiLSGRVP6SkXAXN8hxCRwrCcATt/ozW22jmafWfJYZdQXa8F+CWnqXhKjwjvanQ6ut5TRDLkpfTwA36TOnmG7xw56gGq63/nO4TIlqh4So9JxqNzgQt95xCRwvHLtq8cNTu95wu+c+SY+WjpJMkTWkBeelwklrgHONt3DhEpDGW0tbxacf78/tY4wneWHLAROJTqet09TvKCRjwlGy4C3vQdQkQKQyu9yic03zQo5azOd5Yc8D2VTsknKp7S45Lx6EbgNKDedxYRKQzLGDj47NYf1zlHq+8sHt1Pdf0ffIcQ6Q4VT8mKZDw6HzgTSPvOIiKFYUp65IF3p6LTfOfwZDpwnu8QIt2l4ilZk4xHHweu8J1DRArHDW1fO/rNdKTYJhstBk6hul5LS0ne0eQiybpILPEn4Ou+c4hIYSintXlWxXnvVlrT/r6zZMFG4Giq63UPe8lLGvEUH84BtBafiGREC2UVJzTfOCDtbIXvLFnwbZVOyWcqnpJ1yXi0CTgF0O3vRCQjFrPzkHNaL13sHG2+s/SgONX1D/gOIbItVDzFi2Q8ugQ4meDewiIi2+yZ9OhRf0xNmOo7Rw95DLjSdwiRbaVrPMWrSCxxJvAXwHxnEZHC8GT5j6fuW1J7hO8cGfQmMI7q+vW+g4hsKxVP8S4SS1wG3OQ7h4gUht40b5xVcf6Cvta8n+8sGbAYOJzq+kW+g4hkgk61i3fJePRm4HbfOUSkMDRR0WdiS7wy7Wy17yzbaB3wBZVOKSQqnpIrLgX+5juEiBSGhW7wpy5svTjpHCnfWbZSK/Blquvf8B1EJJNUPCUnJOPRNPANYLLnKCJSIJ5IH3rwQ6ljX/SdYys44Cyq65/xHUQk03SNp+SUSCyxPfAiMNx3FhEpDM+W//Clz5QsHec7RzdcQnW9Lj+SgqTiKTknEkvsBkwDhvrOIiL5rw/NjbMqzlvcx1r29p2lC+JU1+vWwlKwdKpdck4yHl0MTACW+84iIvlvIxV9oy03lKcda31n2YK7VTql0Kl4Sk5KxqPzgM8Bq3xnEZH8957bdfcftF70X+dI+87SiT8C5/sOIdLTVDwlZyXj0RqCkc9cH6UQkTzwSPqIMf9IH/mC7xyb8SDwLarrc7UUi2SMrvGUnBeJJQ4FngK2851FRPLflPJLXv50yfLDfOcI/R/wVarrC/ke8yIfUPGUvBCJJY4EngD6+c4iIvmtHxsbXq04v663te7pOcqjwGlU17d6ziGSNTrVLnkhGY++CJwIbPSdRUTy2wb6VJ7Ucn2Jc9R7jPEEcLpKpxQbFU/JG8l49DngFKDJdxYRyW9vu6F7/Ljt3PnO4eO03zPAKVTXt3g4tohXKp6SV5Lx6JNAFNjgO4uI5Le/pY4Z++/0oVOyfNjHgROprs/KB2gzu9LM5pjZG2Y228wOzcZxM8nM9jWzyWH+uWY2Kdx+jJk5M/tOh9eOCrf96BP2d6mZzTOzGjN73cxuMbOy8Lkzwr/VHDO78SPv+4qZvRU+99cO26vM7J3wUdVhezI8xuzwkU83MegxKp6Sd5Lx6LPA8Wi2u4hso4taLz56sdtxRpYO9zDwJarrs3LJkJkdDnwRONg5dyDBEnWLsnHsDPsVcKtzbpRzbhjw6w7PvQl8pcPPZwKvd7YjMzufYLWUw5xzI4BDCNaM7mNmOwI3A8c55w4AdjGz48L37Q1cARwRPndJuH0gcA1wKDAWuMbMBnQ45LFh7lHOuZe2/k9QOFQ8JS8l49FpwDFokXkR2SZmJzTfuF+z6/V+Dx/oXoLZ69m8pnMIsNI51wzgnFvpnFsCYGZXm9lMM3vTzCaZmYXbJ5vZrWb2Sji6eIiZ/T0czbs+fE0/M0uEo4VvmtkZ4fakme0Ufj/GzCaH31eb2T3hvt8zs4vbA5rZVWY238xeNLMHOhmpHALUtv/gnKvp8NwCoLeZDQ5/h4kEo8qduRK4wDm3NtxXi3Mu7pxbB3wGeMc5tyJ87dPAqeH35wC/dc6tCd/X/n/PCcBTzrnV4XNPhRmkEyqekreS8ejrwNF0+AdJRKS7Gujb/+SW69LOsb6HDnE78G2q61M9tP/O/AcYamZvm9kdZja+w3O/cc4d4pwbDvQhGBlt1+KcGwPcBTwCXAQMB84KRwUnAkuccyPD9z/RhSz7EZS09lHBMjM7hKDYjQQ+D4zp5L23As+a2eNm9gMz2+Ejzz8MnA6MA2YBzZvbiZn1Byqdc519yPgvsK+ZRcysF3AyH966eR9gHzObamYvm1l7udyNTUeRa8Nt7Z4LT7NP7+SYRUfFU/JaMh6dDxwFvOs7i4jkr7lu9z2vbPvWnB6YbHQ91fWXUF2f9UlMzrkGYDRwLrACeMjMzgqfPtbMpptZDfBZ4IAOb300/FoDzHHOLQ1HTd8jKGI1wPFmdqOZHeWc68rqAAnnXLNzbiXBmarBwBHAI865JufceuCxTn6Pe4FhwN8IznS9bGYVHV7yvwTF80zggS5kAcDMTghLYdLMxoUjlhcADwEvAEmg/cNCL2Dv8PhnAndvpgBvTvup9ry7tranqHhK3kvGo0mC8jnHcxQRyWN/TX3usKfTozM52ejHVNdflcH9dZtzLuWcm+ycuwb4LnCqmfUG7gBOC69zvBvo3eFt7SOGaTYdPUwDvZxzbwMHExTQ683s6vD5Nj7sFR3313GfEJS5Xt38PZY45+5xzn0pPM7wDs8tA1oJrv1/pn27mQ3tMLHn/PB0eoOZ7RG+70nn3CiC60TLw22POecOdc4dDswH3g53Vws86pxrDUdM3yYooov5cFQU4FPhNumEiqcUhGQ8upSgfGZ7hqqIFJBzW39wVJ3b4ZVt3E0b8B2q62/ORKatFc4G37vDplGE10SGP680s0rgtG7ud1eg0Tn3Z4LJOAeHTyUJRljhw2sjP8lU4ETvIl1rAAAKOklEQVQz6x3m+OLmXmRmEzvMOt8F2JGPl7urgcudcx9czuCcW9RhYs9d4eafA3e2j1aG14V+UJLNbFD4dQBwIfD78Kl/Eox2El7Hug/BCPCTwAQzGxC+Z0K4TTrRrU8cIrksGY+uicQSEwgu4j/Tdx4RyT+OkpIJzTft9UrFBQvKLLX7VuxiHcHC8P/JdLatUAn8OixZbQTXMJ7rnFtrZncTjPQtA2Z2c78jgJvNLE0w0nhBuP1a4A9mdh0weUs7cc7NNLNHgTeAOoIR1M2dtp8A3G5m7UtQXeacW2Zm+3XYV1dnjN9JcAe86WbWDDQQFODXwudvN7OR4fc/DUd34cOC+RbBiO1lzrlVAOHvO7PDe1Z3MUtR0i0zpeBEYgkDfkaw9IWISLcdaO++80j5Vbuades2vbVAlOr6N3oqV6Exs0rnXIOZ9SU4Y3Wuc26W71zSc1Q8pWBFYolzCK5j0si+iHTb2aWPT7um7E+Hd/HlrwFfpLp+SU9mKjThQuz7E5zuvt8593PPkaSHqXhKQYvEEhMJZkJW+s4iIvnn/rL45PGlbxyzhZf9GziD6vqGLEQSyWsqnlLwIrHEKCAB7Oo7i4jklxLSqRkVF76+k607uJOX3AV818ManSJ5ScVTikIkltgV+AfB4sUiIl02gHWrZ1Rc1FhmqU912JwiWC7pFl+5RPKRllOSopCMR5cQ3OXoj76ziEh+WUP/gV9puXqDc7TfY301MFGlU6T7NOIpRScSS1wC/AIo9Z1FRPLH+aWPTo2VPbgdcDLV9T19b3eRgqQRTyk6yXj0NoI7XKzwnUVE8sddqZNqb2g983CVTpGtpxFPKVqRWGIo8DC67lNEPlkb8ONkPHqr7yAi+U7FU4paJJaoAG4HzvOdRURyUh1wRjIefd53EJFCoOIpAkRiiTOASUB/31lEJGc8DXwjGY8u8x1EpFDoGk8RIBmPPgSMAqb7ziIi3rUBMWCCSqdIZmnEU6SDSCxRBlwPXAaY5zgikn3vA2cm41F9CBXpASqeIpsRiSUmEKz5Odh3FhHJmgeB85Lx6DrfQUQKlYqnSCciscRg4E8ESy+JSOHaAFycjEfv8R1EpNDpGk+RTiTj0TrgBOASoNFzHBHpGTOA0SqdItmhEU+RLojEEnsB9wJH+s4iIhnRDFwD/CIZj6Z8hxEpFiqeIl0UiSVKgO8DPwP6eI4jIltvBnB2Mh59y3cQkWKj4inSTZFYYh/gPuBwz1FEpHs0yinimYqnyFYIRz9/QLD0Um/PcURkyzTKKZIDVDxFtkE4+nkn8FnfWURkszYA1wK3aJRTxD8VT5EMiMQS/w/4JbCL7ywi8oG/A5ck49FFvoOISEDFUyRDIrFEf4JT7xcCpZ7jiBSzd4HvJePRx30HEZFNqXiKZFgkljiY4PT7WN9ZRIpME3AjEE/Go02+w4jIx6l4ivSAcPLROcDPgQGe44gUgyeA7ybj0Xd9BxGRzql4ivSgSCwxELia4PR7mec4IoVoPnB5Mh59xHcQEdkyFU+RLAjvfPRz4DTfWUQKxHKgGrg7GY+2ec4iIl2k4imSRZFYYhzwC7T4vMjW2gjcAtyYjEfX+w4jIt2j4iniQSSWOA2IA3v6ziKSJ9LA/cBVyXh0se8wIrJ1VDxFPInEEuXAecAVwBDPcURy2b+BK5Lx6Bu+g4jItlHxFPEsEkv0Jiigl6MCKtJRArg2GY/O9B1ERDJDxVMkR6iAinzgXwSF8xXfQUQks1Q8RXKMCqgUsccICuervoOISM9Q8RTJUWEBPRe4FNjdcxyRnpIGHgWuS8ajs3yHEZGepeIpkuMisUQpcCrwQ3QbTikcjcB9wG3JePQdz1lEJEtUPEXySCSWOJJgBPRLQInnOCJbYwnwG+B3yXh0te8wIpJdKp4ieSi8E9IlwNlAX89xRLridYKF3x9IxqOtvsOIiB8qniJ5LLwX/NnAOcC+nuOIfFQrwfWbdyTj0Wd9hxER/1Q8RQpEJJY4hmAy0peBCr9ppMj9F/g9cG8yHl3uO4yI5A4VT5ECE4kldgSqCEqoRkElW1qAvwN3A88l41H95yIiH6PiKVLAIrHEeOA7wClAP89xpDDNBf4A3J+MR1f6DiMiuU3FU6QIRGKJfgTl8+vA54BSv4kkzy0BHgD+koxHX/MdRkTyh4qnSJGJxBKDgdOBrwLjAPObSPLEaoJT6Q8Ak5PxaNpzHhHJQyqeIkUsEksMBc4ATiNYnF4lVDpaSzAr/UHgaS2DJCLbSsVTRACIxBK7AF8ETiI4Hd/HbyLx5L8E90x/FHgxGY+2ec4jIgVExVNEPiYSS/QhKJ8nEZTRXfwmkh6UAqYRls1kPDrPcx4RKWAqniLyiSKxhBGchp8AHAcchtYJzXeLgWeBp4F/aza6iGSLiqeIdEs4GnoE8NnwMQbNks91K4DnCMrmc8l49G3PeUSkSKl4isg2icQS/YHxwLEEs+QPAsq9hpJlwMsEZfM54E0t6C4iuUDFU0QyKhJLlAOjgEM7PPbyGqqwbQBeBaYDM4DpyXh0kd9IIiKbp+IpIj0uvI3n2PBxIDAc2BOdou+udcAcoAaYSVA230rGoymvqUREukjFU0S8iMQSvYFhBCX0gPDrcODTaD3RBuAtgpI5B3gTmJOMR2u9phIR2UYqniKSUyKxRCXwGSAC7BE+On6/na9sGZQmmFmeBN4Pv7Y/3gMW6ppMESlEKp4iklciscRAggK6CzBoM4+dO3zN5iQnR3CnnxXhY/lHvq4gmPSzAFikuwCJSDFS8RSRghWezu/X4VH5kZ/7EaxJWvKRhyMYlWx/tBFM4mn8hK/rdZcfEZFPpuIpIiIiIllR4juAiIiIiBQHFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJChVPEREREckKFU8RERERyQoVTxERERHJiv8PaCF8x3gEFrUAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Count plot of browsrs\n",
+        "plt.figure(figsize=(9,7))\n",
+        "axis = sns.countplot(x='browser', data=df)\n",
+        "axis.set_xticklabels(axis.get_xticklabels(), rotation=60, ha='right')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 700
+        },
+        "id": "GhSgwslLDKp7",
+        "outputId": "f59be957-0497-4060-ca94-d4550b057c67"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[Text(0, 0, 'Chrome Mobile WebView'),\n",
+              " Text(0, 0, 'Chrome Mobile'),\n",
+              " Text(0, 0, 'Facebook'),\n",
+              " Text(0, 0, 'Samsung Internet'),\n",
+              " Text(0, 0, 'Mobile Safari'),\n",
+              " Text(0, 0, 'Chrome'),\n",
+              " Text(0, 0, 'Mobile Safari UI/WKWebView'),\n",
+              " Text(0, 0, 'Chrome Mobile iOS')]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 648x504 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# Distribution of Hour\n",
+        "plt.figure(figsize=(9,7))\n",
+        "axis = sns.distplot(df['hour'],bins=24, color='green')\n",
+        "plt.title('Distribution fo Hour')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 475
+        },
+        "id": "b-fJ1yuIDO46",
+        "outputId": "116b8436-5850-4945-9048-f4a17e976ed9"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Distribution fo Hour')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 648x504 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#  aware people vs the total people\n",
+        "expo_yes = df.query('experiment == \"exposed\"').yes\n",
+        "expo_count = len(expo_yes)\n",
+        "expo_yes_count = expo_yes.sum(axis=0)\n",
+        "\n",
+        "cont_yes = df.query('experiment == \"control\"').yes\n",
+        "cont_count = len(cont_yes)\n",
+        "cont_yes_count = cont_yes.sum(axis=0)\n",
+        "print('Control yes count:',expo_yes_count)\n",
+        "print('Total Control:',cont_count)\n",
