GOD1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GOD1_RSM 2023\n",
    "ADVERTISING DATA MODELING \n",
    "CONTACT: mahlaranjeet@gmail.com\n",
    "DR RANJEET SINGH MAHLA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "REQUIREMENTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import streamlit as st\n",
    "import plotly.express as px\n",
    "from numerize.numerize import numerize\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ALOS THE REQUIREMENTS CAN BE MADE VIA \n",
    "pip install -r /Users/rmahla_admin/Desktop/Facebook_Ad_Campaign_Dashboard/requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>date</th>\n",
       "      <th>ad_id</th>\n",
       "      <th>campaign</th>\n",
       "      <th>fb_campaign_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>interest</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Spent</th>\n",
       "      <th>Total_Conversion</th>\n",
       "      <th>Approved_Conversion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>708746</td>\n",
       "      <td>Campaign A</td>\n",
       "      <td>103916</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>7350</td>\n",
       "      <td>1</td>\n",
       "      <td>1.430000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>708749</td>\n",
       "      <td>Campaign A</td>\n",
       "      <td>103917</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>17861</td>\n",
       "      <td>2</td>\n",
       "      <td>1.820000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>708771</td>\n",
       "      <td>Campaign A</td>\n",
       "      <td>103920</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>693</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>708815</td>\n",
       "      <td>Campaign A</td>\n",
       "      <td>103928</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>4259</td>\n",
       "      <td>1</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>708818</td>\n",
       "      <td>Campaign A</td>\n",
       "      <td>103928</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>4133</td>\n",
       "      <td>1</td>\n",
       "      <td>1.290000</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>2022-09-13</td>\n",
       "      <td>1314410</td>\n",
       "      <td>Campaign C</td>\n",
       "      <td>179977</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Female</td>\n",
       "      <td>109</td>\n",
       "      <td>1129773</td>\n",
       "      <td>252</td>\n",
       "      <td>358.189997</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>2022-09-14</td>\n",
       "      <td>1314411</td>\n",
       "      <td>Campaign C</td>\n",
       "      <td>179978</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Female</td>\n",
       "      <td>110</td>\n",
       "      <td>637549</td>\n",
       "      <td>120</td>\n",
       "      <td>173.880003</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>2022-09-15</td>\n",
       "      <td>1314412</td>\n",
       "      <td>Campaign C</td>\n",
       "      <td>179979</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Female</td>\n",
       "      <td>111</td>\n",
       "      <td>151531</td>\n",
       "      <td>28</td>\n",
       "      <td>40.289999</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>2022-09-16</td>\n",
       "      <td>1314414</td>\n",
       "      <td>Campaign C</td>\n",
       "      <td>179981</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Female</td>\n",
       "      <td>113</td>\n",
       "      <td>790253</td>\n",
       "      <td>135</td>\n",
       "      <td>198.710001</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1142</th>\n",
       "      <td>2022-09-17</td>\n",
       "      <td>1314415</td>\n",
       "      <td>Campaign C</td>\n",
       "      <td>179982</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Female</td>\n",
       "      <td>114</td>\n",
       "      <td>513161</td>\n",
       "      <td>114</td>\n",
       "      <td>165.609999</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1143 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date    ad_id    campaign  fb_campaign_id    age  gender  \\\n",
       "0     2021-01-01   708746  Campaign A          103916  30-34    Male   \n",
       "1     2021-01-02   708749  Campaign A          103917  30-34    Male   \n",
       "2     2021-01-03   708771  Campaign A          103920  30-34    Male   \n",
       "3     2021-01-04   708815  Campaign A          103928  30-34    Male   \n",
       "4     2021-01-05   708818  Campaign A          103928  30-34    Male   \n",
       "...          ...      ...         ...             ...    ...     ...   \n",
       "1138  2022-09-13  1314410  Campaign C          179977  45-49  Female   \n",
       "1139  2022-09-14  1314411  Campaign C          179978  45-49  Female   \n",
       "1140  2022-09-15  1314412  Campaign C          179979  45-49  Female   \n",
       "1141  2022-09-16  1314414  Campaign C          179981  45-49  Female   \n",
       "1142  2022-09-17  1314415  Campaign C          179982  45-49  Female   \n",
       "\n",
       "      interest  Impressions  Clicks       Spent  Total_Conversion  \\\n",
       "0           15         7350       1    1.430000                 2   \n",
       "1           16        17861       2    1.820000                 2   \n",
       "2           20          693       0    0.000000                 1   \n",
       "3           28         4259       1    1.250000                 1   \n",
       "4           28         4133       1    1.290000                 1   \n",
       "...        ...          ...     ...         ...               ...   \n",
       "1138       109      1129773     252  358.189997                13   \n",
       "1139       110       637549     120  173.880003                 3   \n",
       "1140       111       151531      28   40.289999                 2   \n",
       "1141       113       790253     135  198.710001                 8   \n",
       "1142       114       513161     114  165.609999                 5   \n",
       "\n",
       "      Approved_Conversion  \n",
       "0                       1  \n",
       "1                       0  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       1  \n",
       "...                   ...  \n",
       "1138                    2  \n",
       "1139                    0  \n",
       "1140                    0  \n",
       "1141                    2  \n",
       "1142                    2  \n",
       "\n",
       "[1143 rows x 12 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/Users/rmahla_admin/Desktop/GOD1_RSM/data/data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LETS HAVE A LOOK ON DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1143 entries, 0 to 1142\n",
      "Data columns (total 12 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   date                 1143 non-null   object \n",
      " 1   ad_id                1143 non-null   int64  \n",
      " 2   campaign             1143 non-null   object \n",
      " 3   fb_campaign_id       1143 non-null   int64  \n",
      " 4   age                  1143 non-null   object \n",
      " 5   gender               1143 non-null   object \n",
      " 6   interest             1143 non-null   int64  \n",
      " 7   Impressions          1143 non-null   int64  \n",
      " 8   Clicks               1143 non-null   int64  \n",
      " 9   Spent                1143 non-null   float64\n",
      " 10  Total_Conversion     1143 non-null   int64  \n",
      " 11  Approved_Conversion  1143 non-null   int64  \n",
      "dtypes: float64(1), int64(7), object(4)\n",
      "memory usage: 107.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ad_id</th>\n",
       "      <th>fb_campaign_id</th>\n",
       "      <th>interest</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Spent</th>\n",
       "      <th>Total_Conversion</th>\n",
       "      <th>Approved_Conversion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.143000e+03</td>\n",
       "      <td>1143.000000</td>\n",
       "      <td>1143.000000</td>\n",
       "      <td>1.143000e+03</td>\n",
       "      <td>1143.000000</td>\n",
       "      <td>1143.000000</td>\n",
       "      <td>1143.000000</td>\n",
       "      <td>1143.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.872611e+05</td>\n",
       "      <td>133783.989501</td>\n",
       "      <td>32.766404</td>\n",
       "      <td>1.867321e+05</td>\n",
       "      <td>33.390201</td>\n",
       "      <td>51.360656</td>\n",
       "      <td>2.855643</td>\n",
