AB_Testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas as pd\n",
    "import datetime\n",
    "from datetime import date, timedelta\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import plotly.io as pio\n",
    "pio.templates.default = \"plotly_dark\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BackGround Info, EDA and Cleaning of the Data\n",
    "\n",
    "<span style=\"font-size: Large;\">\n",
    "\n",
    "- This A/B Testing of the project is about the Instagram Campaign, there are 2 types of ads and we need to find which ad performs well gives better sales, Traffic.\n",
    "\n",
    "- Cleaning data so as to make sure data is in same format for both control and treatment group\n",
    "\n",
    "</span>\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Control_group = pd.read_csv(\"data/control_group.csv\")\n",
    "treatment_group = pd.read_csv(\"data/treatment_group.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Cleaning\n",
    "Control_group.columns = [\"Group Name\", \"Date\", \"Amount Spent\", \n",
    "                        \"Number of Impressions\", \"Reach\", \"Website Clicks\", \n",
    "                        \"Searches Received\", \"Viewed Content\", \"Added to Cart\",\n",
    "                        \"Purchases\"]\n",
    "\n",
    "treatment_group.columns = [\"Group Name\", \"Date\", \"Amount Spent\", \n",
    "                        \"Number of Impressions\", \"Reach\", \"Website Clicks\", \n",
    "                        \"Searches Received\", \"Viewed Content\", \"Added to Cart\",\n",
    "                        \"Purchases\"]\n",
    "\n",
    "# Data Cleaning\n",
    "Control_group[\"Group Name\"] = \"Control\"\n",
    "treatment_group[\"Group Name\"] = \"Treatment\"\n",
    "\n",
    "# Change date to datefomat\n",
    "\n",
    "Control_group[\"Date\"] = pd.to_datetime(Control_group[\"Date\"], format=\"%d.%m.%Y\")\n",
    "treatment_group[\"Date\"] = pd.to_datetime(treatment_group[\"Date\"], format=\"%d.%m.%Y\")\n",
    "\n",
    "# Change all other columns to numeric\n",
    "Control_group.iloc[:, 2:] = Control_group.iloc[:, 2:].apply(pd.to_numeric)\n",
    "treatment_group.iloc[:, 2:] = treatment_group.iloc[:, 2:].apply(pd.to_numeric)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Group Name               0\n",
       " Date                     0\n",
       " Amount Spent             0\n",
       " Number of Impressions    1\n",
       " Reach                    1\n",
       " Website Clicks           1\n",
       " Searches Received        1\n",
       " Viewed Content           1\n",
       " Added to Cart            1\n",
       " Purchases                1\n",
       " dtype: int64,\n",
       " Group Name               0\n",
       " Date                     0\n",
       " Amount Spent             0\n",
       " Number of Impressions    0\n",
       " Reach                    0\n",
       " Website Clicks           0\n",
       " Searches Received        0\n",
       " Viewed Content           0\n",
       " Added to Cart            0\n",
       " Purchases                0\n",
       " dtype: int64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show if there are any NA values\n",
    "\n",
    "Control_group.isna().sum(), treatment_group.isna().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Null Values\n",
    "\n",
    "<span style=\"font-size: Large;\">\n",
    "\n",
    "- As there is one null value in the Control group we will remove as we are using our Overall Evaluation metric as Reach per Amount Spent , Viewed Content per Amount Spent and Purchases per Amount Spent.\n",
    "\n",
    "- If we taken mean of other values in that case it will give the results not accurate\n",
    "\n",
    "</span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the date of NA value in control group\n",
    "Control_group[Control_group[\"Searches Received\"].isna()][\"Date\"]\n",
    "\n",
    "# Remove the row with date 2019-08-05\n",
    "Control_group = Control_group[Control_group[\"Date\"] != \"2019-08-05\"]\n",
    "treatment_group = treatment_group[treatment_group[\"Date\"] != \"2019-08-05\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29, 10) (29, 10)\n",
      "Group Name                       object\n",
      "Date                     datetime64[ns]\n",
      "Amount Spent                      int64\n",
      "Number of Impressions           float64\n",
      "Reach                           float64\n",
      "Website Clicks                  float64\n",
      "Searches Received               float64\n",
      "Viewed Content                  float64\n",
      "Added to Cart                   float64\n",
      "Purchases                       float64\n",
      "dtype: object Group Name                       object\n",
      "Date                     datetime64[ns]\n",
      "Amount Spent                      int64\n",
      "Number of Impressions             int64\n",
      "Reach                             int64\n",
      "Website Clicks                    int64\n",
      "Searches Received                 int64\n",
      "Viewed Content                    int64\n",
      "Added to Cart                     int64\n",
      "Purchases                         int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(Control_group.shape, treatment_group.shape)\n",