+        "print('Exposed yes count:',expo_yes_count)\n",
+        "print('Total Exposed:',expo_count)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9g8x9_jiEjJj",
+        "outputId": "7a795a87-f00f-439e-9cef-6ec8a8dcba10"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Control yes count: 308\n",
+            "Total Control: 586\n",
+            "Exposed yes count: 308\n",
+            "Total Exposed: 657\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import statsmodels.stats.api as sms \n",
+        "baseline_rate = cont_yes_count / cont_count\n",
+        "practical_significance = 0.01  # minimum required effect size\n",
+        "confidence_level = 0.05 \n",
+        "sensitivity = 0.8 \n",
+        "\n",
+        "effect_size = sms.proportion_effectsize(baseline_rate, baseline_rate + practical_significance)\n",
+        "sample_size = sms.NormalIndPower().solve_power(effect_size = effect_size, power = sensitivity, \n",
+        "                                               alpha = confidence_level, ratio=1)\n",
+        "print(\"Required sample size: \", round(sample_size), \" per group\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "79Y-Kf7GFXLz",
+        "outputId": "707a2eba-22fa-42a1-b502-15387cfc0b41"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Required sample size:  38932  per group\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from scipy.stats import binom\n",
+        "# Calculate the Conversions for each group\n",
+        "cv_rate_control, cv_rate_exposed = cont_yes_count / cont_count, expo_yes_count / expo_count\n",
+        "range = np.arange(200, 370)\n",
+        "cv_prob_control = binom(cont_count, cv_rate_control).pmf(range)\n",
+        "cv_prob_exposed = binom(expo_count, cv_rate_exposed).pmf(range)\n",
+        "fig, ax = plt.subplots(figsize=(12,10))\n",
+        "plt.bar(range, cv_prob_control, label=\"Control\",color='green')\n",
+        "plt.bar(range, cv_prob_exposed, label=\"Exposed\",color='pink')\n",
+        "plt.legend()\n",
+        "plt.xlabel(\"Conversions\"); plt.ylabel(\"Probability\");"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 606
+        },
+        "id": "3Z2FveHFFjXI",
+        "outputId": "11a43877-8b9c-4c9e-dbd0-57b5951879fc"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 864x720 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "std_control = np.sqrt(cv_rate_control * (1 - cv_rate_control) / cont_count)\n",
+        "std_exposed = np.sqrt(cv_rate_exposed * (1 - cv_rate_exposed) / expo_count)\n",
+        "\n",
+        "SE_control = np.sqrt(cv_rate_control * (1-cv_rate_control)) / np.sqrt(cont_count)\n",
+        "SE_exposed = np.sqrt(cv_rate_exposed * (1-cv_rate_exposed)) / np.sqrt(expo_count)\n",
+        "print(std_control, SE_control)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7wx_ssjHGJGK",
+        "outputId": "195214f3-cfb5-46f7-fb15-36e1bf5143f0"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.02055339057798332 0.02055339057798332\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from scipy.stats import norm\n",
+        "conversion_rate = np.linspace(0, 0.9, 200)\n",
+        "prob_a = norm(cv_rate_control, SE_control).pdf(conversion_rate)\n",
+        "prob_b = norm(cv_rate_exposed, SE_exposed).pdf(conversion_rate)\n",
+        "plt.figure(figsize=(10,5))\n",
+        "plt.plot(conversion_rate, prob_a, label=\"Control\",color = 'green')\n",
+        "plt.plot(conversion_rate, prob_b, label=\"Exposed\",color = 'pink')\n",
+        "plt.legend(frameon=False)\n",
+        "plt.xlabel(\"Conversion rate\"); plt.ylabel(\"Probability\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 351
+        },
+        "id": "QtQQ0LmzGT1e",
+        "outputId": "da9bd4e4-f624-4130-89c5-fe7580c488a5"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0, 0.5, 'Probability')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x360 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import scipy.stats as scs\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(12, 6))\n",
+        "x = np.linspace(.35, .6, 1000)\n",
+        "\n",
+        "yC = scs.norm(cv_rate_control, SE_control).pdf(x)\n",
+        "ax.plot(x, yC, label='Control')\n",
+        "ax.axvline(x=cv_rate_control, c='red', alpha=0.5, linestyle='--')\n",
+        "\n",
+        "yE = scs.norm(cv_rate_exposed, SE_exposed).pdf(x)\n",
+        "ax.plot(x, yE, label='Exposed')\n",
+        "ax.axvline(x=cv_rate_exposed, c='blue', alpha=0.5, linestyle='--')\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.xlabel('Converted Proportion') # conversion rate\n",
+        "plt.ylabel('PDF') # probability density function\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 388
+        },
+        "id": "P49EJyIjGpmE",
+        "outputId": "5eda82de-3318-4086-db44-4a4a6bb4e053"
+      },
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 864x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#calculate variance of sum\n",
+        "var_cont = np.sqrt(cv_rate_control * (1 - cv_rate_control) / cont_count)\n",
+        "var_exp = np.sqrt(cv_rate_exposed * (1 - cv_rate_exposed) / expo_count)\n",
+        "var_cont, var_exp"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ix8Oyi7DGt9c",
+        "outputId": "3b722af8-2e01-45d0-ad35-4e5534045c2a"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(0.02055339057798332, 0.019468837373132736)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "diff = cv_rate_exposed - cv_rate_control\n",
+        "z_score = (diff) / np.sqrt(var_cont**2 + var_exp**2)\n",
+        "print(f\"zscore is {z_score:0.5f}, with p-value {scs.norm().sf(z_score):0.5f}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hfISnCM9Hxri",
+        "outputId": "92d98361-671f-45d3-c5fa-2a0ad4fa2e0d"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "zscore is 0.64590, with p-value 0.25917\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "hypo_plots.zplot(area=0.95)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 165
+        },
+        "id": "wXyfSx9ZH3eP",
+        "outputId": "3f73c26b-0be7-4f58-8e16-07abaa47f1c0"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-37-edabb2b79c97>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhypo_plots\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marea\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.95\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m: name 'hypo_plots' is not defined"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From a0e7640f2d74a35ad5781885f16d629757a919f1 Mon Sep 17 00:00:00 2001
From: Mohammed Esamaldin
 <110843414+MohammedEsamaldin@users.noreply.github.com>
Date: Wed, 31 Aug 2022 20:23:48 +0200
Subject: [PATCH 4/6] Created using Colaboratory

---
 EDA_Notebooks/AB_classical_testing.ipynb | 34 +++---------------------
 1 file changed, 3 insertions(+), 31 deletions(-)

diff --git a/EDA_Notebooks/AB_classical_testing.ipynb b/EDA_Notebooks/AB_classical_testing.ipynb
index f535c28..ad5dde7 100644
--- a/EDA_Notebooks/AB_classical_testing.ipynb
+++ b/EDA_Notebooks/AB_classical_testing.ipynb
@@ -28,39 +28,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 38,
+      "execution_count": 39,
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 373
-        },
-        "id": "0bNRpfA1-1Ai",
-        "outputId": "9acdcf95-2548-4b37-c080-0568e440b679"
+        "id": "0bNRpfA1-1Ai"
       },
-      "outputs": [
-        {
-          "output_type": "error",
-          "ename": "ModuleNotFoundError",
-          "evalue": "ignored",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-38-8b40a05ce625>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'../scripts/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mDistributionPlots\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDistributionPlots\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilterwarnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'DistributionPlots'",