       "      <td>0.944007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.939928e+05</td>\n",
       "      <td>20500.308622</td>\n",
       "      <td>26.952131</td>\n",
       "      <td>3.127622e+05</td>\n",
       "      <td>56.892438</td>\n",
       "      <td>86.908418</td>\n",
       "      <td>4.483593</td>\n",
       "      <td>1.737708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.087460e+05</td>\n",
       "      <td>103916.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.700000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.776325e+05</td>\n",
       "      <td>115716.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>6.503500e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.480000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.121185e+06</td>\n",
       "      <td>144549.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.150900e+04</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.121804e+06</td>\n",
       "      <td>144657.500000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2.217690e+05</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>60.025000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.314415e+06</td>\n",
       "      <td>179982.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>3.052003e+06</td>\n",
       "      <td>421.000000</td>\n",
       "      <td>639.949998</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ad_id  fb_campaign_id     interest   Impressions       Clicks  \\\n",
       "count  1.143000e+03     1143.000000  1143.000000  1.143000e+03  1143.000000   \n",
       "mean   9.872611e+05   133783.989501    32.766404  1.867321e+05    33.390201   \n",
       "std    1.939928e+05    20500.308622    26.952131  3.127622e+05    56.892438   \n",
       "min    7.087460e+05   103916.000000     2.000000  8.700000e+01     0.000000   \n",
       "25%    7.776325e+05   115716.000000    16.000000  6.503500e+03     1.000000   \n",
       "50%    1.121185e+06   144549.000000    25.000000  5.150900e+04     8.000000   \n",
       "75%    1.121804e+06   144657.500000    31.000000  2.217690e+05    37.500000   \n",
       "max    1.314415e+06   179982.000000   114.000000  3.052003e+06   421.000000   \n",
       "\n",
       "             Spent  Total_Conversion  Approved_Conversion  \n",
       "count  1143.000000       1143.000000          1143.000000  \n",
       "mean     51.360656          2.855643             0.944007  \n",
       "std      86.908418          4.483593             1.737708  \n",
       "min       0.000000          0.000000             0.000000  \n",
       "25%       1.480000          1.000000             0.000000  \n",
       "50%      12.370000          1.000000             1.000000  \n",
       "75%      60.025000          3.000000             1.000000  \n",
       "max     639.949998         60.000000            21.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.42999995e+00, 1.82000002e+00, 0.00000000e+00, 1.25000000e+00,\n",
       "       1.28999996e+00, 4.76999998e+00, 1.26999998e+00, 1.50000000e+00,\n",
       "       3.15999997e+00, 1.02800002e+01, 5.69999993e-01, 1.69000006e+00,\n",
       "       4.63000011e+00, 2.60999990e+00, 3.04999995e+00, 3.05999994e+00,\n",
       "       2.98000002e+00, 5.52000010e+00, 8.47000015e+00, 1.58200002e+01,\n",
       "       1.47000003e+00, 1.52999997e+00, 1.80699997e+01, 1.61000001e+00,\n",
       "       1.19000006e+00, 9.22000027e+00, 1.50999999e+00, 9.43000007e+00,\n",
       "       6.96000004e+00, 1.22000003e+00, 2.79999995e+00, 1.60000002e+00,\n",
       "       1.12999999e+00, 5.73000002e+00, 9.38000000e+00, 5.63000000e+00,\n",
       "       3.84000003e+00, 1.03999996e+00, 1.46000004e+00, 2.83999991e+00,\n",
       "       1.35000002e+00, 5.75000000e+00, 3.98000002e+00, 4.44999993e+00,\n",
       "       1.32000005e+00, 4.09000003e+00, 1.00300001e+01, 4.25000000e+00,\n",
       "       2.02900003e+01, 1.38999999e+00, 4.44000006e+00, 1.57000005e+00,\n",
       "       1.40999997e+00, 3.33300000e+01, 3.34000009e+00, 8.05000007e+00,\n",
       "       5.21000004e+00, 2.34999996e+00, 1.93299996e+01, 1.45000005e+00,\n",
       "       4.05000007e+00, 3.29799998e+01, 3.12899995e+01, 8.78999996e+00,\n",
       "       9.11999989e+00, 6.26000011e+00, 3.86400000e+01, 2.39999995e-01,\n",
       "       1.09000003e+00, 2.68000007e+00, 3.00999999e+00, 1.58000004e+00,\n",
       "       1.40600004e+01, 2.90000010e+00, 1.59000003e+00, 4.59000015e+00,\n",
       "       5.40000021e-01, 1.34100001e+01, 8.60000014e-01, 1.07700005e+01,\n",
       "       5.59000015e+00, 5.81600004e+01, 1.06199998e+01, 5.46000004e+00,\n",
       "       2.01799998e+01, 1.76379998e+02, 3.43900005e+01, 5.49000001e+00,\n",
       "       1.41999996e+00, 5.19700003e+01, 3.19000006e+00, 1.96000004e+00,\n",
       "       6.00000024e-01, 1.14999998e+00, 3.18000007e+00, 9.80000019e-01,\n",
       "       1.48000005e+00, 2.93999994e+00, 4.60000002e+00, 2.66999996e+00,\n",
       "       1.09600005e+01, 2.35999990e+00, 7.35000014e+00, 9.34000003e+00,\n",
       "       4.61999989e+00, 8.50000000e+00, 2.42000008e+00, 2.95000005e+00,\n",
       "       3.72000003e+00, 4.44999981e+00, 1.30700002e+01, 1.08499997e+01,\n",
       "       1.20600001e+01, 8.86000013e+00, 2.77299995e+01, 7.34999990e+00,\n",
       "       2.65300004e+01, 3.94299998e+01, 4.79300003e+01, 6.03999996e+00,\n",
       "       2.36999989e+00, 6.34000015e+00, 5.32199995e+01, 3.57999995e+01,\n",
       "       1.80220001e+02, 7.64100006e+01, 9.54999995e+00, 1.80200000e+01,\n",
       "       1.33700000e+01, 4.59000003e+00, 2.58600000e+01, 4.90000010e-01,\n",
       "       1.67999995e+00, 2.54199994e+01, 3.31000006e+00, 1.54999995e+00,\n",
       "       5.43999994e+00, 8.16099979e+01, 4.98999989e+00, 3.17099998e+01,\n",
       "       2.55200005e+01, 4.25999999e+00, 1.83000004e+00, 2.80999994e+00,\n",
       "       1.10499997e+01, 7.22999978e+00, 4.26999986e+00, 9.47999990e+00,\n",
       "       2.89000010e+00, 1.05699997e+01, 3.20000005e+00, 9.70000029e-01,\n",
       "       4.37000000e+00, 1.80999994e+00, 3.31999993e+00, 1.17999995e+00,\n",
       "       4.73000002e+00, 1.37999999e+00, 1.25999999e+00, 3.57999992e+00,\n",
       "       6.27999997e+00, 1.21000004e+00, 4.66300001e+01, 3.44400001e+01,\n",
       "       9.21999979e+00, 7.71999991e+00, 1.80000007e-01, 5.38999987e+00,\n",
       "       5.77999985e+00, 9.90000010e-01, 1.27999997e+00, 5.27999997e+00,\n",
       "       1.04999995e+00, 1.34000003e+00, 1.04299998e+01, 6.09999990e+00,\n",
       "       1.96599995e+01, 5.61900008e+01, 6.02999985e+00, 9.59999979e-01,\n",
       "       2.61999994e+00, 1.36000001e+00, 6.05000019e+00, 1.98800003e+01,\n",
       "       4.52999997e+00, 6.77999985e+00, 3.09000003e+00, 3.14000010e+00,\n",
       "       7.54000020e+00, 3.41500001e+01, 3.95999992e+00, 2.65000010e+00,\n",
       "       8.17999983e+00, 4.13000011e+00, 7.20000029e-01, 1.91000004e+01,\n",
       "       2.75000000e+00, 4.12900007e+01, 9.41000032e+00, 1.34099999e+01,\n",
       "       9.49999988e-01, 3.22900000e+01, 1.73000002e+00, 8.89999986e-01,\n",
       "       1.02299999e+01, 4.42999983e+00, 3.82999992e+00, 1.11000001e+00,\n",
       "       5.34999990e+00, 3.95000005e+00, 4.01999998e+00, 1.33000004e+00,\n",
       "       3.08999991e+00, 6.01999998e+00, 1.23000002e+00, 7.70799997e+01,\n",
       "       9.32999992e+00, 1.37000001e+00, 5.90999997e+00, 6.91000009e+00,\n",
       "       9.53999996e+00, 1.73000004e+01, 1.30999994e+00, 4.07999992e+00,\n",
       "       1.79299999e+01, 4.56999993e+00, 2.26999998e+00, 1.02999997e+00,\n",
       "       3.61300004e+01, 1.53999996e+00, 5.90699999e+01, 7.81000006e+00,\n",
       "       2.85000000e+01, 1.00799999e+01, 6.79799998e+01, 1.63999999e+00,\n",
       "       5.29999971e-01, 1.44000006e+00, 3.91999996e+00, 4.94400001e+01,\n",
       "       4.18000007e+00, 3.74999988e+00, 2.73000002e+00, 2.32000005e+00,\n",
       "       6.16999996e+00, 4.94000006e+00, 3.92500010e+01, 1.25100004e+01,\n",
       "       4.55000019e+00, 8.91000009e+00, 4.30000019e+00, 2.86999994e+00,\n",
       "       1.55999994e+00, 3.21000004e+00, 1.51999998e+00, 4.32999992e+00,\n",
       "       1.07000005e+00, 4.65999985e+00, 2.72000003e+00, 4.28999996e+00,\n",
       "       3.25000000e+00, 2.61999989e+00, 1.23900003e+01, 3.15000010e+00,\n",
       "       1.19499998e+01, 1.48000002e+00, 7.26999998e+00, 4.09999996e-01,\n",
       "       6.07999992e+00, 2.09999990e+00, 2.96000004e+00, 1.23699999e+01,\n",
       "       2.54049996e+02, 1.22400000e+02, 8.63300011e+01, 1.61909999e+02,\n",
       "       1.82000005e+00, 2.36769999e+02, 6.39949998e+02, 5.29700001e+01,\n",