    "print(Control_group.dtypes, treatment_group.dtypes)\n",
    "\n",
    "# Change datatype of floats to int in control group\n",
    "\n",
    "Control_group = Control_group.astype({\n",
    "    \"Amount Spent\": int,\n",
    "    \"Number of Impressions\": int,\n",
    "    \"Reach\": int,\n",
    "    \"Website Clicks\": int,\n",
    "    \"Searches Received\": int,\n",
    "    \"Viewed Content\": int,\n",
    "    \"Added to Cart\": int,\n",
    "    \"Purchases\": int\n",
    "})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the Cleaning and EDA of the Data is done we are going discuss about the Plan\n",
    "\n",
    "\n",
    "\n",
    "<span style=\"font-size: Large;\">\n",
    "\n",
    "### We are using the following Guide to perform A/B Test\n",
    "\n",
    "#### Major steps in Running the A/B Tests\n",
    "- Prerequisites\n",
    "- Experiment Design\n",
    "- Running Experiment\n",
    "- Results to Decision\n",
    "- Post Launch Monitoring\n",
    "\n",
    "</span>\n",
    "\n",
    "<span style=\"font-size: small;\"><em>for more info : [Emma Ding: A/B Testing Fundamentals](https://www.youtube.com/watch?v=VpTlNRUcIDo&t=11s)</em></span>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "<span style=\"font-size: Large;\">\n",
    "## Prerequisites\n",
    "\n",
    "this is the first step where we need to establish the Objective and Overall Evaluation Criterion (OEC)\n",
    "- Objective and Key Metrics\n",
    "    - Objective is to increase the purchases and Traffic\n",
    "    - Key Metric\n",
    "        - Reach / Amount Spent\n",
    "        - Viewed Content / Amount Spent\n",
    "        - Purchases/ Amount Spent\n",
    "- Product Groups \n",
    "    - Control and Treatment\n",
    "\n",
    "</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column for Reach per amount spent, viewed content per amount spent, Purchases per amount spent\n",
    "Control_group[\"Reach per amount spent\"] = Control_group[\"Reach\"] / Control_group[\"Amount Spent\"]\n",
    "Control_group[\"Viewed Content per amount spent\"] = Control_group[\"Viewed Content\"] / Control_group[\"Amount Spent\"]\n",
    "Control_group[\"Purchases per amount spent\"] = Control_group[\"Purchases\"] / Control_group[\"Amount Spent\"]\n",
    "\n",
    "treatment_group[\"Reach per amount spent\"] = treatment_group[\"Reach\"] / treatment_group[\"Amount Spent\"]\n",
    "treatment_group[\"Viewed Content per amount spent\"] = treatment_group[\"Viewed Content\"] / treatment_group[\"Amount Spent\"]\n",
    "treatment_group[\"Purchases per amount spent\"] = treatment_group[\"Purchases\"] / treatment_group[\"Amount Spent\"]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "<span style=\"font-size: Large;\">\n",
    "\n",
    "## Experiment Design\n",
    "\n",
    "this is the second step where we establish our point to target specific users or all users\n",
    "- Users to target\n",
    "    - Objective is to increase the purchases and Traffic\n",
    "\n",
    "    - <img src=\"reports/figures/User&apos;s Path to Purchase.png\" alt=\"ALT_TEXT\" width=\"500\" height=\"370\">\n",
    "\n",
    "    - from the above info we are targeting the users of viewed content\n",
    "\n",
    "</span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "<span style=\"font-size: Large;\">\n",
    "\n",
    "## Running Experiment\n",
    "\n",
    "this is the third step where we establish how long to run the experiment but as basic thumb rule we follow these four things \n",
    "- ramp up plan\n",
    "- day of week effect\n",
    "- seasonality effect\n",
    "- primary & Novelity effect\n",
    "\n",
    "</span>\n",
    "\n",
    "<span style=\"font-size: small;\"><em>for more info go through the doc section or source video mentioned above.</em></span>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"font-size: Large;\">\n",
    "\n",
    "## Results and Descision\n",
    "\n",
    "this is the fourth step where we establish descision which one is better\n",
    "- Sanity Checks : make sure the data is reliable \n",
    "- When making decisions we need to consider\n",
    "    - Tradeoffs between different metrics\n",
    "    - Cost of Launching\n",
    "    - Oppurtunity cost\n",
    "\n",
    "</span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group Name</th>\n",
       "      <th>Date</th>\n",
       "      <th>Amount Spent</th>\n",
       "      <th>Number of Impressions</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Website Clicks</th>\n",
       "      <th>Searches Received</th>\n",
       "      <th>Viewed Content</th>\n",
       "      <th>Added to Cart</th>\n",
       "      <th>Purchases</th>\n",
       "      <th>Reach per amount spent</th>\n",
       "      <th>Viewed Content per amount spent</th>\n",
       "      <th>Purchases per amount spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>2280</td>\n",
       "      <td>82702</td>\n",
       "      <td>56930</td>\n",
       "      <td>7016</td>\n",
       "      <td>2290</td>\n",
       "      <td>2159</td>\n",
       "      <td>1819</td>\n",
       "      <td>618</td>\n",
       "      <td>24.969298</td>\n",
       "      <td>0.946930</td>\n",
       "      <td>0.271053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-02</td>\n",