-            "",
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
-          ],
-          "errorDetails": {
-            "actions": [
-              {
-                "action": "open_url",
-                "actionText": "Open Examples",
-                "url": "/notebooks/snippets/importing_libraries.ipynb"
-              }
-            ]
-          }
-        }
-      ],
+      "outputs": [],
       "source": [
         "# import important packages\n",
         "import numpy as np\n",

From 55bb90e78a507485fdbd7d650961d3c414f01c69 Mon Sep 17 00:00:00 2001
From: Mohammed Esamaldin
 <110843414+MohammedEsamaldin@users.noreply.github.com>
Date: Wed, 31 Aug 2022 20:25:21 +0200
Subject: [PATCH 5/6] Created using Colaboratory

---
 EDA_Notebooks/AB_testing_overview.ipynb | 1366 +++++++++++++++++++++++
 1 file changed, 1366 insertions(+)
 create mode 100644 EDA_Notebooks/AB_testing_overview.ipynb

diff --git a/EDA_Notebooks/AB_testing_overview.ipynb b/EDA_Notebooks/AB_testing_overview.ipynb
new file mode 100644
index 0000000..24139f2
--- /dev/null
+++ b/EDA_Notebooks/AB_testing_overview.ipynb
@@ -0,0 +1,1366 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": " AB testing_overview.ipynb",
+      "provenance": [],
+      "authorship_tag": "ABX9TyNcyGIdKPQi3lPtRdHzfJXh",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/MohammedEsamaldin/abtest-mlops/blob/main/EDA_Notebooks/AB_testing_overview.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4N60O1vnhSwR",
+        "outputId": "e01c680b-9ebb-4fcf-c339-59f1939d360f"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from IPython.display import Image\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "UPyDdrJdhrb_"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_csv('/content/drive/MyDrive/AdSmartABdata.csv',na_values= ['?',None])\n",
+        "df.isnull().sum().head(10)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yUzyuXb8h6MH",
+        "outputId": "75995b04-c2d5-4783-bdd9-1a806afa575b"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "auction_id     0\n",
+              "experiment     0\n",
+              "date           0\n",
+              "hour           0\n",
+              "device_make    0\n",
+              "platform_os    0\n",
+              "browser        0\n",
+              "yes            0\n",
+              "no             0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head(10)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "id": "y_uT8cemoUcw",
+        "outputId": "422d3d86-27e6-4a35-d7c2-137b409c953d"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                             auction_id experiment        date  hour  \\\n",
+              "0  0008ef63-77a7-448b-bd1e-075f42c55e39    exposed  2020-07-10     8   \n",
+              "1  000eabc5-17ce-4137-8efe-44734d914446    exposed  2020-07-07    10   \n",
+              "2  0016d14a-ae18-4a02-a204-6ba53b52f2ed    exposed  2020-07-05     2   \n",
+              "3  00187412-2932-4542-a8ef-3633901c98d9    control  2020-07-03    15   \n",
+              "4  001a7785-d3fe-4e11-a344-c8735acacc2c    control  2020-07-03    15   \n",
+              "5  0027ce48-d3c6-4935-bb12-dfb5d5627857    control  2020-07-03    15   \n",
+              "6  002e308b-1a07-49d6-8560-0fbcdcd71e4b    control  2020-07-03    15   \n",
+              "7  00393fb9-ca32-40c0-bfcb-1bd83f319820    control  2020-07-09     5   \n",
+              "8  004940f5-c642-417a-8fd2-c8e5d989f358    exposed  2020-07-04     0   \n",
+              "9  004c4cc9-f2ca-4df7-adc9-3d0c3c4f0342    control  2020-07-05    14   \n",
+              "\n",
+              "          device_make  platform_os                browser  yes  no  \n",
+              "0  Generic Smartphone            6          Chrome Mobile    0   0  \n",
+              "1  Generic Smartphone            6          Chrome Mobile    0   0  \n",
+              "2               E5823            6  Chrome Mobile WebView    0   1  \n",
+              "3   Samsung SM-A705FN            6               Facebook    0   0  \n",
+              "4  Generic Smartphone            6          Chrome Mobile    0   0  \n",
+              "5    Samsung SM-G960F            6               Facebook    0   0  \n",
+              "6  Generic Smartphone            6          Chrome Mobile    0   0  \n",
+              "7    Samsung SM-G973F            6               Facebook    0   0  \n",
+              "8  Generic Smartphone            6  Chrome Mobile WebView    0   0  \n",
+              "9  Generic Smartphone            6          Chrome Mobile    0   0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d64bf7dd-1ef9-4cc3-8d0d-62bbf1c8c612\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>auction_id</th>\n",
+              "      <th>experiment</th>\n",
+              "      <th>date</th>\n",
+              "      <th>hour</th>\n",
+              "      <th>device_make</th>\n",
+              "      <th>platform_os</th>\n",
+              "      <th>browser</th>\n",
+              "      <th>yes</th>\n",
+              "      <th>no</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0008ef63-77a7-448b-bd1e-075f42c55e39</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-10</td>\n",
+              "      <td>8</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>000eabc5-17ce-4137-8efe-44734d914446</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-07</td>\n",
+              "      <td>10</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0016d14a-ae18-4a02-a204-6ba53b52f2ed</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-05</td>\n",
+              "      <td>2</td>\n",
+              "      <td>E5823</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile WebView</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>00187412-2932-4542-a8ef-3633901c98d9</td>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-03</td>\n",
+              "      <td>15</td>\n",
+              "      <td>Samsung SM-A705FN</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Facebook</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>001a7785-d3fe-4e11-a344-c8735acacc2c</td>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-03</td>\n",
+              "      <td>15</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>0027ce48-d3c6-4935-bb12-dfb5d5627857</td>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-03</td>\n",
+              "      <td>15</td>\n",
+              "      <td>Samsung SM-G960F</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Facebook</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>002e308b-1a07-49d6-8560-0fbcdcd71e4b</td>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-03</td>\n",
+              "      <td>15</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>00393fb9-ca32-40c0-bfcb-1bd83f319820</td>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-09</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Samsung SM-G973F</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Facebook</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>004940f5-c642-417a-8fd2-c8e5d989f358</td>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-04</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile WebView</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>004c4cc9-f2ca-4df7-adc9-3d0c3c4f0342</td>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-05</td>\n",
+              "      <td>14</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d64bf7dd-1ef9-4cc3-8d0d-62bbf1c8c612')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-d64bf7dd-1ef9-4cc3-8d0d-62bbf1c8c612 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-d64bf7dd-1ef9-4cc3-8d0d-62bbf1c8c612');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "Qy2x5xhEi80v",