       "       3.60150001e+02, 1.65200001e+01, 1.63899997e+02, 2.10360001e+02,\n",
       "       3.57300003e+01, 2.15839999e+02, 3.80000007e+00, 9.68000005e+01,\n",
       "       1.57329998e+02, 2.65699995e+01, 9.30899999e+01, 7.89200002e+01,\n",
       "       1.03150000e+02, 3.65300002e+01, 2.88500000e+01, 2.57709999e+02,\n",
       "       7.22000015e+00, 7.89999998e+00, 6.20600004e+01, 1.41400005e+01,\n",
       "       2.37300003e+01, 1.71000004e+00, 9.44000006e+00, 2.77999992e+01,\n",
       "       6.76500006e+01, 3.24000001e+00, 5.22600001e+01, 1.30400002e+01,\n",
       "       3.83899998e+01, 1.38770000e+02, 2.60380001e+02, 8.54999995e+00,\n",
       "       1.56300002e+01, 6.01999996e+01, 3.41900001e+01, 5.14000011e+00,\n",
       "       2.16559998e+02, 9.30700004e+01, 5.60599992e+01, 5.40999987e+01,\n",
       "       7.80000007e+00, 5.22000003e+00, 3.58550003e+02, 2.80990000e+02,\n",
       "       1.15120001e+02, 2.19770002e+02, 5.20199999e+01, 3.53099999e+01,\n",
       "       6.33200010e+01, 5.42400004e+01, 6.52300000e+01, 4.21000004e+00,\n",
       "       4.86099998e+01, 1.39199997e+01, 1.10599997e+01, 8.74200008e+01,\n",
       "       1.65999997e+00, 6.74000001e+00, 8.22000027e+00, 5.58000004e+00,\n",
       "       9.22999990e+00, 3.42299997e+01, 3.14999998e+00, 2.02099994e+01,\n",
       "       5.94399998e+01, 3.85899997e+01, 2.18499998e+01, 2.23200001e+01,\n",
       "       1.10239999e+02, 2.97500005e+01, 1.84700002e+01, 3.62400003e+01,\n",
       "       7.64399996e+01, 1.18000002e+01, 2.68199998e+01, 6.56700012e+01,\n",
       "       3.22400003e+01, 2.45600000e+02, 1.76400001e+01, 3.85499994e+01,\n",
       "       4.07599995e+01, 6.37000000e+00, 8.97600005e+01, 1.61500001e+01,\n",
       "       1.89129998e+02, 8.52999985e+00, 6.77700013e+01, 9.68999989e+01,\n",
       "       3.71300001e+01, 5.31599996e+01, 1.92930000e+02, 2.36119999e+02,\n",
       "       1.25700002e+01, 1.25800004e+01, 2.80499996e+01, 8.34800012e+01,\n",
       "       3.04799999e+01, 2.02999998e+01, 1.31579998e+02, 1.46299999e+01,\n",
       "       6.44699993e+01, 3.01500006e+01, 6.22500002e+01, 7.80399998e+01,\n",
       "       3.40000010e+00, 1.48300004e+01, 2.24299998e+01, 4.41400003e+01,\n",
       "       6.96600000e+01, 1.62700000e+01, 5.78999990e+00, 4.20800002e+01,\n",
       "       6.76699998e+01, 3.40700009e+01, 4.17199997e+01, 1.95699998e+01,\n",
       "       5.73700000e+01, 1.73699997e+01, 1.00289999e+02, 3.70999992e+00,\n",
       "       2.32099998e+01, 1.77889999e+02, 6.67700001e+01, 3.26800000e+01,\n",
       "       4.20579998e+02, 8.75500007e+01, 2.32370001e+02, 1.50140001e+02,\n",
       "       9.64800007e+01, 9.55100013e+01, 7.23100003e+01, 9.20999992e+00,\n",
       "       2.54250004e+02, 1.11700003e+01, 3.42300003e+01, 2.52600001e+01,\n",
       "       1.13990001e+02, 1.21099998e+02, 9.87000002e+01, 8.80000007e+00,\n",
       "       9.92999995e+00, 5.33899994e+01, 3.76999998e+00, 2.41799999e+01,\n",
       "       7.68999997e+01, 1.45200001e+01, 3.61700005e+01, 3.84400008e+01,\n",
       "       6.19300000e+01, 7.04100008e+01, 3.75999999e+01, 2.05900003e+01,\n",
       "       2.47800002e+01, 2.95399994e+01, 3.78499998e+01, 2.29000002e+00,\n",
       "       2.41900003e+01, 8.51999998e+00, 1.63360000e+02, 5.81400000e+01,\n",
       "       2.93099998e+01, 1.16340000e+02, 6.08999991e+01, 4.00200009e+01,\n",
       "       2.60069998e+02, 3.59470000e+02, 1.05710000e+02, 1.06600001e+01,\n",
       "       1.43300001e+02, 6.50299985e+01, 1.17560000e+02, 1.43430000e+02,\n",
       "       9.65999985e+00, 1.24799999e+02, 9.85500002e+01, 4.07700002e+01,\n",
       "       1.34880000e+02, 1.93599999e+01, 4.30100003e+01, 4.26299998e+01,\n",
       "       7.57899998e+01, 7.71000004e+00, 1.40399998e+01, 3.98599997e+01,\n",
       "       3.67799996e+01, 5.48699995e+01, 8.77899996e+01, 6.73000002e+00,\n",
       "       7.68399993e+01, 1.55399997e+01, 1.76700001e+01, 4.96000001e+01,\n",
       "       5.32699990e+01, 1.26930000e+02, 1.79620001e+02, 3.84200004e+01,\n",
       "       5.85999999e+01, 3.97299998e+01, 1.91100000e+01, 1.58100002e+01,\n",
       "       1.40970002e+02, 6.07600002e+01, 1.35100001e+01, 5.51000000e+01,\n",
       "       1.26300001e+01, 8.13600002e+01, 1.64700000e+01, 3.10300000e+01,\n",
       "       6.07299997e+01, 7.48300014e+01, 7.48000002e+00, 3.64800005e+01,\n",
       "       6.80600005e+01, 7.88000000e+00, 1.23550000e+02, 1.35750001e+02,\n",
       "       1.16880000e+02, 3.65660001e+02, 1.28279999e+02, 1.07799998e+01,\n",
       "       1.33699999e+02, 6.48799995e+01, 6.03380002e+02, 2.20559999e+02,\n",
       "       3.39000000e+01, 3.18970003e+02, 2.48640000e+02, 4.09300001e+01,\n",
       "       1.09250001e+02, 1.34119998e+02, 1.03390000e+02, 1.16560000e+02,\n",
       "       1.20299999e+02, 2.54599997e+01, 3.56799998e+01, 1.31780001e+02,\n",
       "       2.99100002e+01, 4.67900002e+01, 3.18999994e+00, 1.66799998e+01,\n",
       "       1.11100001e+01, 1.27569999e+02, 1.10100000e+01, 3.59999990e+00,\n",
       "       3.36600000e+01, 3.33499999e+01, 1.28610000e+02, 3.39300003e+01,\n",
       "       3.64400003e+01, 5.76999998e+00, 1.81720001e+02, 4.14200001e+01,\n",
       "       5.86799997e+01, 8.03399998e+01, 1.04460000e+02, 8.77300005e+01,\n",
       "       2.53990002e+02, 3.94400001e+01, 3.90499995e+01, 1.69399996e+01,\n",
       "       1.42709999e+02, 2.72999998e+01, 2.93799994e+01, 2.77579999e+02,\n",
       "       4.22840004e+02, 3.85000008e+01, 1.73299998e+01, 3.23099999e+01,\n",
       "       4.19699994e+01, 3.52449999e+02, 2.64590000e+02, 1.58600000e+02,\n",
       "       4.82000005e+00, 3.72600000e+01, 3.95200007e+01, 1.94799998e+01,\n",
       "       1.63800000e+01, 2.26700003e+01, 1.59900002e+01, 1.70500000e+01,\n",
       "       6.44100000e+01, 4.42199995e+01, 3.81800001e+01, 2.68050002e+02,\n",
       "       7.75900003e+01, 4.67800000e+01, 1.53119998e+02, 5.59900003e+01,\n",
       "       3.15400002e+01, 1.47339999e+02, 1.59570002e+02, 1.17629999e+02,\n",
       "       1.71979998e+02, 1.81000004e+01, 1.15700001e+01, 2.44800000e+01,\n",
       "       1.69669998e+02, 6.21400003e+01, 7.66800003e+01, 1.95149998e+02,\n",
       "       3.83600001e+01, 2.45699997e+01, 7.44100003e+01, 3.34699991e+01,\n",
       "       4.22000003e+00, 1.47670001e+02, 6.21400006e+01, 4.56000006e+00,\n",
       "       3.50599998e+01, 2.93100002e+01, 1.69919997e+02, 4.16300000e+01,\n",
       "       7.57000008e+01, 2.56469998e+02, 6.12300003e+02, 5.18400009e+01,\n",
       "       5.47900002e+01, 1.79050001e+02, 1.77400000e+01, 3.75719996e+02,\n",
       "       4.29479998e+02, 9.58500005e+01, 3.81999993e+00, 2.26539999e+02,\n",
       "       5.07200006e+01, 5.45700009e+01, 2.52200004e+01, 9.97000027e+00,\n",
       "       1.23090002e+02, 3.78300002e+01, 3.30399994e+01, 1.05020000e+02,\n",
       "       5.92999995e+00, 3.75000000e+00, 3.43259994e+02, 1.91899997e+01,\n",
       "       6.10299977e+01, 5.61700000e+01, 3.92300003e+01, 9.91799984e+01,\n",
       "       1.61799997e+01, 2.39700004e+01, 2.62000001e+00, 2.77999997e+00,\n",
       "       4.23700004e+01, 2.20499997e+01, 1.03199998e+01, 9.80999994e+00,\n",
       "       6.34500008e+01, 1.05500000e+01, 3.89400004e+01, 3.80299996e+01,\n",
       "       3.14299998e+02, 1.73760000e+02, 3.73200012e+01, 2.62999999e+00,\n",
       "       2.63810001e+02, 1.77109999e+02, 3.43419999e+02, 3.32299994e+01,\n",
       "       2.34659998e+02, 2.97829998e+02, 3.10099996e+01, 8.46599989e+01,\n",
       "       1.15880003e+02, 8.45100002e+01, 1.30599996e+01, 2.36499996e+01,\n",
       "       8.93500003e+01, 1.73000002e+01, 2.33399997e+01, 4.09600004e+01,\n",
       "       5.01100000e+01, 6.67900007e+01, 3.62299993e+01, 6.28699999e+01,\n",
       "       7.15800005e+01, 3.50509996e+02, 2.13689999e+02, 1.09600002e+01,\n",
       "       3.73999996e+01, 2.10820003e+02, 4.24100003e+01, 3.31999993e+01,\n",
       "       7.29100010e+01, 2.93880001e+02, 1.67050000e+02, 1.23800001e+02,\n",
       "       8.57000020e+01, 6.51799997e+01, 4.64300001e+01, 6.85599996e+01,\n",
       "       8.18999994e+00, 4.85499995e+01, 8.22799990e+01, 7.15300010e+01,\n",
       "       1.91419996e+02, 3.21870000e+02, 6.50300003e+01, 1.94809999e+02,\n",
       "       7.84599993e+01, 5.36299987e+01, 2.43769998e+02, 2.62889998e+02,\n",
       "       4.05000019e+00, 1.64099998e+01, 6.63899999e+01, 9.60999989e+00,\n",
       "       2.21200001e+01, 7.80600004e+01, 4.08799993e+01, 8.82100002e+01,\n",
       "       4.89399986e+01, 9.58499989e+01, 1.06930000e+02, 2.54419997e+02,\n",
       "       1.17340001e+02, 3.90259999e+02, 3.40409999e+02, 3.34700004e+01,\n",