       "      <td>1757</td>\n",
       "      <td>121040</td>\n",
       "      <td>102513</td>\n",
       "      <td>8110</td>\n",
       "      <td>2033</td>\n",
       "      <td>1841</td>\n",
       "      <td>1219</td>\n",
       "      <td>511</td>\n",
       "      <td>58.345475</td>\n",
       "      <td>1.047809</td>\n",
       "      <td>0.290837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-03</td>\n",
       "      <td>2343</td>\n",
       "      <td>131711</td>\n",
       "      <td>110862</td>\n",
       "      <td>6508</td>\n",
       "      <td>1737</td>\n",
       "      <td>1549</td>\n",
       "      <td>1134</td>\n",
       "      <td>372</td>\n",
       "      <td>47.316261</td>\n",
       "      <td>0.661118</td>\n",
       "      <td>0.158771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-04</td>\n",
       "      <td>1940</td>\n",
       "      <td>72878</td>\n",
       "      <td>61235</td>\n",
       "      <td>3065</td>\n",
       "      <td>1042</td>\n",
       "      <td>982</td>\n",
       "      <td>1183</td>\n",
       "      <td>340</td>\n",
       "      <td>31.564433</td>\n",
       "      <td>0.506186</td>\n",
       "      <td>0.175258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-06</td>\n",
       "      <td>3083</td>\n",
       "      <td>109076</td>\n",
       "      <td>87998</td>\n",
       "      <td>4028</td>\n",
       "      <td>1709</td>\n",
       "      <td>1249</td>\n",
       "      <td>784</td>\n",
       "      <td>764</td>\n",
       "      <td>28.542978</td>\n",
       "      <td>0.405125</td>\n",
       "      <td>0.247811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-07</td>\n",
       "      <td>2544</td>\n",
       "      <td>142123</td>\n",
       "      <td>127852</td>\n",
       "      <td>2640</td>\n",
       "      <td>1388</td>\n",
       "      <td>1106</td>\n",
       "      <td>1166</td>\n",
       "      <td>499</td>\n",
       "      <td>50.256289</td>\n",
       "      <td>0.434748</td>\n",
       "      <td>0.196148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-08</td>\n",
       "      <td>1900</td>\n",
       "      <td>90939</td>\n",
       "      <td>65217</td>\n",
       "      <td>7260</td>\n",
       "      <td>3047</td>\n",
       "      <td>2746</td>\n",
       "      <td>930</td>\n",
       "      <td>462</td>\n",
       "      <td>34.324737</td>\n",
       "      <td>1.445263</td>\n",
       "      <td>0.243158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-09</td>\n",
       "      <td>2813</td>\n",
       "      <td>121332</td>\n",
       "      <td>94896</td>\n",
       "      <td>6198</td>\n",
       "      <td>2487</td>\n",
       "      <td>2179</td>\n",
       "      <td>645</td>\n",
       "      <td>501</td>\n",
       "      <td>33.734803</td>\n",
       "      <td>0.774618</td>\n",
       "      <td>0.178102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-10</td>\n",
       "      <td>2149</td>\n",
       "      <td>117624</td>\n",
       "      <td>91257</td>\n",
       "      <td>2277</td>\n",
       "      <td>2475</td>\n",
       "      <td>1984</td>\n",
       "      <td>1629</td>\n",
       "      <td>734</td>\n",
       "      <td>42.464867</td>\n",
       "      <td>0.923220</td>\n",
       "      <td>0.341554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-11</td>\n",
       "      <td>2490</td>\n",
       "      <td>115247</td>\n",
       "      <td>95843</td>\n",
       "      <td>8137</td>\n",
       "      <td>2941</td>\n",
       "      <td>2486</td>\n",
       "      <td>1887</td>\n",
       "      <td>475</td>\n",
       "      <td>38.491165</td>\n",
       "      <td>0.998394</td>\n",
       "      <td>0.190763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-12</td>\n",
       "      <td>2319</td>\n",
       "      <td>116639</td>\n",
       "      <td>100189</td>\n",
       "      <td>2993</td>\n",
       "      <td>1397</td>\n",
       "      <td>1147</td>\n",
       "      <td>1439</td>\n",
       "      <td>794</td>\n",
       "      <td>43.203536</td>\n",
       "      <td>0.494610</td>\n",
       "      <td>0.342389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-13</td>\n",
       "      <td>2697</td>\n",
       "      <td>82847</td>\n",
       "      <td>68214</td>\n",
       "      <td>6554</td>\n",
       "      <td>2390</td>\n",
       "      <td>1975</td>\n",
       "      <td>1794</td>\n",
       "      <td>766</td>\n",
       "      <td>25.292547</td>\n",
       "      <td>0.732295</td>\n",
       "      <td>0.284019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-14</td>\n",
       "      <td>1875</td>\n",
       "      <td>145248</td>\n",
       "      <td>118632</td>\n",
       "      <td>4521</td>\n",
       "      <td>1209</td>\n",
       "      <td>1149</td>\n",
       "      <td>1339</td>\n",
       "      <td>788</td>\n",
       "      <td>63.270400</td>\n",
       "      <td>0.612800</td>\n",
       "      <td>0.420267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>2774</td>\n",
       "      <td>132845</td>\n",
       "      <td>102479</td>\n",
       "      <td>4896</td>\n",
       "      <td>1179</td>\n",
       "      <td>1005</td>\n",
       "      <td>1641</td>\n",
       "      <td>366</td>\n",
       "      <td>36.942682</td>\n",