+        "outputId": "e60d3249-6ab1-4a49-f7f7-4f2a457ae264"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              hour  platform_os          yes           no\n",
+              "count  8077.000000  8077.000000  8077.000000  8077.000000\n",
+              "mean     11.615080     5.947134     0.070818     0.083075\n",
+              "std       5.734879     0.224333     0.256537     0.276013\n",
+              "min       0.000000     5.000000     0.000000     0.000000\n",
+              "25%       7.000000     6.000000     0.000000     0.000000\n",
+              "50%      13.000000     6.000000     0.000000     0.000000\n",
+              "75%      15.000000     6.000000     0.000000     0.000000\n",
+              "max      23.000000     7.000000     1.000000     1.000000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-dbde3970-723f-47e9-a90c-a2ebac6090ef\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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",
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+              "\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>hour</th>\n",
+              "      <th>platform_os</th>\n",
+              "      <th>yes</th>\n",
+              "      <th>no</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>8077.000000</td>\n",
+              "      <td>8077.000000</td>\n",
+              "      <td>8077.000000</td>\n",
+              "      <td>8077.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>11.615080</td>\n",
+              "      <td>5.947134</td>\n",
+              "      <td>0.070818</td>\n",
+              "      <td>0.083075</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>5.734879</td>\n",
+              "      <td>0.224333</td>\n",
+              "      <td>0.256537</td>\n",
+              "      <td>0.276013</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>7.000000</td>\n",
+              "      <td>6.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>13.000000</td>\n",
+              "      <td>6.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>15.000000</td>\n",
+              "      <td>6.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>23.000000</td>\n",
+              "      <td>7.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dbde3970-723f-47e9-a90c-a2ebac6090ef')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-dbde3970-723f-47e9-a90c-a2ebac6090ef button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-dbde3970-723f-47e9-a90c-a2ebac6090ef');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(df.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uLmSsucSmORD",
+        "outputId": "1e082db0-803d-4bad-d949-7bde300c14f0"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(8077, 9)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.describe(include=[\"object\", \"bool\"])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 175
+        },
+        "id": "ogurWvHxmqjC",
+        "outputId": "ace3770a-09af-4a5b-fe19-de0177d69ae2"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                  auction_id experiment        date  \\\n",
+              "count                                   8077       8077        8077   \n",
+              "unique                                  8077          2           8   \n",
+              "top     0008ef63-77a7-448b-bd1e-075f42c55e39    control  2020-07-03   \n",
+              "freq                                       1       4071        2015   \n",
+              "\n",
+              "               device_make        browser  \n",
+              "count                 8077           8077  \n",
+              "unique                 270             15  \n",
+              "top     Generic Smartphone  Chrome Mobile  \n",
+              "freq                  4743           4554  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d7a2b5cf-edc0-4bbe-8186-819e698e79fb\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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",
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+              "\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>auction_id</th>\n",
+              "      <th>experiment</th>\n",
+              "      <th>date</th>\n",
+              "      <th>device_make</th>\n",
+              "      <th>browser</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>8077</td>\n",
+              "      <td>8077</td>\n",
+              "      <td>8077</td>\n",
+              "      <td>8077</td>\n",
+              "      <td>8077</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>unique</th>\n",
+              "      <td>8077</td>\n",
+              "      <td>2</td>\n",
+              "      <td>8</td>\n",
+              "      <td>270</td>\n",
+              "      <td>15</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>top</th>\n",
+              "      <td>0008ef63-77a7-448b-bd1e-075f42c55e39</td>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-03</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>freq</th>\n",
+              "      <td>1</td>\n",
+              "      <td>4071</td>\n",
+              "      <td>2015</td>\n",
+              "      <td>4743</td>\n",
+              "      <td>4554</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7a2b5cf-edc0-4bbe-8186-819e698e79fb')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-d7a2b5cf-edc0-4bbe-8186-819e698e79fb button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-d7a2b5cf-edc0-4bbe-8186-819e698e79fb');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def value_func(x:str):\n",
+        "  data= df[x].value_counts()\n",
+        "  return data"
+      ],
+      "metadata": {
+        "id": "_APgbiQ_m6d1"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "value_func('device_make').head(20)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IzPhVSZPnf0F",
+        "outputId": "0a426e55-b0a8-4635-8985-e30f2e182250"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Generic Smartphone     4743\n",
+              "iPhone                  433\n",
+              "Samsung SM-G960F        203\n",
+              "Samsung SM-G973F        154\n",
+              "Samsung SM-G950F        148\n",
+              "Samsung SM-G930F        100\n",
+              "Samsung SM-G975F         97\n",
+              "Samsung SM-A202F         88\n",
+              "Samsung SM-A405FN        87\n",
+              "Samsung SM-J330FN        69\n",
+              "Samsung SM-G965F         66\n",
+              "Samsung SM-A105FN        66\n",
+              "Nokia$2$3                64\n",
+              "Samsung SM-G935F         63\n",
+              "Nokia undefined$2$3      60\n",
+              "Samsung SM-G970F         58\n",
+              "Samsung SM-A705FN        56\n",
+              "Samsung SM-A505FN        53\n",
+              "Samsung SM-A520F         51\n",
+              "Samsung SM-G920F         48\n",
+              "Name: device_make, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "value_func('browser')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JsGL84LPn73N",
+        "outputId": "6a5d3a35-78a7-4e09-bb22-01eefb084cdb"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Chrome Mobile                 4554\n",
+              "Chrome Mobile WebView         1489\n",
+              "Samsung Internet               824\n",
+              "Facebook                       764\n",
+              "Mobile Safari                  337\n",
+              "Chrome Mobile iOS               51\n",
+              "Mobile Safari UI/WKWebView      44\n",
+              "Chrome                           3\n",
+              "Pinterest                        3\n",
+              "Opera Mobile                     3\n",