       "       6.62799998e+01, 4.81800001e+01, 1.56119999e+02, 2.88330003e+02,\n",
       "       4.03700002e+01, 1.92799998e+01, 3.40300001e+01, 2.96700001e+01,\n",
       "       4.77899991e+01, 1.11650000e+02, 2.43300002e+01, 1.88510000e+02,\n",
       "       3.31400003e+01, 5.98500003e+01, 2.11099997e+01, 3.83500006e+01,\n",
       "       3.08400003e+01, 1.45899999e+01, 1.59199997e+01, 2.42200002e+01,\n",
       "       2.38400001e+02, 1.32730001e+02, 2.01600004e+02, 4.65079998e+02,\n",
       "       2.34939999e+02, 1.95079999e+02, 1.04689999e+02, 2.95549996e+02,\n",
       "       2.26030001e+02, 2.87690003e+02, 3.15900005e+02, 3.32989999e+02,\n",
       "       2.82490001e+02, 1.15660001e+02, 2.47320003e+02, 1.12000002e+01,\n",
       "       2.54600000e+01, 2.99529998e+02, 1.40420001e+02, 1.00880001e+02,\n",
       "       1.33500004e+01, 7.37499996e+01, 7.67599992e+01, 1.45819997e+02,\n",
       "       1.25270001e+02, 3.86299998e+01, 7.13999999e+00, 1.16989998e+02,\n",
       "       1.75970000e+02, 1.05660001e+02, 2.59179999e+02, 4.69200019e+01,\n",
       "       1.87739998e+02, 3.80659995e+02, 5.41700002e+02, 4.09560003e+02,\n",
       "       1.02390000e+02, 6.71300011e+01, 2.28600002e+01, 1.34889999e+02,\n",
       "       3.69069997e+02, 1.98089997e+02, 9.10000005e+01, 2.97910001e+02,\n",
       "       5.02900004e+01, 9.41999984e+00, 2.05200000e+01, 3.81399999e+01,\n",
       "       1.42050003e+02, 4.02300003e+02, 1.59299999e+02, 1.93659999e+02,\n",
       "       6.68399987e+01, 1.78670001e+02, 1.30360001e+02, 1.14860000e+02,\n",
       "       3.19999993e+00, 7.52999985e+00, 2.69000006e+00, 2.73299997e+01,\n",
       "       5.37899997e+01, 6.10099990e+01, 4.66699994e+01, 1.21099998e+01,\n",
       "       3.20900002e+01, 2.36999996e+01, 1.69400002e+01, 4.66499993e+01,\n",
       "       1.26799999e+01, 1.98200001e+01, 2.12600002e+01, 4.07300006e+01,\n",
       "       2.50100002e+01, 1.15199995e+01, 7.70000005e+00, 1.21499996e+01,\n",
       "       1.49600003e+01, 1.49000001e+00, 2.84700000e+01, 1.51500000e+01,\n",
       "       6.02299991e+01, 2.87199996e+01, 3.48699996e+01, 1.35000004e+01,\n",
       "       1.35599999e+01, 9.25999987e+00, 1.79400001e+01, 2.57500002e+01,\n",
       "       3.00800008e+01, 7.26000011e+00, 1.84200000e+01, 5.05000000e+01,\n",
       "       8.21399991e+01, 3.66000004e+01, 1.95800002e+01, 1.36500005e+01,\n",
       "       3.02600001e+01, 1.13680003e+02, 1.14299998e+01, 9.95200024e+01,\n",
       "       1.10780002e+02, 1.20879999e+02, 3.55400003e+01, 1.19640002e+02,\n",
       "       1.11899999e+01, 1.38920001e+02, 1.44800004e+01, 1.63800000e+02,\n",
       "       1.11989999e+02, 1.50500000e+01, 2.37319997e+02, 4.78799995e+01,\n",
       "       1.64640002e+02, 2.00829996e+02, 1.05219997e+02, 1.27320003e+02,\n",
       "       6.86700001e+01, 2.00500002e+01, 9.40999997e+00, 7.75999998e+01,\n",
       "       1.67300000e+01, 2.46749998e+02, 2.23189995e+02, 6.76099992e+01,\n",
       "       3.19000002e+02, 8.29299989e+01, 5.70000000e+01, 2.33110002e+02,\n",
       "       2.17779997e+02, 3.23060007e+02, 8.79900017e+01, 1.05450001e+02,\n",
       "       1.20899998e+02, 3.01049999e+02, 6.23199992e+01, 2.79219995e+02,\n",
       "       1.58050002e+02, 3.60100007e+01, 1.63400005e+01, 1.62639997e+02,\n",
       "       3.33749994e+02, 3.37100005e+01, 2.32590001e+02, 4.76900001e+01,\n",
       "       3.58189997e+02, 1.73880003e+02, 4.02899995e+01, 1.98710001e+02,\n",
       "       1.65609999e+02])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Impressions'].unique()\n",
    "df['Spent'].unique()\n",
    "# THE SAME CAN BE DONE WITH ALL OTHER VARIABLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wd/0cjqfxsd7zjc3gx989xp40980000gt/T/ipykernel_18650/1950083548.py:1: FutureWarning:\n",
      "\n",
      "The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "interest               0.101973\n",
       "fb_campaign_id         0.439439\n",
       "ad_id                  0.462308\n",
       "Approved_Conversion    0.684249\n",
       "Total_Conversion       0.812838\n",
       "Clicks                 0.948514\n",
       "Spent                  0.970386\n",
       "Impressions            1.000000\n",
       "Name: Impressions, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Impressions'].sort_values()\n",
    "# THE SAME CAN BE DONE FOR ALL OTHER VARIABES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wd/0cjqfxsd7zjc3gx989xp40980000gt/T/ipykernel_18650/58359773.py:1: FutureWarning:\n",
      "\n",
      "The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "COORELATION BETWEEN THE TWO VARIABLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Total_Conversion', ylabel='Approved_Conversion'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TOTAL CONVERSIONS VS APPROVED CONVERSIONS\n",
    "sns.scatterplot(x='Total_Conversion',y='Approved_Conversion',data=df)\n",
    "# INFERENCE: THE COORELATION SUGGEST THAT MORE INQUIRY MEANS MORE PEOPLE BROUGHT THE PRODUCT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GENDER NORMALIZED REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rmahla_admin/.pyenv/versions/3.9.2/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "/Users/rmahla_admin/.pyenv/versions/3.9.2/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 611.111x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col=\"gender\")\n",
    "g.map(plt.scatter, \"Total_Conversion\", \"Approved_Conversion\", alpha=.8)\n",
    "g.add_legend();\n",
    "\n",
    "# INFERENCE: IT SEEMS MALE HAVE TENDENCY OF BUYING MORE PRODUCT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AGE NORMALIZED REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rmahla_admin/.pyenv/versions/3.9.2/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "/Users/rmahla_admin/.pyenv/versions/3.9.2/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "/Users/rmahla_admin/.pyenv/versions/3.9.2/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "/Users/rmahla_admin/.pyenv/versions/3.9.2/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1211.11x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col=\"age\")\n",
    "g.map(plt.scatter, \"Total_Conversion\", \"Approved_Conversion\", alpha=.8)\n",
    "g.add_legend();\n",
    "# PEOPLE IN AGE GROUP OF 30-34 BUY MORE PRODUCTS "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THE SIMILAR COORELATION CAN BE SEEN WITH RESPECT TO OTHER VARIABLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>date</th>\n",
       "      <th>ad_id</th>\n",
       "      <th>campaign</th>\n",
       "      <th>fb_campaign_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>interest</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Spent</th>\n",
       "      <th>Total_Conversion</th>\n",
       "      <th>Approved_Conversion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>708746</td>\n",
       "      <td>Campaign A</td>\n",
       "      <td>103916</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>7350</td>\n",
       "      <td>1</td>\n",
       "      <td>1.430000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>708749</td>\n",
       "      <td>Campaign A</td>\n",
       "      <td>103917</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>17861</td>\n",
       "      <td>2</td>\n",
       "      <td>1.820000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>708771</td>\n",
       "      <td>Campaign A</td>\n",
       "      <td>103920</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>693</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>708815</td>\n",
       "      <td>Campaign A</td>\n",
       "      <td>103928</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>4259</td>\n",
       "      <td>1</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>708818</td>\n",
       "      <td>Campaign A</td>\n",
       "      <td>103928</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>4133</td>\n",
       "      <td>1</td>\n",
       "      <td>1.290000</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>2022-09-13</td>\n",
       "      <td>1314410</td>\n",
       "      <td>Campaign C</td>\n",
       "      <td>179977</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Female</td>\n",
       "      <td>109</td>\n",
       "      <td>1129773</td>\n",
       "      <td>252</td>\n",
       "      <td>358.189997</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>2022-09-14</td>\n",
       "      <td>1314411</td>\n",
       "      <td>Campaign C</td>\n",
       "      <td>179978</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Female</td>\n",