       "      <td>0.362293</td>\n",
       "      <td>0.131939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-16</td>\n",
       "      <td>2024</td>\n",
       "      <td>71274</td>\n",
       "      <td>42859</td>\n",
       "      <td>5224</td>\n",
       "      <td>2427</td>\n",
       "      <td>2158</td>\n",
       "      <td>1613</td>\n",
       "      <td>438</td>\n",
       "      <td>21.175395</td>\n",
       "      <td>1.066206</td>\n",
       "      <td>0.216403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-17</td>\n",
       "      <td>2177</td>\n",
       "      <td>119612</td>\n",
       "      <td>106518</td>\n",
       "      <td>6628</td>\n",
       "      <td>1756</td>\n",
       "      <td>1642</td>\n",
       "      <td>878</td>\n",
       "      <td>222</td>\n",
       "      <td>48.928801</td>\n",
       "      <td>0.754249</td>\n",
       "      <td>0.101975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-18</td>\n",
       "      <td>1876</td>\n",
       "      <td>108452</td>\n",
       "      <td>96518</td>\n",
       "      <td>7253</td>\n",
       "      <td>2447</td>\n",
       "      <td>2115</td>\n",
       "      <td>1695</td>\n",
       "      <td>243</td>\n",
       "      <td>51.448827</td>\n",
       "      <td>1.127399</td>\n",
       "      <td>0.129531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>2596</td>\n",
       "      <td>107890</td>\n",
       "      <td>81268</td>\n",
       "      <td>3706</td>\n",
       "      <td>2483</td>\n",
       "      <td>2098</td>\n",
       "      <td>908</td>\n",
       "      <td>542</td>\n",
       "      <td>31.305085</td>\n",
       "      <td>0.808166</td>\n",
       "      <td>0.208783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-20</td>\n",
       "      <td>2675</td>\n",
       "      <td>113430</td>\n",
       "      <td>78625</td>\n",
       "      <td>2578</td>\n",
       "      <td>1001</td>\n",
       "      <td>848</td>\n",
       "      <td>1709</td>\n",
       "      <td>299</td>\n",
       "      <td>29.392523</td>\n",
       "      <td>0.317009</td>\n",
       "      <td>0.111776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-21</td>\n",
       "      <td>1803</td>\n",
       "      <td>74654</td>\n",
       "      <td>59873</td>\n",
       "      <td>5691</td>\n",
       "      <td>2711</td>\n",
       "      <td>2496</td>\n",
       "      <td>1460</td>\n",
       "      <td>800</td>\n",
       "      <td>33.207432</td>\n",
       "      <td>1.384359</td>\n",
       "      <td>0.443705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-22</td>\n",
       "      <td>2939</td>\n",
       "      <td>105705</td>\n",
       "      <td>86218</td>\n",
       "      <td>6843</td>\n",
       "      <td>3102</td>\n",
       "      <td>2988</td>\n",
       "      <td>819</td>\n",
       "      <td>387</td>\n",
       "      <td>29.335829</td>\n",
       "      <td>1.016672</td>\n",
       "      <td>0.131677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-23</td>\n",
       "      <td>2496</td>\n",
       "      <td>129880</td>\n",
       "      <td>109413</td>\n",
       "      <td>4410</td>\n",
       "      <td>2896</td>\n",
       "      <td>2496</td>\n",
       "      <td>1913</td>\n",
       "      <td>766</td>\n",
       "      <td>43.835337</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.306891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-24</td>\n",
       "      <td>1892</td>\n",
       "      <td>72515</td>\n",
       "      <td>51987</td>\n",
       "      <td>4085</td>\n",
       "      <td>1274</td>\n",
       "      <td>1149</td>\n",
       "      <td>1146</td>\n",
       "      <td>585</td>\n",
       "      <td>27.477273</td>\n",
       "      <td>0.607294</td>\n",
       "      <td>0.309197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-25</td>\n",
       "      <td>1962</td>\n",
       "      <td>117006</td>\n",
       "      <td>100398</td>\n",
       "      <td>4234</td>\n",
       "      <td>2423</td>\n",
       "      <td>2096</td>\n",
       "      <td>883</td>\n",
       "      <td>386</td>\n",
       "      <td>51.171254</td>\n",
       "      <td>1.068298</td>\n",
       "      <td>0.196738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-26</td>\n",
       "      <td>2233</td>\n",
       "      <td>124897</td>\n",
       "      <td>98432</td>\n",
       "      <td>5435</td>\n",
       "      <td>2847</td>\n",
       "      <td>2421</td>\n",
       "      <td>1448</td>\n",
       "      <td>251</td>\n",
       "      <td>44.080609</td>\n",
       "      <td>1.084192</td>\n",
       "      <td>0.112405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-27</td>\n",
       "      <td>2061</td>\n",
       "      <td>104678</td>\n",
       "      <td>91579</td>\n",
       "      <td>4941</td>\n",
       "      <td>3549</td>\n",
       "      <td>3249</td>\n",
       "      <td>980</td>\n",
       "      <td>605</td>\n",
       "      <td>44.434255</td>\n",
       "      <td>1.576419</td>\n",
       "      <td>0.293547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-28</td>\n",
       "      <td>2421</td>\n",
       "      <td>141654</td>\n",
       "      <td>125874</td>\n",
       "      <td>6287</td>\n",
       "      <td>1672</td>\n",