+              "Opera Mini                       1\n",
+              "Edge Mobile                      1\n",
+              "Android                          1\n",
+              "Firefox Mobile                   1\n",
+              "Puffin                           1\n",
+              "Name: browser, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "agg_data = df.groupby(['experiment']).agg({'device_make':'count','browser':'count','yes':'count', 'no':'count'})\n",
+        "agg_data"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 143
+        },
+        "id": "mnkilzmWpbNc",
+        "outputId": "750a57b2-cd14-4058-98b4-a92d2179a669"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            device_make  browser   yes    no\n",
+              "experiment                                  \n",
+              "control            4071     4071  4071  4071\n",
+              "exposed            4006     4006  4006  4006"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5b5a0ccb-5b53-431e-b4e0-df38b7c1e3d0\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>device_make</th>\n",
+              "      <th>browser</th>\n",
+              "      <th>yes</th>\n",
+              "      <th>no</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>experiment</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>control</th>\n",
+              "      <td>4071</td>\n",
+              "      <td>4071</td>\n",
+              "      <td>4071</td>\n",
+              "      <td>4071</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>exposed</th>\n",
+              "      <td>4006</td>\n",
+              "      <td>4006</td>\n",
+              "      <td>4006</td>\n",
+              "      <td>4006</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5b5a0ccb-5b53-431e-b4e0-df38b7c1e3d0')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-5b5a0ccb-5b53-431e-b4e0-df38b7c1e3d0 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-5b5a0ccb-5b53-431e-b4e0-df38b7c1e3d0');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()\n",
+        "ex_co = df['experiment'].value_counts()\n",
+        "\n",
+        "ex_co"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iOZ4PLtP0t4I",
+        "outputId": "08cb8a1c-71f8-4392-de36-d60ed0a8c3ec"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "control    4071\n",
+              "exposed    4006\n",
+              "Name: experiment, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import os, sys\n",
+        "import datetime\n",
+        "import seaborn as sns\n",
+        "from scipy.stats import chi2_contingency, beta\n",
+        "import plotly.graph_objs as go\n",
+        "#from plot import Plo"
+      ],
+      "metadata": {
+        "id": "zTSI72Y7WaXL"
+      },
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.hist(bins=10, figsize=(25, 10))\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 558
+        },
+        "id": "n-4e4Jom_-tC",
+        "outputId": "dae1dde9-381b-472e-9c0e-7a22c7d29d9b"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x720 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**`Calculating p_values`**"
+      ],
+      "metadata": {
+        "id": "DU4J5I-1Mpws"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def delete_users(data):\n",
+        "    cleand_df = data.query(\"not(yes==0 & no==0)\")\n",
+        "    return cleand_df\n",
+        "\n",
+        "clean_df = delete_users(df)"
+      ],
+      "metadata": {
+        "id": "n0ObyKqNAdRE"
+      },
+      "execution_count": 23,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "clean_df.groupby(['experiment']).count()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 143
+        },
+        "id": "HCp-bUCmAgrt",
+        "outputId": "07650dd8-3372-47cb-dad8-69e73aa36cc0"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            auction_id  date  hour  device_make  platform_os  browser  yes  \\\n",
+              "experiment                                                                   \n",
+              "control            586   586   586          586          586      586  586   \n",
+              "exposed            657   657   657          657          657      657  657   \n",
+              "\n",
+              "             no  \n",
+              "experiment       \n",
+              "control     586  \n",
+              "exposed     657  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-82098d38-9446-40e6-9423-c7147da7d179\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>auction_id</th>\n",
+              "      <th>date</th>\n",
+              "      <th>hour</th>\n",
+              "      <th>device_make</th>\n",
+              "      <th>platform_os</th>\n",
+              "      <th>browser</th>\n",
+              "      <th>yes</th>\n",
+              "      <th>no</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>experiment</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>control</th>\n",
+              "      <td>586</td>\n",
+              "      <td>586</td>\n",
+              "      <td>586</td>\n",
+              "      <td>586</td>\n",
+              "      <td>586</td>\n",
+              "      <td>586</td>\n",
+              "      <td>586</td>\n",
+              "      <td>586</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>exposed</th>\n",
+              "      <td>657</td>\n",
+              "      <td>657</td>\n",
+              "      <td>657</td>\n",
+              "      <td>657</td>\n",
+              "      <td>657</td>\n",
+              "      <td>657</td>\n",
+              "      <td>657</td>\n",
+              "      <td>657</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82098d38-9446-40e6-9423-c7147da7d179')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-82098d38-9446-40e6-9423-c7147da7d179 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-82098d38-9446-40e6-9423-c7147da7d179');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# number of user completed the survey with a 'yes' or 'no'\n",
+        "df_yes = df[(df['experiment'] == 'exposed') & (df['yes']==1)].shape[0]\n",
+        "df_no = df[(df['experiment'] == 'exposed') & (df['no']==1)].shape[0]\n",
+        "#plot the count of the column with yes\n",
+        "plot_count(df.yes, df.experiment)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 200
+        },
+        "id": "qZEYn_mRMm0K",
+        "outputId": "04f8bf8a-20f0-47ff-d0e5-55ee260e1f3b"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-26-c9939b45f32c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf_no\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'experiment'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'exposed'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'no'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#plot the count of the column with yes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplot_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperiment\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m: name 'plot_count' is not defined"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 7212516233cde4667abd36642b210faa68781584 Mon Sep 17 00:00:00 2001
From: Mohammed Esamaldin
 <110843414+MohammedEsamaldin@users.noreply.github.com>
Date: Fri, 2 Sep 2022 22:59:44 +0200
Subject: [PATCH 6/6] ML code

---
 notebooks/ML_model.ipynb | 1379 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 1379 insertions(+)
 create mode 100644 notebooks/ML_model.ipynb

diff --git a/notebooks/ML_model.ipynb b/notebooks/ML_model.ipynb
new file mode 100644
index 0000000..56a929d
--- /dev/null
+++ b/notebooks/ML_model.ipynb
@@ -0,0 +1,1379 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyN0OGre+ekra198ybEfkSKi",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/MohammedEsamaldin/abtest-mlops/blob/main/notebooks/ML_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "N8RWT4Ki1Y4L",