       "      <td>1589</td>\n",
       "      <td>1711</td>\n",
       "      <td>643</td>\n",
       "      <td>51.992565</td>\n",
       "      <td>0.656340</td>\n",
       "      <td>0.265593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>2375</td>\n",
       "      <td>92029</td>\n",
       "      <td>74192</td>\n",
       "      <td>8127</td>\n",
       "      <td>4891</td>\n",
       "      <td>4219</td>\n",
       "      <td>1486</td>\n",
       "      <td>334</td>\n",
       "      <td>31.238737</td>\n",
       "      <td>1.776421</td>\n",
       "      <td>0.140632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>2324</td>\n",
       "      <td>111306</td>\n",
       "      <td>88632</td>\n",
       "      <td>4658</td>\n",
       "      <td>1615</td>\n",
       "      <td>1249</td>\n",
       "      <td>442</td>\n",
       "      <td>670</td>\n",
       "      <td>38.137694</td>\n",
       "      <td>0.537435</td>\n",
       "      <td>0.288296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group Name       Date  Amount Spent  Number of Impressions   Reach  \\\n",
       "0     Control 2019-08-01          2280                  82702   56930   \n",
       "1     Control 2019-08-02          1757                 121040  102513   \n",
       "2     Control 2019-08-03          2343                 131711  110862   \n",
       "3     Control 2019-08-04          1940                  72878   61235   \n",
       "5     Control 2019-08-06          3083                 109076   87998   \n",
       "6     Control 2019-08-07          2544                 142123  127852   \n",
       "7     Control 2019-08-08          1900                  90939   65217   \n",
       "8     Control 2019-08-09          2813                 121332   94896   \n",
       "9     Control 2019-08-10          2149                 117624   91257   \n",
       "10    Control 2019-08-11          2490                 115247   95843   \n",
       "11    Control 2019-08-12          2319                 116639  100189   \n",
       "12    Control 2019-08-13          2697                  82847   68214   \n",
       "13    Control 2019-08-14          1875                 145248  118632   \n",
       "14    Control 2019-08-15          2774                 132845  102479   \n",
       "15    Control 2019-08-16          2024                  71274   42859   \n",
       "16    Control 2019-08-17          2177                 119612  106518   \n",
       "17    Control 2019-08-18          1876                 108452   96518   \n",
       "18    Control 2019-08-19          2596                 107890   81268   \n",
       "19    Control 2019-08-20          2675                 113430   78625   \n",
       "20    Control 2019-08-21          1803                  74654   59873   \n",
       "21    Control 2019-08-22          2939                 105705   86218   \n",
       "22    Control 2019-08-23          2496                 129880  109413   \n",
       "23    Control 2019-08-24          1892                  72515   51987   \n",
       "24    Control 2019-08-25          1962                 117006  100398   \n",
       "25    Control 2019-08-26          2233                 124897   98432   \n",
       "26    Control 2019-08-27          2061                 104678   91579   \n",
       "27    Control 2019-08-28          2421                 141654  125874   \n",
       "28    Control 2019-08-29          2375                  92029   74192   \n",
       "29    Control 2019-08-30          2324                 111306   88632   \n",
       "\n",
       "    Website Clicks  Searches Received  Viewed Content  Added to Cart  \\\n",
       "0             7016               2290            2159           1819   \n",
       "1             8110               2033            1841           1219   \n",
       "2             6508               1737            1549           1134   \n",
       "3             3065               1042             982           1183   \n",
       "5             4028               1709            1249            784   \n",
       "6             2640               1388            1106           1166   \n",
       "7             7260               3047            2746            930   \n",
       "8             6198               2487            2179            645   \n",
       "9             2277               2475            1984           1629   \n",
       "10            8137               2941            2486           1887   \n",
       "11            2993               1397            1147           1439   \n",
       "12            6554               2390            1975           1794   \n",
       "13            4521               1209            1149           1339   \n",
       "14            4896               1179            1005           1641   \n",
       "15            5224               2427            2158           1613   \n",
       "16            6628               1756            1642            878   \n",
       "17            7253               2447            2115           1695   \n",
       "18            3706               2483            2098            908   \n",
       "19            2578               1001             848           1709   \n",
       "20            5691               2711            2496           1460   \n",