+        "outputId": "9dd5437e-3f2f-48cc-8829-1ca9b48cbd12"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/abtest-mlops/data/split_AdSmartABdata.csv\n",
+            "/content/abtest-mlops/data/.gitignore\n",
+            "/content/abtest-mlops/data/samsug_internetAdSmartABdata.csv\n",
+            "/content/abtest-mlops/data/AdSmartABdata.csv.dvc\n",
+            "/content/abtest-mlops/data/Chrome_MobileAdSmartABdata.csv\n",
+            "/content/abtest-mlops/data/Chrome_WebViewAdSmartABdata.csv\n",
+            "/content/abtest-mlops/data/FacebookAdSmartABdata.csv\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "for dirname, _, filenames in os.walk('/content/abtest-mlops/data'):\n",
+        "    for filename in filenames:\n",
+        "      files_dic = os.path.join(dirname, filename)\n",
+        "      print(files_dic)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Importing Pandas an Numpy Libraries to use on manipulating our Data\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# To Preproccesing our data\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "\n",
+        "# To fill missing values\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "\n",
+        "# To Split our train data\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "# To Visualize Data\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "# To Train our data\n",
+        "from xgboost import XGBClassifier\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.naive_bayes import BernoulliNB, GaussianNB\n",
+        "\n",
+        "# To evaluate end result we have\n",
+        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
+        "from sklearn.model_selection import LeaveOneOut\n",
+        "from sklearn.model_selection import cross_val_score\n",
+        "\n",
+        "\n",
+        "# We are importing our Data with Pandas Library\n",
+        "# We use \"Coronary_artery.csv\" \n",
+        "df = pd.read_csv(\"/content/abtest-mlops/data/Chrome_MobileAdSmartABdata.csv\")"
+      ],
+      "metadata": {
+        "id": "Ynx1zMF74xQm"
+      },
+      "execution_count": 32,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df  = df.drop(['auction_id','Unnamed: 0'], axis = 1)\n"
+      ],
+      "metadata": {
+        "id": "7b5IRt4s5Wb_"
+      },
+      "execution_count": 33,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "0pBawsiC5Bup",
+        "outputId": "ad9b3b2e-5b1a-4aa1-8955-2487cc46a7e1"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  experiment        date  hour         device_make  platform_os  \\\n",
+              "0    exposed  2020-07-04    16  Generic Smartphone            6   \n",
+              "1    exposed  2020-07-06     8  Generic Smartphone            6   \n",
+              "2    control  2020-07-03    15  Generic Smartphone            6   \n",
+              "3    exposed  2020-07-10     2  Generic Smartphone            6   \n",
+              "4    control  2020-07-03    15  Generic Smartphone            6   \n",
+              "\n",
+              "         browser  yes  no  \n",
+              "0  Chrome Mobile    1   0  \n",
+              "1  Chrome Mobile    0   1  \n",
+              "2  Chrome Mobile    0   1  \n",
+              "3  Chrome Mobile    0   1  \n",
+              "4  Chrome Mobile    1   0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c761d9bd-3a7c-42a0-bc4e-276996bb3f60\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>experiment</th>\n",
+              "      <th>date</th>\n",
+              "      <th>hour</th>\n",
+              "      <th>device_make</th>\n",
+              "      <th>platform_os</th>\n",
+              "      <th>browser</th>\n",
+              "      <th>yes</th>\n",
+              "      <th>no</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-04</td>\n",
+              "      <td>16</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-06</td>\n",
+              "      <td>8</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-03</td>\n",
+              "      <td>15</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>exposed</td>\n",
+              "      <td>2020-07-10</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>control</td>\n",
+              "      <td>2020-07-03</td>\n",
+              "      <td>15</td>\n",
+              "      <td>Generic Smartphone</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Chrome Mobile</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c761d9bd-3a7c-42a0-bc4e-276996bb3f60')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-c761d9bd-3a7c-42a0-bc4e-276996bb3f60 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-c761d9bd-3a7c-42a0-bc4e-276996bb3f60');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "numerical_column = df.select_dtypes(exclude=\"object\").columns.tolist()\n",
+        "categorical_column = df.select_dtypes(include=\"object\").columns.tolist()\n",
+        "print(\"Numerical Columns:\", numerical_column)\n",
+        "print(\"****************\")\n",
+        "print(\"Categorical Columns:\", categorical_column)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qOHlVfGu5ME3",
+        "outputId": "b760401f-819f-4e62-e93f-cd3e07f2f83f"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Numerical Columns: ['hour', 'platform_os', 'yes', 'no']\n",
+            "****************\n",
+            "Categorical Columns: ['experiment', 'date', 'device_make', 'browser']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get column names have less than 10 more than 2 unique values\n",
+        "to_one_hot_encoding = [col for col in categorical_column if df[col].nunique() <= 10 and df[col].nunique() > 2]\n",
+        "\n",
+        "# Get Categorical Column names thoose are not in \"to_one_hot_encoding\"\n",
+        "to_label_encoding = [col for col in categorical_column if not col in to_one_hot_encoding]\n",
+        "\n",
+        "print(\"To One Hot Encoding:\", to_one_hot_encoding)\n",
+        "print(\"To Label Encoding:\", to_label_encoding)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9I5fVrAD6eeg",
+        "outputId": "2c866509-1b98-425f-87ac-e1a5cda621e7"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "To One Hot Encoding: ['date']\n",
+            "To Label Encoding: ['experiment', 'device_make', 'browser']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "label_encoded_columns = []\n",
+        "# For loop for each columns\n",
+        "for col in to_label_encoding:\n",
+        "    # We define new label encoder to each new column\n",
+        "    le = LabelEncoder()\n",
+        "    # Encode our data and create new Dataframe of it, \n",
+        "    # notice that we gave column name in \"columns\" arguments\n",
+        "    column_dataframe = pd.DataFrame(le.fit_transform(df[col]), columns=[col] )\n",
+        "    # and add new DataFrame to \"label_encoded_columns\" list\n",
+        "    label_encoded_columns.append(column_dataframe)\n",
+        "\n",
+        "# Merge all data frames\n",
+        "label_encoded_columns = pd.concat(label_encoded_columns, axis=1)\n",
+        "label_encoded_columns"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "G1yN0qf_7SDy",
+        "outputId": "06879701-b078-40a1-e9ec-ccf3cf2c82b2"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     experiment  device_make  browser\n",
+              "0             1            1        0\n",
+              "1             1            1        0\n",
+              "2             0            1        0\n",
+              "3             1            1        0\n",
+              "4             0            1        0\n",
+              "..          ...          ...      ...\n",
+              "690           1            1        0\n",