       "21            6843               3102            2988            819   \n",
       "22            4410               2896            2496           1913   \n",
       "23            4085               1274            1149           1146   \n",
       "24            4234               2423            2096            883   \n",
       "25            5435               2847            2421           1448   \n",
       "26            4941               3549            3249            980   \n",
       "27            6287               1672            1589           1711   \n",
       "28            8127               4891            4219           1486   \n",
       "29            4658               1615            1249            442   \n",
       "\n",
       "    Purchases  Reach per amount spent  Viewed Content per amount spent  \\\n",
       "0         618               24.969298                         0.946930   \n",
       "1         511               58.345475                         1.047809   \n",
       "2         372               47.316261                         0.661118   \n",
       "3         340               31.564433                         0.506186   \n",
       "5         764               28.542978                         0.405125   \n",
       "6         499               50.256289                         0.434748   \n",
       "7         462               34.324737                         1.445263   \n",
       "8         501               33.734803                         0.774618   \n",
       "9         734               42.464867                         0.923220   \n",
       "10        475               38.491165                         0.998394   \n",
       "11        794               43.203536                         0.494610   \n",
       "12        766               25.292547                         0.732295   \n",
       "13        788               63.270400                         0.612800   \n",
       "14        366               36.942682                         0.362293   \n",
       "15        438               21.175395                         1.066206   \n",
       "16        222               48.928801                         0.754249   \n",
       "17        243               51.448827                         1.127399   \n",
       "18        542               31.305085                         0.808166   \n",
       "19        299               29.392523                         0.317009   \n",
       "20        800               33.207432                         1.384359   \n",
       "21        387               29.335829                         1.016672   \n",
       "22        766               43.835337                         1.000000   \n",
       "23        585               27.477273                         0.607294   \n",
       "24        386               51.171254                         1.068298   \n",
       "25        251               44.080609                         1.084192   \n",
       "26        605               44.434255                         1.576419   \n",
       "27        643               51.992565                         0.656340   \n",
       "28        334               31.238737                         1.776421   \n",
       "29        670               38.137694                         0.537435   \n",
       "\n",
       "    Purchases per amount spent  \n",
       "0                     0.271053  \n",
       "1                     0.290837  \n",
       "2                     0.158771  \n",
       "3                     0.175258  \n",
       "5                     0.247811  \n",
       "6                     0.196148  \n",
       "7                     0.243158  \n",
       "8                     0.178102  \n",
       "9                     0.341554  \n",
       "10                    0.190763  \n",
       "11                    0.342389  \n",
       "12                    0.284019  \n",
       "13                    0.420267  \n",
       "14                    0.131939  \n",
       "15                    0.216403  \n",
       "16                    0.101975  \n",
       "17                    0.129531  \n",
       "18                    0.208783  \n",
       "19                    0.111776  \n",
       "20                    0.443705  \n",
       "21                    0.131677  \n",
       "22                    0.306891  \n",
       "23                    0.309197  \n",
       "24                    0.196738  \n",
       "25                    0.112405  \n",
       "26                    0.293547  \n",
       "27                    0.265593  \n",
       "28                    0.140632  \n",
       "29                    0.288296  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Control_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group Name</th>\n",
       "      <th>Date</th>\n",
       "      <th>Amount Spent</th>\n",
       "      <th>Number of Impressions</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Website Clicks</th>\n",
       "      <th>Searches Received</th>\n",
       "      <th>Viewed Content</th>\n",
       "      <th>Added to Cart</th>\n",
       "      <th>Purchases</th>\n",
       "      <th>Reach per amount spent</th>\n",
       "      <th>Viewed Content per amount spent</th>\n",
       "      <th>Purchases per amount spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>2280</td>\n",
       "      <td>82702</td>\n",
       "      <td>56930</td>\n",
       "      <td>7016</td>\n",
       "      <td>2290</td>\n",
       "      <td>2159</td>\n",
       "      <td>1819</td>\n",
       "      <td>618</td>\n",
       "      <td>24.969298</td>\n",
       "      <td>0.946930</td>\n",
       "      <td>0.271053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-02</td>\n",
       "      <td>1757</td>\n",
       "      <td>121040</td>\n",
       "      <td>102513</td>\n",
       "      <td>8110</td>\n",
       "      <td>2033</td>\n",
       "      <td>1841</td>\n",
       "      <td>1219</td>\n",
       "      <td>511</td>\n",
       "      <td>58.345475</td>\n",
       "      <td>1.047809</td>\n",
       "      <td>0.290837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-03</td>\n",
       "      <td>2343</td>\n",
       "      <td>131711</td>\n",
       "      <td>110862</td>\n",
       "      <td>6508</td>\n",
       "      <td>1737</td>\n",
       "      <td>1549</td>\n",
       "      <td>1134</td>\n",
       "      <td>372</td>\n",
       "      <td>47.316261</td>\n",
       "      <td>0.661118</td>\n",
       "      <td>0.158771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-04</td>\n",
       "      <td>1940</td>\n",
       "      <td>72878</td>\n",
       "      <td>61235</td>\n",
       "      <td>3065</td>\n",
       "      <td>1042</td>\n",
       "      <td>982</td>\n",
       "      <td>1183</td>\n",
       "      <td>340</td>\n",
       "      <td>31.564433</td>\n",
       "      <td>0.506186</td>\n",
       "      <td>0.175258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Control</td>\n",
       "      <td>2019-08-06</td>\n",
       "      <td>3083</td>\n",
       "      <td>109076</td>\n",
       "      <td>87998</td>\n",
       "      <td>4028</td>\n",
       "      <td>1709</td>\n",
       "      <td>1249</td>\n",
       "      <td>784</td>\n",
       "      <td>764</td>\n",
       "      <td>28.542978</td>\n",
       "      <td>0.405125</td>\n",
       "      <td>0.247811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Group Name       Date  Amount Spent  Number of Impressions   Reach  \\\n",
       "0    Control 2019-08-01          2280                  82702   56930   \n",
       "1    Control 2019-08-02          1757                 121040  102513   \n",
       "2    Control 2019-08-03          2343                 131711  110862   \n",
       "3    Control 2019-08-04          1940                  72878   61235   \n",
       "4    Control 2019-08-06          3083                 109076   87998   \n",
       "\n",
       "   Website Clicks  Searches Received  Viewed Content  Added to Cart  \\\n",
       "0            7016               2290            2159           1819   \n",
       "1            8110               2033            1841           1219   \n",
       "2            6508               1737            1549           1134   \n",
       "3            3065               1042             982           1183   \n",
       "4            4028               1709            1249            784   \n",
       "\n",
       "   Purchases  Reach per amount spent  Viewed Content per amount spent  \\\n",
       "0        618               24.969298                         0.946930   \n",
       "1        511               58.345475                         1.047809   \n",
       "2        372               47.316261                         0.661118   \n",
       "3        340               31.564433                         0.506186   \n",
       "4        764               28.542978                         0.405125   \n",
       "\n",
       "   Purchases per amount spent  \n",
       "0                    0.271053  \n",
       "1                    0.290837  \n",
       "2                    0.158771  \n",
       "3                    0.175258  \n",
       "4                    0.247811  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the two dataframes\n",
    "\n",
    "Combined_dataframe = pd.concat([Control_group, treatment_group], axis = 0)\n",
    "Combined_dataframe.reset_index(drop=True, inplace=True)\n",
    "Combined_dataframe.head()"
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    "                    y=\"Amount Spent\", \n",
    "                    size=\"Amount Spent\", \n",
    "                    color= \"Group Name\", \n",
    "                    trendline=\"ols\",\n",
    "                    title=\"Reach vs Amount Spent in Control and Treatment Group\")\n",
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    "                    size=\"Amount Spent\", \n",
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    "fig = px.scatter(data_frame = Combined_dataframe, \n",
    "                    x=\"Purchases\",\n",
    "                    y=\"Amount Spent\", \n",
    "                    size=\"Amount Spent\", \n",
    "                    color= \"Group Name\", \n",
    "                    trendline=\"ols\",\n",
    "                    title=\"Purchases vs Amount Spent in control and treatment group\")\n",
    "fig.show()"
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  },