+              "691           1            1        0\n",
+              "692           0            1        0\n",
+              "693           0            1        0\n",
+              "694           1            1        0\n",
+              "\n",
+              "[695 rows x 3 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-eba7494e-788b-4076-b08d-46fc5d8bbcc7\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>experiment</th>\n",
+              "      <th>device_make</th>\n",
+              "      <th>browser</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
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+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
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+              "    <tr>\n",
+              "      <th>690</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
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+              "    <tr>\n",
+              "      <th>691</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
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+              "    <tr>\n",
+              "      <th>692</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>693</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>694</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>695 rows × 3 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eba7494e-788b-4076-b08d-46fc5d8bbcc7')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-eba7494e-788b-4076-b08d-46fc5d8bbcc7 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-eba7494e-788b-4076-b08d-46fc5d8bbcc7');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# We will use built in pandas function \"get_dummies()\" to simply to encode \"to_one_hot_encoding\" columns\n",
+        "one_hot_encoded_columns = pd.get_dummies(df[to_one_hot_encoding])\n",
+        "one_hot_encoded_columns"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "Qh5aCJNi8uoN",
+        "outputId": "1aa3e37a-d9b8-4a51-dc6d-4f1a8a8bffd9"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     date_2020-07-03  date_2020-07-04  date_2020-07-05  date_2020-07-06  \\\n",
+              "0                  0                1                0                0   \n",
+              "1                  0                0                0                1   \n",
+              "2                  1                0                0                0   \n",
+              "3                  0                0                0                0   \n",
+              "4                  1                0                0                0   \n",
+              "..               ...              ...              ...              ...   \n",
+              "690                0                0                1                0   \n",
+              "691                0                1                0                0   \n",
+              "692                0                0                0                0   \n",
+              "693                0                0                0                0   \n",
+              "694                1                0                0                0   \n",
+              "\n",
+              "     date_2020-07-07  date_2020-07-08  date_2020-07-09  date_2020-07-10  \n",
+              "0                  0                0                0                0  \n",
+              "1                  0                0                0                0  \n",
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+              "3                  0                0                0                1  \n",
+              "4                  0                0                0                0  \n",
+              "..               ...              ...              ...              ...  \n",
+              "690                0                0                0                0  \n",
+              "691                0                0                0                0  \n",
+              "692                0                0                1                0  \n",
+              "693                0                0                0                1  \n",
+              "694                0                0                0                0  \n",
+              "\n",
+              "[695 rows x 8 columns]"
+            ],
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+              "\n",
+              "  <div id=\"df-307f482d-bbf0-4e9e-bf5d-3438a869572c\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
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+              "\n",
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+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>date_2020-07-03</th>\n",
+              "      <th>date_2020-07-04</th>\n",
+              "      <th>date_2020-07-05</th>\n",
+              "      <th>date_2020-07-06</th>\n",
+              "      <th>date_2020-07-07</th>\n",
+              "      <th>date_2020-07-08</th>\n",
+              "      <th>date_2020-07-09</th>\n",
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+              "      <th>3</th>\n",
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+              "      <th>690</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
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+              "    <tr>\n",
+              "      <th>691</th>\n",
+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
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+              "    <tr>\n",
+              "      <th>692</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
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+              "    <tr>\n",
+              "      <th>693</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>1</td>\n",
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+              "    <tr>\n",
+              "      <th>694</th>\n",
+              "      <td>1</td>\n",
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+              "      <td>0</td>\n",
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+              "  </tbody>\n",
+              "</table>\n",
+              "<p>695 rows × 8 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-307f482d-bbf0-4e9e-bf5d-3438a869572c')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-307f482d-bbf0-4e9e-bf5d-3438a869572c button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-307f482d-bbf0-4e9e-bf5d-3438a869572c');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Copy our DataFrame to X variable\n",
+        "X = df.copy()\n",
+        "\n",
+        "# Droping Categorical Columns,\n",
+        "# \"inplace\" means replace our data with new one\n",
+        "# Don't forget to \"axis=1\"\n",
+        "X.drop(categorical_column, axis=1, inplace=True)\n",
+        "\n",
+        "# Merge DataFrames\n",
+        "X = pd.concat([X, one_hot_encoded_columns, label_encoded_columns], axis=1)\n",
+        "print(\"All columns:\", X.columns.tolist())\n",
+        "X"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 522
+        },
+        "id": "YRdoUxdR8ybP",
+        "outputId": "f3070fb9-5ac1-4655-f0f9-ae5440970722"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "All columns: ['hour', 'platform_os', 'yes', 'no', 'date_2020-07-03', 'date_2020-07-04', 'date_2020-07-05', 'date_2020-07-06', 'date_2020-07-07', 'date_2020-07-08', 'date_2020-07-09', 'date_2020-07-10', 'experiment', 'device_make', 'browser']\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     hour  platform_os  yes  no  date_2020-07-03  date_2020-07-04  \\\n",
+              "0      16            6    1   0                0                1   \n",
+              "1       8            6    0   1                0                0   \n",
+              "2      15            6    0   1                1                0   \n",
+              "3       2            6    0   1                0                0   \n",
+              "4      15            6    1   0                1                0   \n",
+              "..    ...          ...  ...  ..              ...              ...   \n",
+              "690    21            6    1   0                0                0   \n",
+              "691     1            6    1   0                0                1   \n",
+              "692     7            6    0   1                0                0   \n",
+              "693    16            6    0   1                0                0   \n",
+              "694     4            6    0   1                1                0   \n",
+              "\n",
+              "     date_2020-07-05  date_2020-07-06  date_2020-07-07  date_2020-07-08  \\\n",
+              "0                  0                0                0                0   \n",
+              "1                  0                1                0                0   \n",
+              "2                  0                0                0                0   \n",
+              "3                  0                0                0                0   \n",
+              "4                  0                0                0                0   \n",
+              "..               ...              ...              ...              ...   \n",
+              "690                1                0                0                0   \n",
+              "691                0                0                0                0   \n",
+              "692                0                0                0                0   \n",
+              "693                0                0                0                0   \n",
+              "694                0                0                0                0   \n",
+              "\n",
+              "     date_2020-07-09  date_2020-07-10  experiment  device_make  browser  \n",
+              "0                  0                0           1            1        0  \n",
+              "1                  0                0           1            1        0  \n",
+              "2                  0                0           0            1        0  \n",
+              "3                  0                1           1            1        0  \n",
+              "4                  0                0           0            1        0  \n",
+              "..               ...              ...         ...          ...      ...  \n",
+              "690                0                0           1            1        0  \n",
+              "691                0                0           1            1        0  \n",
+              "692                1                0           0            1        0  \n",
+              "693                0                1           0            1        0  \n",
+              "694                0                0           1            1        0  \n",
+              "\n",
+              "[695 rows x 15 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-877dac2e-d7be-45bc-b35f-8bec6306bdbc\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>hour</th>\n",
+              "      <th>platform_os</th>\n",
+              "      <th>yes</th>\n",
+              "      <th>no</th>\n",
+              "      <th>date_2020-07-03</th>\n",
+              "      <th>date_2020-07-04</th>\n",
+              "      <th>date_2020-07-05</th>\n",
+              "      <th>date_2020-07-06</th>\n",
+              "      <th>date_2020-07-07</th>\n",
+              "      <th>date_2020-07-08</th>\n",
+              "      <th>date_2020-07-09</th>\n",
+              "      <th>date_2020-07-10</th>\n",
+              "      <th>experiment</th>\n",
+              "      <th>device_make</th>\n",
+              "      <th>browser</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>16</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
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+              "      <th>1</th>\n",
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+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>15</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>15</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>690</th>\n",
+              "      <td>21</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
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+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>691</th>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>692</th>\n",
+              "      <td>7</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>693</th>\n",
+              "      <td>16</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>694</th>\n",
+              "      <td>4</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>695 rows × 15 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-877dac2e-d7be-45bc-b35f-8bec6306bdbc')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-877dac2e-d7be-45bc-b35f-8bec6306bdbc button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-877dac2e-d7be-45bc-b35f-8bec6306bdbc');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 39
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y = df[[\"yes\",\"no\"]]\n",
+        "\n",
+        "# Droping \"class\" from X\n",
+        "X.drop([\"yes\",\"no\"], axis=1, inplace=True)\n",
+        "X\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "id": "96GCWF_M9fqb",
+        "outputId": "11a0fb26-818e-486e-85f7-f42a1481ec33"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "KeyError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-43-4b8d060f207b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Droping \"class\" from X\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"yes\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"no\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4911\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4912\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4913\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4914\u001b[0m         )\n\u001b[1;32m   4915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4148\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4149\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4150\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4152\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   4183\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4184\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4185\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4186\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6015\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6016\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6017\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6018\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6019\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyError\u001b[0m: \"['yes' 'no'] not found in axis\""
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!git config --global user.email 'mohammedesam.mem@gmail.com'\n",
+        "!git config --global user.name 'MohammedEsamaldin'\n"
+      ],
+      "metadata": {
+        "id": "ip0vg6r--UUv"
+      },
+      "execution_count": 45,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!git remote add origin https://MohammedEsamaldin:mohamed.zeezo123@github.com/MohammedEsamaldin/abtest-mlops.git"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ieCll8Ur_WJg",
+        "outputId": "5086a2de-5c4e-434b-af44-0a626253d93a"
+      },
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "fatal: not a git repository (or any of the parent directories): .git\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file