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   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "    .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>Purchases per amount spent</th>\n",
       "      <th>Viewed Content per amount spent</th>\n",
       "      <th>Reach per amount spent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group Name</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>Control</th>\n",
       "      <td>0.232042</td>\n",
       "      <td>0.866409</td>\n",
       "      <td>39.168313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treatment</th>\n",
       "      <td>0.202172</td>\n",
       "      <td>0.749462</td>\n",
       "      <td>20.540740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "            Purchases per amount spent  Viewed Content per amount spent  \\\n",
       "Group Name                                                                \n",
       "Control                       0.232042                         0.866409   \n",
       "Treatment                     0.202172                         0.749462   \n",
       "\n",
       "            Reach per amount spent  \n",
       "Group Name                          \n",
       "Control                  39.168313  \n",
       "Treatment                20.540740  "
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     "execution_count": 14,
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    "# average Purchases, viewed count, reach per amount spent in control group vs treatment group plot\n",
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    "Combined_dataframe.groupby(\"Group Name\")[[\"Purchases per amount spent\", \"Viewed Content per amount spent\", \"Reach per amount spent\"]].mean()"
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    "\n",
    "conclusion = Combined_dataframe.groupby(\"Group Name\")[[\"Purchases per amount spent\", \"Viewed Content per amount spent\", \"Reach per amount spent\"]].mean()\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=[\"Viewed Content\", \"Purchases\", \"Reach\"],\n",
    "    y=[conclusion.loc[\"Control\", \"Viewed Content per amount spent\"], conclusion.loc[\"Control\", \"Purchases per amount spent\"], conclusion.loc[\"Control\", \"Reach per amount spent\"]],\n",
    "    name=\"Control\",\n",
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    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=[\"Viewed Content\", \"Purchases\", \"Reach\"],\n",
    "    y=[conclusion.loc[\"Treatment\", \"Viewed Content per amount spent\"], conclusion.loc[\"Treatment\", \"Purchases per amount spent\"], conclusion.loc[\"Treatment\", \"Reach per amount spent\"]],\n",
    "    name=\"Treatment\",\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Performance Metrics per Amount Spent\",\n",
    "    barmode=\"group\",\n",
    "    xaxis_title=\"Metrics\",\n",
    "    yaxis_title=\"Value\"\n",
    ")\n",
    "\n",
    "fig.show()\n",
    "\n"
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    "# using conclusion dataframe plot individual metrics for Viewed Content\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=[\"Viewed Content\"],\n",
    "    y=[conclusion.loc[\"Control\", \"Viewed Content per amount spent\"]],\n",
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    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=[\"Viewed Content\"],\n",
    "    y=[conclusion.loc[\"Treatment\", \"Viewed Content per amount spent\"]],\n",
    "    name=\"Treatment\",\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Mean of Viewed Content per Amount Spent\",\n",
    "    barmode=\"group\",\n",
    "    xaxis_title=\"Metrics\",\n",
    "    yaxis_title=\"Value\"\n",
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    "# using conclusion dataframe plot individual metrics for purchases\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=[\"Purchases\"],\n",
    "    y=[conclusion.loc[\"Control\", \"Purchases per amount spent\"]],\n",
    "    name=\"Control\",\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=[\"Purchases\"],\n",
    "    y=[conclusion.loc[\"Treatment\", \"Purchases per amount spent\"]],\n",
    "    name=\"Treatment\",\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
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    "    barmode=\"group\",\n",
    "    xaxis_title=\"Metrics\",\n",
    "    yaxis_title=\"Value\"\n",
    ")\n",
    "\n",
    "fig.show()\n"
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    "<span style=\"font-size: Large;\">\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "If we observe the above charts clearly we can get to a conlcusion before getting to conclusion let's see what we found from the above charts\n",
    "- The amount spent in Control is 66,818 USD and Treatment is 74,595 USD\n",
    "- Performance Metrics\n",
    "    - Range of Users : for each dollar we spent Control : 39 users and Treatment : 20 users\n",
    "    - User's Viewed Content : for each dollar we spent Control : 0.866 users and Treatment : 0.749 Users\n",
    "    - User's Purchased : for each dollar we spent Control : 0.232 users and Treatment : 0.202 Users\n",
    "\n",
    " By the above info we can say that Control group is perfoming better compared to the Treatment group\n",
    "\n",
    "</span>\n"
   ]
  }
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