       "      <td>110</td>\n",
       "      <td>637549</td>\n",
       "      <td>120</td>\n",
       "      <td>173.880003</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>2022-09-15</td>\n",
       "      <td>1314412</td>\n",
       "      <td>Campaign C</td>\n",
       "      <td>179979</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Female</td>\n",
       "      <td>111</td>\n",
       "      <td>151531</td>\n",
       "      <td>28</td>\n",
       "      <td>40.289999</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>2022-09-16</td>\n",
       "      <td>1314414</td>\n",
       "      <td>Campaign C</td>\n",
       "      <td>179981</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Female</td>\n",
       "      <td>113</td>\n",
       "      <td>790253</td>\n",
       "      <td>135</td>\n",
       "      <td>198.710001</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1142</th>\n",
       "      <td>2022-09-17</td>\n",
       "      <td>1314415</td>\n",
       "      <td>Campaign C</td>\n",
       "      <td>179982</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Female</td>\n",
       "      <td>114</td>\n",
       "      <td>513161</td>\n",
       "      <td>114</td>\n",
       "      <td>165.609999</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1143 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date    ad_id    campaign  fb_campaign_id    age  gender interest  \\\n",
       "0     2021-01-01   708746  Campaign A          103916  30-34    Male       15   \n",
       "1     2021-01-02   708749  Campaign A          103917  30-34    Male       16   \n",
       "2     2021-01-03   708771  Campaign A          103920  30-34    Male       20   \n",
       "3     2021-01-04   708815  Campaign A          103928  30-34    Male       28   \n",
       "4     2021-01-05   708818  Campaign A          103928  30-34    Male       28   \n",
       "...          ...      ...         ...             ...    ...     ...      ...   \n",
       "1138  2022-09-13  1314410  Campaign C          179977  45-49  Female      109   \n",
       "1139  2022-09-14  1314411  Campaign C          179978  45-49  Female      110   \n",
       "1140  2022-09-15  1314412  Campaign C          179979  45-49  Female      111   \n",
       "1141  2022-09-16  1314414  Campaign C          179981  45-49  Female      113   \n",
       "1142  2022-09-17  1314415  Campaign C          179982  45-49  Female      114   \n",
       "\n",
       "      Impressions  Clicks       Spent  Total_Conversion  Approved_Conversion  \n",
       "0            7350       1    1.430000                 2                    1  \n",
       "1           17861       2    1.820000                 2                    0  \n",
       "2             693       0    0.000000                 1                    0  \n",
       "3            4259       1    1.250000                 1                    0  \n",
       "4            4133       1    1.290000                 1                    1  \n",
       "...           ...     ...         ...               ...                  ...  \n",
       "1138      1129773     252  358.189997                13                    2  \n",
       "1139       637549     120  173.880003                 3                    0  \n",
       "1140       151531      28   40.289999                 2                    0  \n",
       "1141       790253     135  198.710001                 8                    2  \n",
       "1142       513161     114  165.609999                 5                    2  \n",
       "\n",
       "[1143 rows x 12 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Campaign A', 'Campaign B', 'Campaign C'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CONVERT STRING FOR CATEGORICAL VARIABLES\n",
    "df['interest'] = df['interest'].apply(str)\n",
    "df['campaign']=df['campaign'].apply(str)\n",
    "df['campaign'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CAMPAIGN WISE ADD RUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='campaign', ylabel='count'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='campaign')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Campaigns vs Conversion')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CAMPAIGN VERSUS APPROVED CONVERSION  \n",
    "plt.bar(df[\"campaign\"], df[\"Approved_Conversion\"])\n",
    "plt.ylabel('Approved_Conversion')\n",
    "plt.xlabel('campaign')\n",
    "plt.title('Campaigns vs Conversion')\n",
    "##campaign 1178 yields more conversions as expected. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AGE NORMALIZED COMPAIGN VERSUS APPROVED CONVERSIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30-34    426\n",
       "45-49    259\n",
       "35-39    248\n",
       "40-44    210\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='campaign', ylabel='Approved_Conversion'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df[\"campaign\"], y=df[\"Approved_Conversion\"], hue=df[\"age\"], data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GENDER NORMALIZED COMPAIGN VERSUS APPROVED CONVERSIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='campaign', ylabel='Approved_Conversion'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oUKHAN6OysrI0btw4PfDAAwX6e3p62n728PCw/WyxWC7alp+fL0kaMWKE1q5dq6lTp6p+/fry8vLSv/71L+Xm5hZaW1ZWlmrVqqX169cXWOfr61u0NwgAQBEQblxcixYttHv3bod/HXzz5s169NFHdf/990s6H15+++23S9aRmpqqcuXKKSgoyKG1AADwd4QbFzd69Gjde++9qlOnjv71r3/Jzc1NO3fu1E8//aRXXnml2Ntt0KCBVqxYoYiICFksFo0aNcp2VKcwHTt2VFhYmLp3767JkycrODhYf/zxh1atWqX7779fLVu2LHYtAAD8Hd+WcnHh4eH6z3/+oy+//FKtWrXSLbfcotdee01169a9ou1Onz5dVapU0a233qqIiAiFh4erRYsWF+1vsVj0+eef64477lBUVJSCg4PVq1cv/f7777ZrfAAAcASLYRiGs4u42jIzM1W5cmWdPHlSPj4+tvYzZ84oOTlZ119/vd31KHAeficAiqtPnz5KS0uTdP5LEYsWLXJyRc7hSp/Dxf5+/xNHbgAAgEtxarjZuHGjIiIiFBAQIIvFopUrVxZ57ObNm1WuXDk1a9asxOoDAABlj1MvKM7OzlZISIj69+9f6FeVL+bEiRPq16+fOnToYDvUBgBAaXW1H778d+dOVJPk/v8//+HUWsw+FLq4nBpuOnfurM6dO5seN3DgQPXp00fu7u6mjvYAAADXV+auuZk/f74OHDhg9+iAy8nJyVFmZqbd61KuwWusSy1+FwAAs8pUuNm7d6+ef/55vf/++ypXrugHnWJjY1W5cmXbKzAwsNB+F+7Ie/r0aYfUiyt34Xfx97slAwBwKWXmJn55eXnq06ePxo0bp+DgYFNjY2JiFB0dbVvOzMwsNOC4u7vL19dX6enpkiRvb2/bYwdwdRmGodOnTys9PV2+vr5yd3d3dkkAgDKizISbU6dOadu2bdqxY4ftqdP5+fkyDEPlypXTl19+qbvuuqvQsVar1fYk6svx9/eXJFvAgXP5+vraficAABRFmQk3Pj4++vFH+6us33rrLX311VdatmyZrr/+eofMY7FYVKtWLdWsWVNnz551yDZRPB4eHhyxAQCY5tRwk5WVpX379tmWk5OTlZSUpKpVq6pOnTqKiYnR4cOHtXDhQrm5ualJkyZ242vWrClPT88C7Y7g7u7OH1YAAMogp4abbdu2qX379rblC9fFREZGKj4+XkeOHFFKSoqzygMAXKHQZxc6bW6f41m2b80cOZ7l1Fo+ruS0qa9JTg037dq1u+RXfePj4y85fuzYsRo7dqxjiwIAAGVamfoqOAAAwOUQbgAAgEsh3AAAAJdCuAEAAC6FcAMAAFwK4QYAALgUwg0AAHAphBsAAOBSCDcAAMClEG4AAIBLIdwAAACXQrgBAAAuhXADAABcCuEGAAC4FMINAABwKYQbAADgUgg3AADApRBuAACASyHcAAAAl0K4AQAALoVwAwAAXArhBgAAuBTCDQAAcCmEGwAA4FIINwAAwKUQbgAAgEsh3AAAAJdCuAEAAC6FcAMAAFwK4QYAALgUwg0AAHAphBsAAOBSnBpuNm7cqIiICAUEBMhisWjlypWX7L9ixQp16tRJNWrUkI+Pj8LCwrRmzZqrUywAACgTnBpusrOzFRISori4uCL137hxozp16qTPP/9ciYmJat++vSIiIrRjx44SrhQAAJQV5YozaO/evfr666+Vnp6u/Px8u3WjR48u8nY6d+6szp07F7n/jBkz7JYnTpyoTz75RJ999pmaN29e5O0AAADXZTrczJkzR08++aSqV68uf39/WSwW2zqLxWIq3Fyp/Px8nTp1SlWrVr1kv5ycHOXk5NiWMzMzS7o0AADgJKbDzSuvvKIJEyZo5MiRJVGPKVOnTlVWVpZ69OhxyX6xsbEaN27cVaoKAAA4k+lrbo4fP66HHnqoJGoxZdGiRRo3bpw+/PBD1axZ85J9Y2JidPLkSdvr4MGDV6lKAABwtZkONw899JC+/PLLkqilyJYsWaLHHntMH374oTp27HjZ/larVT4+PnYvAADgmkyflqpfv75GjRqlb7/9VjfffLM8PDzs1g8ZMsRhxRVm8eLF6t+/v5YsWaKuXbuW6FwAAKDsMR1u3nnnHVWsWFEbNmzQhg0b7NZZLBZT4SYrK0v79u2zLScnJyspKUlVq1ZVnTp1FBMTo8OHD2vhwoWSzp+KioyM1MyZM9WmTRulpqZKkry8vFS5cmWzbwUAALgg0+EmOTnZYZNv27ZN7du3ty1HR0dLkiIjIxUfH68jR44oJSXFtv6dd97RuXPnNGjQIA0aNMjWfqE/AACwV9WaV+jPrqxY97m5wDAMSbL7OrgZ7dq1s22jMP8MLOvXry/WPAAAXKteaH7C2SVcdcW6Q/HChQt18803y8vLS15eXmratKnee+89R9cGAABgmukjN9OnT9eoUaM0ePBgtW3bVpK0adMmDRw4UEePHtWwYcMcXiQAAEBRmQ43b7zxhmbNmqV+/frZ2u677z7ddNNNGjt2LOEGAAA4lenTUkeOHNGtt95aoP3WW2/VkSNHHFIUAABAcZkON/Xr19eHH35YoH3p0qVq0KCBQ4oCAAAoLtOnpcaNG6eePXtq48aNtmtuNm/erISEhEJDDwAAwNVkOtw8+OCD+u677/Taa69p5cqVkqRGjRpp69atat68uaPrAwCgWPI9KhT6M1xfse5zExoaqvfff9/RtQAA4DBZDTs7uwQ4SZHCTWZmpu1hk5mZmZfsy0MpAQCAMxUp3FSpUkVHjhxRzZo15evrW+gdiQ3DkMViUV7etXFrZwAAUDoVKdx89dVXqlq1qiTp66+/LtGCAAAArkSRws2dd95Z6M8AAACljen73KxevVqbNm2yLcfFxalZs2bq06ePjh8/7tDiAAAAzDIdbp599lnbRcU//vijoqOj1aVLFyUnJys6OtrhBQIAAJhh+qvgycnJaty4sSRp+fLlioiI0MSJE7V9+3Z16dLF4QUCAACYYfrITfny5XX69GlJ0rp163T33XdLkqpWrXrZr4kDAACUNNNHbtq2bavo6Gi1bdtWW7du1dKlSyVJe/bs0XXXXefwAgEAAMwwfeQmLi5OHh4eWrZsmWbNmqXatWtLkr744gvdc889Di8QAADADFNHbs6dO6f169drzpw58vf3t1v32muvObQwAACA4jB15KZcuXIaOHCgcnJySqoeAACAK2L6tFTr1q21Y8eOkqgFAADgipm+oPipp57S8OHDdejQIYWGhqpCBfvHyDdt2tRhxQEAAJhlOtz06tVLkjRkyBBbm8Vi4cGZAACgVCjWTfwAAABKK9Phpm7duiVRBwAAgEOYvqBYkt577z21bdtWAQEB+v333yVJM2bM0CeffOLQ4gAAAMwyHW5mzZple1jmiRMnbNfY+Pr6asaMGY6uDwAAwBTT4eaNN97QnDlz9OKLL8rd3d3W3rJlS/34448OLQ4AAMAs0+EmOTlZzZs3L9ButVqVnZ3tkKIAAACKy3S4uf7665WUlFSgffXq1WrUqJEjagIAACg209+Wio6O1qBBg3TmzBkZhqGtW7dq8eLFio2N1bvvvlsSNQIAABSZ6XDz2GOPycvLSy+99JJOnz6tPn36KCAgQDNnzrTd4A8AAMBZivVV8L59+2rv3r3KyspSamqqDh06pAEDBpjezsaNGxUREaGAgABZLBatXLnysmPWr1+vFi1ayGq1qn79+oqPjzf/BgAAgMsyHW5eeeUV212Kvb29VbNmzWJPnp2drZCQEMXFxRWpf3Jysrp27ar27dsrKSlJzzzzjB577DGtWbOm2DUAAADXYvq01EcffaQxY8aoTZs2evjhh9WjRw9Vr169WJN37txZnTt3LnL/2bNn6/rrr9e0adMkSY0aNdKmTZv02muvKTw8/KLjcnJylJOTY1vOzMwsVr0AAKD0M33kZufOnfrhhx/Url07TZ06VQEBAeratasWLVqk06dPl0SNNlu2bFHHjh3t2sLDw7Vly5ZLjouNjVXlypVtr8DAwJIsEwAAOFGxrrm56aabNHHiRB04cEBff/21goKC9Mwzz8jf39/R9dlJTU2Vn5+fXZufn58yMzP1119/XXRcTEyMTp48aXsdPHiwROsEAADOY/q01D9VqFBBXl5eKl++vE6dOuWImhzOarXKarU6uwwAAHAVFOvITXJysiZMmKCbbrpJLVu21I4dOzRu3DilpqY6uj47/v7+SktLs2tLS0uTj4+PvLy8SnRuAABQNpg+cnPLLbfo+++/V9OmTRUVFaXevXurdu3aJVFbAWFhYfr888/t2tauXauwsLCrMj8AACj9TIebDh06aN68eWrcuPEVT56VlaV9+/bZlpOTk5WUlKSqVauqTp06iomJ0eHDh7Vw4UJJ0sCBA/Xmm2/queeeU//+/fXVV1/pww8/1KpVq664FgAA4BpMh5sJEyY4bPJt27apffv2tuXo6GhJUmRkpOLj43XkyBGlpKTY1l9//fVatWqVhg0bppkzZ+q6667Tu+++e8mvgQMAgGuL6XCTl5en+Ph4JSQkKD09Xfn5+Xbrv/rqqyJvq127djIM46LrC7v7cLt27bRjx44izwEAAK4tpsPN0KFDFR8fr65du6pJkyayWCwlURcAAECxmA43S5Ys0YcffqguXbqURD0AAABXxPRXwcuXL6/69euXRC0AAABXzHS4GT58uGbOnHnJa2UAAACcxfRpqU2bNunrr7/WF198oZtuukkeHh5261esWOGw4gAAAMwyHW58fX11//33l0QtAAAAV8x0uJk/f35J1AEAAOAQxX5wZkZGhnbv3i1JatiwoWrUqOGwogAAAIrL9AXF2dnZ6t+/v2rVqqU77rhDd9xxhwICAjRgwACdPn26JGoEAAAoMtPhJjo6Whs2bNBnn32mEydO6MSJE/rkk0+0YcMGDR8+vCRqBAAAKDLTp6WWL1+uZcuWqV27dra2Ll26yMvLSz169NCsWbMcWR8AAIAppo/cnD59Wn5+fgXaa9asyWkpAADgdKbDTVhYmMaMGaMzZ87Y2v766y+NGzdOYWFhDi0OAADALNOnpWbOnKnw8HBdd911CgkJkSTt3LlTnp6eWrNmjcMLBAAAMMN0uGnSpIn27t2rDz74QL/++qskqXfv3urbt6+8vLwcXiAAAIAZxbrPjbe3tx5//HFH1wIAAHDFinzNTWJiotq3b6/MzMwC606ePKn27dtr586dDi0OAADArCKHm2nTpumuu+6Sj49PgXWVK1dWp06dNGXKFIcWBwAAYFaRw813332nbt26XXR9RESEvvnmG4cUBQAAUFxFDjeHDx9WpUqVLrq+YsWKOnLkiEOKAgAAKK4ih5saNWrYHpRZmF9//VXVq1d3SFEAAADFVeRw07FjR02YMKHQdYZhaMKECerYsaPDCgMAACiOIn8V/KWXXlJoaKjatGmj4cOHq2HDhpLOH7GZNm2a9uzZo/j4+JKqEwAAoEiKHG5uuOEGrVu3To8++qh69eoli8Ui6fxRm8aNG2vt2rWqX79+iRUKAABQFKZu4teyZUv99NNPSkpK0t69e2UYhoKDg9WsWbMSKg8AAMCcYt2huFmzZpcNND4+PkpKSlK9evWKMwUAAECxmH4qeFEZhlFSmwYAALioEgs3AAAAzkC4AQAALoVwAwAAXEqJhZsLXxUHAAC4mkrFBcVxcXEKCgqSp6en2rRpo61bt16y/4wZM9SwYUN5eXkpMDBQw4YN05kzZ660ZAAA4AJKLNx88cUXql279mX7LV26VNHR0RozZoy2b9+ukJAQhYeHKz09vdD+ixYt0vPPP68xY8Zo165dmjt3rpYuXaoXXnjB0W8BAACUQUW6z010dHSRNzh9+nRJ0m233Vbk/o8//riioqIkSbNnz9aqVas0b948Pf/88wX6f/PNN2rbtq369OkjSQoKClLv3r313XffFblGAADguooUbnbs2GG3vH37dp07d872fKk9e/bI3d1doaGhpibPzc1VYmKiYmJibG1ubm7q2LGjtmzZUuiYW2+9Ve+//762bt2q1q1b68CBA/r888/1yCOPXHSenJwc5eTk2JYzMzNN1QkAAMqOIoWbr7/+2vbz9OnTValSJS1YsEBVqlSRJB0/flxRUVG6/fbbTU1+9OhR5eXlyc/Pz67dz89Pv/76a6Fj+vTpo6NHj+q2226TYRg6d+6cBg4ceMnTUrGxsRo3bpyp2gAAQNlk+pqbadOmKTY21hZsJKlKlSp65ZVXNG3aNIcWV5j169dr4sSJeuutt7R9+3atWLFCq1at0vjx4y86JiYmRidPnrS9Dh48WOJ1AgAA5zD9bKnMzExlZGQUaM/IyNCpU6dMbat69epyd3dXWlqaXXtaWpr8/f0LHTNq1Cg98sgjeuyxxyRJN998s7Kzs/Xvf/9bL774otzcCuY1q9Uqq9VqqjYAAFA2mT5yc//99ysqKkorVqzQoUOHdOjQIS1fvlwDBgzQAw88YGpb5cuXV2hoqBISEmxt+fn5SkhIUFhYWKFjTp8+XSDAuLu7S+J5VgAAoBhHbmbPnq0RI0aoT58+Onv27PmNlCunAQMGaMqUKaYLiI6OVmRkpFq2bKnWrVtrxowZys7Otn17ql+/fqpdu7ZiY2MlSREREZo+fbqaN2+uNm3aaN++fRo1apQiIiJsIQcAAFy7TIcbb29vvfXWW5oyZYr2798vSbrhhhtUoUKFYhXQs2dPZWRkaPTo0UpNTVWzZs20evVq20XGKSkpdkdqXnrpJVksFr300ks6fPiwatSooYiICE2YMKFY8wMAANdiMYp5Lmffvn3av3+/7rjjDnl5eckwjDLzyIXMzExVrlxZJ0+elI+Pj7PLAQCXFfrsQmeXUCp8XMn8mQ1XVGf0j1c0vqh/v01fc/Pnn3+qQ4cOCg4OVpcuXXTkyBFJ0oABAzR8+PDiVwwAAOAApsPNsGHD5OHhoZSUFHl7e9vae/bsqdWrVzu0OAAAALNMX3Pz5Zdfas2aNbruuuvs2hs0aKDff//dYYUBAAAUh+kjN9nZ2XZHbC44duwY95IBAABOZzrc3H777Vq48H8XiFksFuXn52vy5Mlq3769Q4sDAAAwy/RpqcmTJ6tDhw7atm2bcnNz9dxzz+nnn3/WsWPHtHnz5pKoEQAAoMhMH7lp0qSJ9uzZo9tuu03dunVTdna2HnjgAe3YsUM33HBDSdQIAABQZKaP3EhS5cqV9eKLLzq6FgAAgCtm+shN/fr1NXbsWO3du7ck6gEAALgipsPNoEGDtGrVKjVs2FCtWrXSzJkzlZqaWhK1AQAAmFasm/h9//33+vXXX9WlSxfFxcUpMDBQd999t923qAAAAJzBdLi5IDg4WOPGjdOePXv03//+VxkZGbYneQMAADhLsS4ovmDr1q1atGiRli5dqszMTD300EOOqgsAAKBYTIebPXv26IMPPtDixYuVnJysu+66S5MmTdIDDzygihUrlkSNAAAARWY63Nx4441q1aqVBg0apF69esnPz68k6gIAACgW0+Fm9+7datCgQUnUAgAAcMVMh5sLwSYxMVG7du2SJDVu3FgtWrRwbGUAAADFYDrcpKenq2fPntqwYYN8fX0lSSdOnFD79u21ZMkS1ahRw9E1AgAAFJnpr4I//fTTysrKsj0s89ixY/rpp5+UmZmpIUOGlESNAAAARWb6yM3q1au1bt06NWrUyNbWuHFjxcXF6e6773ZocQAAAGaZPnKTn58vDw+PAu0eHh7Kz893SFEAAADFZTrc3HXXXRo6dKj++OMPW9vhw4c1bNgwdejQwaHFAQAAmGU63Lz55pvKzMxUUFCQbrjhBt1www26/vrrlZmZqTfeeKMkagQAACgy09fcBAYGavv27Vq3bp1+/fVXSVKjRo3UsWNHhxcHAABglqlwc/bsWXl5eSkpKUmdOnVSp06dSqouAACAYjF1WsrDw0N16tRRXl5eSdUDAABwRUyflnrxxRf1wgsv6L333lPVqlVLoiagSIYOHaqMjAxJUo0aNTRz5kwnVwQAKA1Mh5s333xT+/btU0BAgOrWrasKFSrYrd++fbvDikPh+KN+XkZGhtLS0pxdBgCglDEdbrp3714CZcAM/qgDAHBxpsPNmDFjSqIOAAAAhzAdbi7Ytm2b3VPBQ0NDHVYUAABAcZkON4cOHVLv3r21efNmu6eC33rrrVqyZImuu+46R9cIAABQZKbvUPzYY4/p7Nmz2rVrl+2p4Lt27VJ+fr4ee+yxYhURFxenoKAgeXp6qk2bNtq6desl+584cUKDBg1SrVq1ZLVaFRwcrM8//7xYcwMAANdi+sjNhg0b9M0336hhw4a2toYNG+qNN97Q7bffbrqApUuXKjo6WrNnz1abNm00Y8YMhYeHa/fu3apZs2aB/rm5uerUqZNq1qypZcuWqXbt2vr9999tR5EAAMC1rViPXzh79myB9ry8PAUEBJguYPr06Xr88ccVFRUlSZo9e7ZWrVqlefPm6fnnny/Qf968eTp27Ji++eYb29PJg4KCLjlHTk6OcnJybMuZmZmm6wQAAGWD6dNSU6ZM0dNPP61t27bZ2rZt26ahQ4dq6tSppraVm5urxMREu+dSubm5qWPHjtqyZUuhYz799FOFhYVp0KBB8vPzU5MmTTRx4sRL3jU5NjZWlStXtr0CAwNN1QkAAMoO00duHn30UZ0+fVpt2rRRuXLnh587d07lypVT//791b9/f1vfY8eOXXJbR48eVV5envz8/Oza/fz8bA/l/KcDBw7oq6++Ut++ffX5559r3759euqpp3T27NmLfk09JiZG0dHRtuXMzEwCDgAALsp0uJkxY0YJlFF0+fn5qlmzpt555x25u7srNDRUhw8f1pQpUy4abqxWq6xW61WuFAAAOIPpcBMZGemwyatXry53d/cCd9tNS0uTv79/oWNq1aolDw8Pubu729oaNWqk1NRU5ebmqnz58g6rDwAAlD2mr7mRzl88vGzZMo0fP17jx4/X8uXLde7cOdPbKV++vEJDQ5WQkGBry8/PV0JCgsLCwgod07ZtW+3bt0/5+fm2tj179qhWrVoEGwAAYD7c/PzzzwoODlZkZKQ+/vhjffzxx4qMjFSDBg30008/mS4gOjpac+bM0YIFC7Rr1y49+eSTys7Otn17ql+/foqJibH1f/LJJ3Xs2DENHTpUe/bs0apVqzRx4kQNGjTI9NwAAMD1mD4t9dhjj+mmm27Stm3bVKVKFUnS8ePH9eijj+rf//63vvnmG1Pb69mzpzIyMjR69GilpqaqWbNmWr16te0i45SUFLm5/S+DBQYGas2aNRo2bJiaNm2q2rVra+jQoRo5cqTZtwIAAFyQ6XCTlJRkF2wkqUqVKpowYYJatWpVrCIGDx6swYMHF7pu/fr1BdrCwsL07bffFmsuAADg2kyflgoODi5wAbAkpaenq379+g4pCgAAoLhMh5vY2FgNGTJEy5Yt06FDh3To0CEtW7ZMzzzzjCZNmqTMzEzbCwAA4GozfVrq3nvvlST16NFDFotFkmQYhiQpIiLCtmyxWC5512AAAICSYDrcfP311xdd98MPP6hp06ZXVBAAAMCVMB1u7rzzTrvlU6dOafHixXr33XeVmJjI0RoAAOBUxbqJnyRt3LhRkZGRqlWrlqZOnaq77rqLbzABAACnM3XkJjU1VfHx8Zo7d64yMzPVo0cP5eTkaOXKlWrcuHFJ1QgAAFBkRT5yExERoYYNG+qHH37QjBkz9Mcff+iNN94oydoAAABMK/KRmy+++EJDhgzRk08+qQYNGpRkTQAAAMVW5CM3mzZt0qlTpxQaGqo2bdrozTff1NGjR0uyNgAAANOKHG5uueUWzZkzR0eOHNETTzyhJUuWKCAgQPn5+Vq7dq1OnTpVknUCAAAUielvS1WoUEH9+/fXpk2b9OOPP2r48OF69dVXVbNmTd13330lUSMAAECRFfur4JLUsGFDTZ48WYcOHdLixYsdVRMAAECxmb6JX2Hc3d3VvXt3de/e3RGbA1DGDB06VBkZGZKkGjVqaObMmU6uCMC1zCHhBsC1LSMjQ2lpac4uAwAkXeFpKQAAgNKGcAMAAFwK4QYAALgUwg0AAHAphBsAAOBSCDcAAMClEG4AAIBLIdwAAACXwk38iin02YVOm9vneJYtlR45nuXUWhKn9HPa3AAAFIZwAwAOxKMoAOcj3ACAA/EoCsD5uOYGAAC4FMINAABwKYQbAADgUgg3AADApXBBMa5Iyss3O23ucyeqSXL//5//cGotdUb/6LS5AQD2SkW4iYuL05QpU5SamqqQkBC98cYbat269WXHLVmyRL1791a3bt20cuXKki8UKMW499L/cP8l4Nrm9NNSS5cuVXR0tMaMGaPt27crJCRE4eHhSk9Pv+S43377TSNGjNDtt99+lSoFAABlgdPDzfTp0/X4448rKipKjRs31uzZs+Xt7a158+ZddExeXp769u2rcePGqV69elexWgAAUNo5Ndzk5uYqMTFRHTt2tLW5ubmpY8eO2rJly0XHvfzyy6pZs6YGDBhQpHlycnKUmZlp9wIAAK7JqeHm6NGjysvLk5+fn127n5+fUlNTCx2zadMmzZ07V3PmzCnyPLGxsapcubLtFRgYeEV1AwCA0svpp6XMOHXqlB555BHNmTNH1atXL/K4mJgYnTx50vY6ePBgCVYJAACcyanflqpevbrc3d0LPIclLS1N/v7+Bfrv379fv/32myIiImxt+fn5kqRy5cpp9+7duuGGGwqMs1qtslqtDq4eAACURk49clO+fHmFhoYqISHB1pafn6+EhASFhYUV6H/jjTfqxx9/VFJSku113333qX379kpKSuJ0EwAAcP59bqKjoxUZGamWLVuqdevWmjFjhrKzsxUVFSVJ6tevn2rXrq3Y2Fh5enqqSZMmduN9fX0lqUA7AAC4Njk93PTs2VMZGRkaPXq0UlNT1axZM61evdp2kXFKSorc3MrUpUEAnIw7Z5/HnbNxrXJ6uJGkwYMHa/DgwYWuW79+/SXHxsfHO74gAABQZnFIBAAAuBTCDQAAcCmEGwAA4FIINwAAwKUQbgAAgEspFd+WAlC25XtUKPRnAHAGwg2AK5bVsLOzSwAAG05LAQAAl0K4AQAALoVwAwAAXArhBgAAuBQuKAYAB6pqzSv0ZwBXD+EGABzoheYnnF0CcM3jtBQAAHAphBsAAOBSCDcAAMClEG4AAIBLIdwAAACXwrelyiAeUggAwMURbsogHlIIAMDFcVoKAAC4FMINAABwKYQbAADgUgg3AADApRBuAACASyHcAAAAl0K4AQAALoVwAwAAXArhBgAAuBTCDQAAcCmEGwAA4FIINwAAwKWUinATFxenoKAgeXp6qk2bNtq6detF+86ZM0e33367qlSpoipVqqhjx46X7A8AAK4tTg83S5cuVXR0tMaMGaPt27crJCRE4eHhSk9PL7T/+vXr1bt3b3399dfasmWLAgMDdffdd+vw4cNXuXIAAFAaOT3cTJ8+XY8//riioqLUuHFjzZ49W97e3po3b16h/T/44AM99dRTatasmW688Ua9++67ys/PV0JCwkXnyMnJUWZmpt0LZV9Va56q/f+rqjXP2eUAAEqJcs6cPDc3V4mJiYqJibG1ubm5qWPHjtqyZUuRtnH69GmdPXtWVatWvWif2NhYjRs37orrRenyQvMTzi4BAFAKOfXIzdGjR5WXlyc/Pz+7dj8/P6WmphZpGyNHjlRAQIA6dux40T4xMTE6efKk7XXw4MErqhsAAJReTj1yc6VeffVVLVmyROvXr5enp+dF+1mtVlmt1qtYGQAAcBanhpvq1avL3d1daWlpdu1paWny9/e/5NipU6fq1Vdf1bp169S0adOSLBMAAJQhTj0tVb58eYWGhtpdDHzh4uCwsLCLjps8ebLGjx+v1atXq2XLllejVAAAUEY4/bRUdHS0IiMj1bJlS7Vu3VozZsxQdna2oqKiJEn9+vVT7dq1FRsbK0maNGmSRo8erUWLFikoKMh2bU7FihVVsWJFp70PAABQOjg93PTs2VMZGRkaPXq0UlNT1axZM61evdp2kXFKSorc3P53gGnWrFnKzc3Vv/71L7vtjBkzRmPHjr2apQMAgFLI6eFGkgYPHqzBgwcXum79+vV2y7/99lvJFwQAAMosp9/EDwAAwJEINwAAwKUQbgAAgEsh3AAAAJdCuAEAAC6FcAMAAFwK4QYAALgUwg0AAHAphBsAAOBSCDcAAMClEG4AAIBLIdwAAACXQrgBAAAuhXADAABcCuEGAAC4FMINAABwKYQbAADgUgg3AADApRBuAACASyHcAAAAl0K4AQAALoVwAwAAXArhBgAAuBTCDQAAcCmEGwAA4FIINwAAwKUQbgAAgEsh3AAAAJdCuAEAAC6FcAMAAFwK4QYAALiUUhFu4uLiFBQUJE9PT7Vp00Zbt269ZP+PPvpIN954ozw9PXXzzTfr888/v0qVAgCA0s7p4Wbp0qWKjo7WmDFjtH37doWEhCg8PFzp6emF9v/mm2/Uu3dvDRgwQDt27FD37t3VvXt3/fTTT1e5cgAAUBo5PdxMnz5djz/+uKKiotS4cWPNnj1b3t7emjdvXqH9Z86cqXvuuUfPPvusGjVqpPHjx6tFixZ68803r3LlAACgNCrnzMlzc3OVmJiomJgYW5ubm5s6duyoLVu2FDpmy5Ytio6OtmsLDw/XypUrLzpPTk6OcnJybMsnT56UJGVmZha79rycv4o91pWc8shzdgmlwpXsS47CPvk/7JfnsV+WHuyT513pPnlhvGEYl+zn1HBz9OhR5eXlyc/Pz67dz89Pv/76a6FjUlNTC+2fmpp60XliY2M1bty4Au2BgYHFqBp/18TZBZQWsZWdXQH+hv3y/7Fflhrsk//PQfvkqVOnVLnyxbfl1HBztcTExNgd7cnPz9exY8dUrVo1WSwWJ1ZWtmVmZiowMFAHDx6Uj4+Ps8sBJLFfovRhn3QcwzB06tQpBQQEXLKfU8NN9erV5e7urrS0NLv2tLQ0+fv7FzrG39/fVH9Jslqtslqtdm2+vr7FKxoF+Pj48A8WpQ77JUob9knHuNQRmwucekFx+fLlFRoaqoSEBFtbfn6+EhISFBYWVuiYsLAwu/6StHbt2ov2BwAA1xann5aKjo5WZGSkWrZsqdatW2vGjBnKzs5WVFSUJKlfv36qXbu2YmNjJUlDhw7VnXfeqWnTpqlr165asmSJtm3bpnfeeceZbwMAAJQSTg83PXv2VEZGhkaPHq3U1FQ1a9ZMq1evtl00nJKSIje3/x1guvXWW7Vo0SK99NJLeuGFF9SgQQOtXLlSTZpwudbVZrVaNWbMmAKn/ABnYr9EacM+efVZjMt9nwoAAKAMcfpN/AAAAByJcAMAAFwK4QYAALgUwg1KVHx8PPcUQqnCPonShn3S8Qg3pVxqaqqefvpp1atXT1arVYGBgYqIiChwr5/SqmfPntqzZ89Vm++vv/5S1apVVb16dbvnicFx2CeLJj4+XhaLxfaqWLGiQkNDtWLFihKf+1rDPll0ubm5mjx5skJCQuTt7a3q1aurbdu2mj9/vs6ePXtVarganP5VcFzcb7/9prZt28rX11dTpkzRzTffrLNnz2rNmjUaNGjQRZ+/VZp4eXnJy8vrqs23fPly3XTTTTIMQytXrlTPnj2v2tzXAvZJc3x8fLR7925J55+FM3/+fPXo0UM///yzGjZseFVqcHXsk0WXm5ur8PBw7dy5U+PHj1fbtm3l4+Ojb7/9VlOnTlXz5s3VrFmzEq/jqjBQanXu3NmoXbu2kZWVVWDd8ePHbT9PmzbNaNKkieHt7W1cd911xpNPPmmcOnXKtn7+/PlG5cqVjc8++8wIDg42vLy8jAcffNDIzs424uPjjbp16xq+vr7G008/bZw7d842rm7dusbLL79s9OrVy/D29jYCAgKMN998066Oos79d+PHjzdq1KhhVKxY0RgwYIAxcuRIIyQkxLY+MjLS6NatmzFlyhTD39/fqFq1qvHUU08Zubm5l/3M2rVrZ8yePduYNWuW0alTp8v2hznsk0XfJwubJy8vz/Dw8DA+/PDDi46DOeyTRd8nJ02aZLi5uRnbt28vsC43N7fQz7CsItyUUn/++adhsViMiRMnXrbva6+9Znz11VdGcnKykZCQYDRs2NB48sknbevnz59veHh4GJ06dTK2b99ubNiwwahWrZpx9913Gz169DB+/vln47PPPjPKly9vLFmyxDaubt26RqVKlYzY2Fhj9+7dxuuvv264u7sbX375pam5//6P9v333zc8PT2NefPmGbt37zbGjRtn+Pj4FPhH6+PjYwwcONDYtWuX8dlnnxne3t7GO++8c8nPYd++fYbVajWOHTtm/Pnnn4anp6fx22+/XfbzQ9GwT5rbJ/85z7lz54x58+YZHh4exr59+y77GeLy2CfN7ZNNmzY17r777st+Vq6AcFNKfffdd4YkY8WKFabHfvTRR0a1atVsy/Pnzzck2f0H9YknnjC8vb3t/u8hPDzceOKJJ2zLdevWNe655x67bffs2dPo3Lmzqbn//o+2TZs2xqBBg+zGtG3btsA/2rp169r939FDDz1k9OzZ8xLv2jBeeOEFo3v37rblbt26GWPGjLnkGBQd+6S5ffLCe6xQoYJRoUIFw83NzbBarcb8+fMvOgbmsE+a2ye9vLyMIUOGXHS9K+GC4lLKMHHj6HXr1qlDhw6qXbu2KlWqpEceeUR//vmnTp8+bevj7e2tG264wbbs5+enoKAgVaxY0a4tPT3dbtv/fCBpWFiYdu3aZWruv9u9e7dat25t1/bPZUm66aab5O7ubluuVatWgdr+Li8vTwsWLNDDDz9sa3v44YcVHx+v/Pz8i45D0bFPmtsnJalSpUpKSkpSUlKSduzYoYkTJ2rgwIH67LPPLjkORcM+aW6fNPN5lXWEm1KqQYMGslgsl70Y7rffftO9996rpk2bavny5UpMTFRcXJyk8xePXeDh4WE3zmKxFNpmJggUde7iMFvbmjVrdPjwYfXs2VPlypVTuXLl1KtXL/3+++9l5hsTpR37pPna3NzcVL9+fdWvX19NmzZVdHS02rVrp0mTJl1RLTiPfdJcbcHBwWXiAmtHINyUUlWrVlV4eLji4uKUnZ1dYP2JEyckSYmJicrPz9e0adN0yy23KDg4WH/88YfD6vj2228LLDdq1KjYczds2FDff/+9Xds/l4tj7ty56tWrl+3/ki+8evXqpblz517x9sE+6Sju7u7666+/Smz71xL2SXP69OmjdevWaceOHQXWnT17ttDPsKwi3JRicXFxysvLU+vWrbV8+XLt3btXu3bt0uuvv247DFq/fn2dPXtWb7zxhg4cOKD33ntPs2fPdlgNmzdv1uTJk7Vnzx7FxcXpo48+0tChQ4s999NPP625c+dqwYIF2rt3r1555RX98MMPslgsxa4xIyNDn332mSIjI9WkSRO7V79+/bRy5UodO3as2NvH/7BPmmMYhlJTU5Wamqrk5GS98847WrNmjbp163bF28Z57JNF98wzz6ht27bq0KGD4uLitHPnTh04cEAffvihbrnlFu3du/eKtl+aEG5KsXr16mn79u1q3769hg8friZNmqhTp05KSEjQrFmzJEkhISGaPn26Jk2apCZNmuiDDz5QbGysw2oYPny4tm3bpubNm+uVV17R9OnTFR4eXuy5+/btq5iYGI0YMUItWrRQcnKyHn30UXl6eha7xoULF6pChQrq0KFDgXUdOnSQl5eX3n///WJvH//DPmlOZmamatWqpVq1aqlRo0aaNm2aXn75Zb344otXvG2cxz5ZdFarVWvXrtVzzz2nt99+W7fccotatWql119/XUOGDFGTJk2uaPulicW4lq4wgilBQUF65pln9Mwzz5ToPJ06dZK/v7/ee++9Ep0HZR/7JEob9snSiTsU46o6ffq0Zs+erfDwcLm7u2vx4sVat26d1q5d6+zScI1in0Rpwz555Qg3uKosFos+//xzTZgwQWfOnFHDhg21fPlydezY0dml4RrFPonShn3yynFaCgAAuBQuKAYAAC6FcAMAAFwK4QYAALgUwg0AAHAphBsAAOBSCDcAIGns2LFq1qyZs8sA4AB8FRwAJGVlZSknJ0fVqlVzdikArhDhBgAAuBROSwFwqPz8fE2ePFn169eX1WpVnTp1NGHCBEnSyJEjFRwcLG9vb9WrV0+jRo3S2bNnbWMvnBqaN2+e6tSpo4oVK+qpp55SXl6eJk+eLH9/f9WsWdO2vQssFotmzZqlzp07y8vLS/Xq1dOyZcvs+hR17gvOnTunIUOGyNfXV9WqVdPIkSMVGRmp7t272/q0a9dOQ4YM0XPPPaeqVavK399fY8eOddyHCaBYCDcAHComJkavvvqqRo0apV9++UWLFi2Sn5+fJKlSpUqKj4/XL7/8opkzZ2rOnDl67bXX7Mbv379fX3zxhVavXq3Fixdr7ty56tq1qw4dOqQNGzZo0qRJeumll/Tdd9/ZjRs1apQefPBB7dy5U3379lWvXr20a9cu2/qizP13kyZN0gcffKD58+dr8+bNyszM1MqVKwv0W7BggSpUqKDvvvtOkydP1ssvv8wzgABnMwDAQTIzMw2r1WrMmTOnSP2nTJlihIaG2pbHjBljeHt7G5mZmba28PBwIygoyMjLy7O1NWzY0IiNjbUtSzIGDhxot+02bdoYTz75pKm5Q0JCbMt+fn7GlClTbMvnzp0z6tSpY3Tr1s3Wdueddxq33Xab3XZbtWpljBw58hLvGkBJ48GZABxm165dysnJUYcOHQpdv3TpUr3++uvav3+/srKydO7cOfn4+Nj1CQoKUqVKlWzLfn5+cnd3l5ubm11benq63biwsLACy0lJSabmvuDkyZNKS0tT69atbW3u7u4KDQ1Vfn6+Xd+mTZvaLdeqVatAbQCuLk5LAXAYLy+vi67bsmWL+vbtqy5duug///mPduzYoRdffFG5ubl2/Tw8POyWLRZLoW3/DBmXUtS5i+NKawPgeIQbAA7ToEEDeXl5KSEhocC6b775RnXr1tWLL76oli1bqkGDBvr9998dNve3335bYLlRo0bFmrty5cry8/PT999/b2vLy8vT9u3bHVYvgJLDaSkADuPp6amRI0fqueeeU/ny5dW2bVtlZGTo559/VoMGDZSSkqIlS5aoVatWWrVqlT7++GOHzf3RRx+pZcuWuu222/TBBx9o69atmjt3riQVa+6nn35asbGxql+/vm688Ua98cYbOn78uCwWi8NqBlAyOHIDwKFGjRql4cOHa/To0WrUqJF69uyp9PR03XfffRo2bJgGDx6sZs2a6ZtvvtGoUaMcNu+4ceO0ZMkSNW3aVAsXLtTixYvVuHFjSSrW3CNHjlTv3r3Vr18/hYWFqWLFigoPD5enp6fDagZQMriJH4Ayz2Kx6OOPP7a7B42j5efnq1GjRurRo4fGjx9fYvMAuHKclgKAQvz+++/68ssvdeeddyonJ0dvvvmmkpOT1adPH2eXBuAyOC0FAIVwc3NTfHy8WrVqpbZt2+rHH3/UunXrbBcpAyi9OC0FAABcCkduAACASyHcAAAAl0K4AQAALoVwAwAAXArhBgAAuBTCDQAAcCmEGwAA4FIINwAAwKX8H62JtGxI2YjwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df[\"campaign\"], y=df[\"Approved_Conversion\"], hue=df[\"gender\"], data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "INTERESTS VS APPROVED CONVERSIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='interest', ylabel='Approved_Conversion'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rPd67d++sNQcAAAAA1A4ZB4m/+MUvIiLirLPOqhhXUFAQSZJEQUFBrF69OnvdAQAAAAC1QsZB4uzZs3PRBwAAAABQi2UcJLo2IgAAAABsejK6a/Mad999d+y+++7RsWPH+PTTTyMi4rrrrovHH388q80BAAAAALVDxkHiTTfdFMOHD4+DDjooFi1aVHFNxBYtWsR1112X7f4AAAAAgFog4yDx+uuvj9tuuy3+8Ic/xGabbVYxfpdddol33nknq80BAAAAALVDxkHi7NmzY6eddvre+MLCwli2bFlWmgIAAAAAapeMg8Ru3brFjBkzvjd+woQJsc0222SjJwAAAACglsn4rs3Dhw+PoUOHxrfffhtJksQbb7wR999/f4waNSpuv/32XPQIAAAAAORZxkHiKaecEg0bNowLLrggysrK4phjjomOHTvG6NGj4xe/+EUuegQAAACAWmvYsGFRUlISERFFRUUxevToPHeUGxkHiRERxx57bBx77LFRVlYWS5cujbZt22a7LwAAAADYIJSUlERxcXG+28i5jK+R+Je//CVmz54dERGNGjUSIgIAAADAJiDjIPGhhx6K7t27R79+/eJvf/tbfPnll7noCwAAAACoRTIOEt966614++23Y++9946rrroqOnbsGD/96U/jvvvui7Kyslz0CAAAAADkWcZBYkTEtttuG5deemn85z//iUmTJsUWW2wRZ599drRv3z7b/QEAAAAAtcB6BYlra9y4cTRs2DDq168fK1euzEZPAAAAAEAts15B4uzZs+OSSy6JbbfdNnbZZZeYPn16jBw5MubPn5/t/gAAAACAWqBupjP8+Mc/jilTpkTv3r3jpJNOiqOPPjo233zzXPQGAAAAANQSGQeJ++23X/z973+PXr165aIfAAAAAKAWyjhIvOSSS3LRBwAAAABQi2UcJK5evTruvPPOePbZZ2PBggVRXl5e6fHnnnsua80BAAAAALVDxkHisGHD4s4774yf/vSnsd1220VBQUEu+gIAAAAAapGMg8SxY8fGgw8+GAcddFAu+gEAAAAAaqE6mc5Qv3796N69ey56AQAAAABqqYyDxHPPPTdGjx4dSZLkoh8AAAAAoBbK+NTml156KSZNmhTjx4+PbbfdNurVq1fp8UcffTRrzQEAAAAAtUPGQWKLFi3isMMOy0UvAAAAAEAtlXGQOGbMmFz0AQAAAADUYhkHiWuUlJTEzJkzIyKiZ8+eUVRUlLWmAAAAAIDaJeObrSxbtixOPvnk6NChQ+y5556x5557RseOHWPIkCFRVlaWix4BAAAAgDzLOEgcPnx4TJ48Of7xj3/EokWLYtGiRfH444/H5MmT49xzz81FjwAAAABAnmV8avMjjzwSDz/8cOy9994V4w466KBo2LBhHHnkkXHTTTdlsz8AAAAAoBbI+IjEsrKyaNeu3ffGt23b1qnNAAAAALCRyjhI3G233eKiiy6Kb7/9tmLcN998EyNHjozddtstq80BAAAAALVDxqc2jx49Og444IDo1KlT7LDDDhER8dZbb0WDBg3i6aefznqDAAAAAED+ZRwkbrfddvHRRx/FvffeGx988EFERBx99NFx7LHHRsOGDbPeIAAAAACQfxkHiRERjRo1ilNPPTXbvQAAAAAAtVTa10icNm1a7LPPPlFaWvq9xxYvXhz77LNPvPXWW1ltDgAAAACoHdIOEq+++urYd999o1mzZt97rHnz5rH//vvHlVdemVHxm266KXr37h3NmjWLZs2axW677Rbjx4/P6DkAAAAAgNxLO0h8/fXXY9CgQet8/OCDD45XXnklo+KdOnWKyy67LKZNmxZTp06NfffdNwYNGhTvvfdeRs8DAAAAAORW2tdInDt3bjRt2nSdjzdp0iTmzZuXUfGDDz640u+XXHJJ3HTTTfHaa6/Ftttum9FzAQAAAAC5k3aQWFRUFDNnzoxu3bpV+fgHH3wQbdq0We9GVq9eHQ899FAsW7YsdttttyqnWb58eSxfvrzi96qu1wgAAAAAZF/apzb3798/LrnkkiofS5IkLrnkkujfv3/GDbzzzjvRpEmTKCwsjNNPPz3GjRsXvXr1qnLaUaNGRfPmzSt+OnfunHE9AAAAACBzaQeJF1xwQbzzzjvRt2/fePDBB+Ott96Kt956Kx544IHo27dvvPvuu/GHP/wh4wZ69uwZM2bMiNdffz3OOOOMOOGEE+Lf//53ldOOGDEiFi9eXPHz+eefZ1wPAAAAAMhc2qc2b7nlljFx4sQ48cQT4xe/+EUUFBRExH+PRuzVq1c888wz0b1794wbqF+/fsV8ffr0iSlTpsTo0aPjlltu+d60hYWFUVhYmHENAAAAAKB60g4SIyJ22WWXePfdd2PGjBnx0UcfRZIksdVWW8WOO+6YtYbKy8srXQcRAAAAAMi/jILENXbccceU4WGzZs1ixowZ8aMf/Wid04wYMSIOPPDA6NKlSyxZsiTuu+++eP755+Ppp59en7YAAAAAgBxZryAxHUmSpJxmwYIFcfzxx8e8efOiefPm0bt373j66adj//33z1VbAAAAAMB6yFmQmI477rgjn+UBAAAAgDSlfddmAAAAAGDTJUgEAAAAAFLKWZBYUFCQq6cGAAAAAGpYzoLEdG62AgAAAABsGHIWJI4fPz4233zzXD09AAAAAFCD0rpr8/Dhw9N+wmuuuSYiIvbYY4/16wgAAAAAqHXSChKnT59e6fc333wzVq1aFT179oyIiA8//DA222yz6NOnT/Y7BAAAAADyLq0gcdKkSRXD11xzTTRt2jT+7//+L1q2bBkREV9//XWcdNJJ8ZOf/CQ3XQIAAAAAeZXxNRKvvvrqGDVqVEWIGBHRsmXL+Mtf/hJXX311VpsDAAAAAGqHjIPE0tLSKCkp+d74kpKSWLJkSVaaAgAAAABql4yDxMMOOyxOOumkePTRR2POnDkxZ86ceOSRR2LIkCExePDgXPQIAAAAAORZWtdIXNvNN98cv/nNb+KYY46JlStX/vdJ6taNIUOGxJVXXpn1BgEAAACA/Ms4SGzUqFH87W9/iyuvvDJmzZoVERFbbrllNG7cOOvNAQAAAAC1Q8anNq8xb968mDdvXvTo0SMaN24cSZJksy8AAAAAoBbJOEj86quvYr/99outttoqDjrooJg3b15ERAwZMiTOPffcrDcIAAAAAORfxkHiOeecE/Xq1YvPPvssGjVqVDH+qKOOigkTJmS1OQAAAACgdsj4Gon/+te/4umnn45OnTpVGt+jR4/49NNPs9YYAAAAAFB7ZHxE4rJlyyodibjGwoULo7CwMCtNAQAAAAC1S8ZB4k9+8pO46667Kn4vKCiI8vLyuOKKK2KfffbJanMAAAAAQO2Q8anNV1xxRey3334xderUWLFiRfzud7+L9957LxYuXBgvv/xyLnoEAAAAAPIs4yMSt9tuu/jwww9jjz32iEGDBsWyZcti8ODBMX369Nhyyy1z0SMAAAAAkGcZH5EYEdG8efP4wx/+kO1eAAAAAIBaKuMgsXv37vHLX/4yjj322OjRo0cuegIAAAAA0jBs2LAoKSmJiIiioqIYPXp0zmplfGrz0KFD45///Gf07Nkzdt111xg9enTMnz8/F70BAAAAAD+gpKQkiouLo7i4uCJQzJWMg8RzzjknpkyZEh988EEcdNBBceONN0bnzp1jwIABle7mDAAAAABsPDIOEtfYaqutYuTIkfHhhx/Giy++GCUlJXHSSSdlszcAAAAAoJZYr5utrPHGG2/EfffdFw888ECUlpbGEUccka2+AAAAAIBaJOMg8cMPP4x777037r///pg9e3bsu+++cfnll8fgwYOjSZMmuegRAAAAAMizjIPErbfeOnbdddcYOnRo/OIXv4h27drloi8AAAAAoBbJOEicOXNm9OjRIxe9AAAAAAC1VMZB4poQcdq0afH+++9HRESvXr1i5513zm5nAAAAAECtkXGQuGDBgjjqqKNi8uTJ0aJFi4iIWLRoUeyzzz4xduzYKCoqynaPAAAAUCuNf+DLtKf9Zll5peF05z3wqDYZ9wWQC3UyneHXv/51LF26NN57771YuHBhLFy4MN59990oLS2Ns846Kxc9AgAAAAB5lvERiRMmTIiJEyfGNttsUzGuV69eceONN8aAAQOy2hwAAAAAUDtkfERieXl51KtX73vj69WrF+Xl5VXMAQAAAABs6DIOEvfdd98YNmxYfPHFFxXj5s6dG+ecc07st99+WW0OAAAAAKgdMg4Sb7jhhigtLY0tttgittxyy9hyyy2jW7duUVpaGtdff30uegQAAAAA8izjayR27tw53nzzzZg4cWJ88MEHERGxzTbbRP/+/bPeHAAAAABQO2QUJK5cuTIaNmwYM2bMiP333z/233//XPUFAAAAANQiGZ3aXK9evejSpUusXr06V/0AAAAAALVQxqc2/+EPf4jf//73cffdd0erVq1y0RNk3bBhw6KkpCQiIoqKimL06NF57ggAAABgw5JxkHjDDTfExx9/HB07doyuXbtG48aNKz3+5ptvZq05yJaSkpIoLi7OdxsAAAAAG6yMg8RDDz00B20AAAAAALVZxkHiRRddlIs+AAAAAIBaLOMgcY2pU6fG+++/HxERvXr1ij59+mStKQAAAACgdsk4SJwzZ04cffTR8fLLL0eLFi0iImLRokXRr1+/GDt2bHTq1CnbPQIAAAAAeVYn0xlOOeWUWLlyZbz//vuxcOHCWLhwYbz//vtRXl4ep5xySi56BAAAAADyLOMjEidPnhyvvPJK9OzZs2Jcz5494/rrr4+f/OQnWW0OAAAAAKgdMj4isXPnzrFy5crvjV+9enV07NgxK00BAAAAALVLxkHilVdeGb/+9a9j6tSpFeOmTp0aw4YNi6uuuiqrzQEAAAAAtUPGpzafeOKJUVZWFn379o26df87+6pVq6Ju3bpx8sknx8knn1wx7cKFC7PXKQAAAACQNxkHidddd10O2gAAAAAAarOMg8QTTjghF30AAAAAALVYxkFixH9vrDJu3Lh4//33IyKiV69eMWjQoIpTnQEAAACAjUvGyd97770XhxxySMyfPz969uwZERGXX355FBUVxT/+8Y/Ybrvtst4kAAAAAJBfGd+1+ZRTToltt9025syZE2+++Wa8+eab8fnnn0fv3r3jf//3f3PRIwAAAACQZxkfkThjxoyYOnVqtGzZsmJcy5Yt45JLLoldd901q80BAAAAALVDxkckbrXVVlFcXPy98QsWLIju3btnpSkAAAAAoHbJOEgcNWpUnHXWWfHwww/HnDlzYs6cOfHwww/H2WefHZdffnmUlpZW/AAAAAAAG4eMT23+2c9+FhERRx55ZBQUFERERJIkERFx8MEHV/xeUFAQq1evzlafAAAAAPHRDd8/S3JdVi5ZXWk43Xl7nNku475gU5BxkDhp0qR1Pvb2229H7969q9UQAAAAAFD7ZBwk7rXXXpV+X7JkSdx///1x++23x7Rp0xyFCAAAAAAboYyvkbjGCy+8ECeccEJ06NAhrrrqqth3333jtddey2ZvAAAAAEAtkdERifPnz48777wz7rjjjigtLY0jjzwyli9fHo899lj06tUrVz3mxLBhw6KkpCQiIoqKimL06NF57ggAAAAAaq+0j0g8+OCDo2fPnvH222/HddddF1988UVcf/31uewtp0pKSqK4uDiKi4srAkUAAAAAoGppH5E4fvz4OOuss+KMM86IHj165LInAAAAAKCWSfuIxJdeeimWLFkSffr0ib59+8YNN9wQX375ZbWKjxo1Knbddddo2rRptG3bNg499NCYOXNmtZ4TAAAAAMi+tIPEH//4x3HbbbfFvHnz4rTTTouxY8dGx44do7y8PJ555plYsmRJxsUnT54cQ4cOjddeey2eeeaZWLlyZQwYMCCWLVuW8XMBAAAAALmT0c1WIiIaN24cJ598cpx88skxc+bMuOOOO+Kyyy6L888/P/bff/944okn0n6uCRMmVPr9zjvvjLZt28a0adNizz33zLQ1AAAA+EFuvAmw/tI+IrEqPXv2jCuuuCLmzJkT999/f7WbWbx4cUREtGrVqsrHly9fHqWlpZV+AAAAIF1uvAmw/qoVJK6x2WabxaGHHprR0YjfVV5eHmeffXbsvvvusd1221U5zahRo6J58+YVP507d17vegAAAABA+rISJGbD0KFD4913342xY8euc5oRI0bE4sWLK34+//zzGuwQAAAAADZdGV8jMRfOPPPMePLJJ+OFF16ITp06rXO6wsLCKCwsrMHOAAAAAICIPAeJSZLEr3/96xg3blw8//zz0a1bt3y2AwAAAACsQ16DxKFDh8Z9990Xjz/+eDRt2jTmz58fERHNmzePhg0b5rM1AAAAAGAteb1G4k033RSLFy+OvffeOzp06FDx88ADD+SzLQAAAADgO/J+ajMAAAAAUPvViputAAAAALkzbNiwKCkpiYiIoqKiGD16dJ47AjZEgkQAAADYyJWUlERxcXG+2wA2cHm9RiIAAAAAsGEQJAIAAAAAKTm1GQAAAICscD3OjZsgEQAAIE/qNGlR5TDAhsr1ODdugkQAAIA8aTH4d/luIS8csQSwYRIkAgAAUKMcsQSwYXKzFQAAAAAgJUEiAAAAAJCSIBEAAAAASEmQCAAAAACkJEgEAAAAAFISJAIAAAAAKQkSAQAAAICU6ua7AVhfH9w4KO1pVy5ZttbwgrTn3Xro4xn3BQAAALAxEiQCAAAAZNmwYcOipKQkIiKKiopi9OjRee4Iqk+QCAAAAJBlJSUlUVxcnO82IKsEiQAAAEBGpt++IO1pVyxdXWk43Xl3OqVtxn0BueVmKwAAAABASoJEAAAAACAlpzYDUG0uJA0AALDxEyQCUG0uJA0AALDxc2ozAAAAAJCSIxIBANLgFH5gY2O9BvDDFtzwr7SnXb3km0rD6c7b9swBGfeVT4JEAIA0OIUf2NhYrwGQKUEikBP+ww0AAAAbF0EikBP+ww0AAAAbFzdbAQAAAABSEiQCAAAAACkJEgEAAACAlFwjEQDYYLiRE5Cunz/yZtrTLi5bUTFcUrYi7XkfPnznjPsCgA2ZIHET4wtYzfOeA2SPGzkBAED+CBI3Mb6A1TzvOQAAALAxECTmgSPUAAAAANjQCBLzwBFqAFSXf0oBALAu9hXJFUEiAGyA/FMKAIB1sa9IrggSAQAAANjgORIz9wSJAAAAAGzwHImZe3Xy3QAAAAAAUPsJEgEAAACAlASJAAAAAEBKrpEIAAAANaBp41ZVDgNsKASJAAAAUAOOHvSnfLcAUC2CRAAgI8OGDYuSkpKIiCgqKorRo0fnuSMAAHKp+LppaU+7unRFpeF05213dp+M+6LmCRKpMb54AmwcSkpKori4ON9tAAAANUyQSI3xxRMAAABgw+WuzQAAAABASoJEAAAAACAlQSIAAAAAkJIgEQAAAABISZAIAAAAAKS0Ud21ueSme9KedvWSZZWG05236IxfZtwXAAAAAGzoHJEIAAAAAKQkSAQAAAAAUtqoTm0GAADW37Bhw6KkpCQiIoqKimL06NF57ggAqE0EiQAAQERElJSURHFxcb7bAABqKUEiAAAAQBrmXTE37WlXL15daTjdeTv8bvOM+4KaIkhkk9CiYUGVwwAAAACkR5DIJuHcvRrluwUAAACADZogEQAAADZAL95dkva03y5dXWk43Xl/clxRxn0BG686+W4AAAAAAKj9BIkAAAAAQEpObQbYSAwbNixKSv57ikpRUVGMHj06zx0BAACwMREkAmwkSkpKori4ON9t1DgB6obvoHF/SXva5WWLK4aLyxanPe9Th12QcV8AAEBlgkQAqnTNfQekPW3pslVrDRenPe/wY57OuK/v2lQDVAAAgJqW12skvvDCC3HwwQdHx44do6CgIB577LF8tgMAGRk2bFgcc8wxccwxx8SwYcPy3Q4AAEBO5fWIxGXLlsUOO+wQJ598cgwePDifrQBAxhwNueH76aPXpj3tt2WlFcPFZaVpz/vPwedk3BcAANRGeQ0SDzzwwDjwwAPz2QIAAAAAkIYN6hqJy5cvj+XLl1f8Xlpa+gNT16ySm29Oe9rVS5ZUGk533qLTT8+4LwAAAADIhg0qSBw1alSMHDky320AQE4c+PjhaU+7otLdixekPe/4QY9k3BcAG47fj5ub9rRfl62uNJzuvJcetnmV428cl/7lPpasVXtJ2eq05x16WLu0a8DGZv7VH6Q97erSlZWG0523/blbZ9wXubHgxsfSnnb1krJKw+nO23booZk1FRtYkDhixIgYPnx4xe+lpaXRuXPnPHYEtduwYcOipKQkIiKKiopi9OjRee5o4+c9B3LBugUAgNpggwoSCwsLo7CwMN9twAbDjSBqnvccyAXrFgAAaoMNKkikavNv+kva065esrjScLrztj/jgoz7AgAAAGDjkdcgcenSpfHxxx9X/D579uyYMWNGtGrVKrp06ZLHzgAAAACAteU1SJw6dWrss88+Fb+vuf7hCSecEHfeeWeeugIAAAAAviuvQeLee+8dSZLkswUAAAAAIA2ukQjrYcotB6c97fIl36w1vCDteXc97R8Z9wUAAACQK3Xy3QAAAAAAUPsJEgEAAACAlASJAAAAAEBKrpEIAADABu2BR75Me9plZeWVhtOd96jD22TcF8DGxhGJAAAAAEBKjkikWubccGra065e8lWl4XTn7XTmbRn3BQAAAEB2OSIRAAAAAEjJEYkAAPAdw4YNi5KSkoiIKCoqitGjR+e5IwDYNBX/9cW0p11d+m2l4XTnbXfWTzLua1MlSATS9tQdB6U97TdLl681XJz2vAcNeSrjvgDInU01UCspKYni4uJ8twEAUKsIEgEAWCeBGgAAawgSAah1Ln7wgLSnXbRs1VrDxWnPe/GRT2fcF8CG6NCHn0l72qVl31QMLyj7Ju15H/v5/hn3BQBseASJALXYLXenH6gtWbpqreHitOc97TiBGgAAAKkJEgFgLUMfHZj2tF+VrVxruDjteW8cPCHjvnLtoMfOTXva5WULK4aLyxamPe9Th16dcV8AAEDtIUgEADYYBU0KI1lrGAAAqDmCRGCjs6neYRQ2BfUP3znfLQAAwCZLkAhsdNxhFAAAALKvTr4bAAAAAABqP0EiAAAAAJCSU5sBAKAW2VSv9bupvm5g49WqQcsqhzd2rRo0r3KYjYMgMQ9aN2xY5TCk4/nbfpr2tN8u+Xat4eK059371H9m3BcAkB2b6rV+N9XXDWy8/tj3wny3kBcX7/GrfLdADgkS82DkvvvmuwUAAAAAyIggESCFu+48IO1ply5dtdZwcdrzHn/i0xn3VZs0aFz1MAAAABsPQSIA1dbvZzYnbPwKmjSIZK1hAADY1Pjmt4lp1bCwymEA4IcVDt493y1sctx8AwCgdtlkg8TWDRtVObyxu2ifnfPdAgBAWtx8AwCgdtlkg8SR+6R/51sAqG0KmtSJJMorhoHUfvbQQ2lP++2yZRXDC5YtS3veJ484IuO+AAA2FJtskAgAG7J6RzTNdwtU008fvSntab8tW1IxXFy2JO15/zn4jIz7AgA2fK0atKhyGKpLkAgAAACwEblot3Pz3QIbKedCAQAAAAApCRIBAAAAgJSc2gzABq2wSdXDAAAAZJcgEYAN2g6DbMoAAFJp1rh1lcMAmfDtC9ggPDxmYNrTli1dudZwcdrz/vykCRn3BbAh+tkjY9Ke9tuypRXDC8qWpj3vk4eflHFfAOTOkING5rsFYCMgSATYSDRqXPUwuVOvSUFEJGsNAwAAbLwEiQAbiX0PskqvaV0He88BAIBNh29AkGPNGxVUOQwAAACwIREkQo6dsW+DfLcAAJCWOo2bRflawwAAaxMkUmNaNtisymEAAGqHRof9Mt8tUIPqN2lZ5TAArIsgkRpzwV6b57sFAADg/9v28Avz3QIAG5g6+W4AAAAAAKj9BIkAAAAAQEpObQYAgO8oaNKkyuH1dcjD/0h72rKysorhBWVlac/7xM8PzrivXDvskZfSnnZJ2fKK4QVly9Oed9zhe2TcFwCwfgSJsBFr1qggIpK1hgGAdBQeemi+WwAAqHUEibARO7F/Yb5bAAAAADYSgkSALGrcqOphAAAA2NAJEgGy6MCBVqsAP+RnD9+b9rTfli2rGF5QtizteZ/8+bEZ9wUAQGq+8QIAGSloUv//X331v8MAAMCmQZAI5ESTtW700sSNXmCjUv/nPfLdwianoEnDtcLbhnntBQCATZcgEciJo/fP31FKjdcKMRsLMYGNQOHgffLdAgBskFo2aFXlMLB+BInARuewA6zaAACAiN/udXG+W4CNSp18NwAAAAAA1H4O2wEAYJ0KGjeqchgAgE2PIBEAgHUqHHxAvlsAAKCWcGozAAAAAJCSIxIBAACoUQ2btKpyGKA6WjVsVuVwTWjdoGmVwxsbQSIAAAA1aq/DL853C8BG6OKfDMlf7b1+mbfaNcmpzQAAAABASo5IBAAANmkFjZtXHGFR0Lh5XnsBgNpMkAgAALVIQeOmVQ6TO80GD813CwCwQRAkAgBALdLwsCPz3QJs1Bo3blXlMACpCRIBAADYZAwa/Kd8twCwwXKzFQAAAAAgJUEiAAAAAJCSU5sBAKiVCho3qnKYjVOdxs2ifK1hAKD2ESQCAFArFR72s3y3QA1qPHhIvlsAAFKoFac233jjjbHFFltEgwYNom/fvvHGG2/kuyUAAAAAYC15DxIfeOCBGD58eFx00UXx5ptvxg477BAHHHBALFiwIN+tAQAAAAD/X96DxGuuuSZOPfXUOOmkk6JXr15x8803R6NGjeLvf/97vlsDAAAAAP6/vF4jccWKFTFt2rQYMWJExbg6depE//7949VXX/3e9MuXL4/ly5dX/L548eKIiCgtLY2IiCXffJPjjiMK/3+t78pv7W9zXrvROmuvyHnt0nXUXvrNyk2y9rI81i7La+1Veav9zSZa+9uy/NVensfaK/JYe1VZ/j5jK8uWVzm+ZmrnfjtWO2vnft+hdtYuU7vGay/bRGsvzWPtJXmrvTyPtb+pkdoNqxxfViO169e62su+qYnahVWOX5pB7ZUrV8aqVasqhtOdt7S0QbVrr691LWtLvs197cbr+v79be7Xa+v87l8DtRuus3butyXrrP1N7ms3WGft9PcdvvsZS3feNbXXrNeTJEk5T0GSzlQ58sUXX8Tmm28er7zySuy2224V43/3u9/F5MmT4/XXX680/cUXXxwjR46s6TYBAAAAYKP2+eefR6dOnX5wmg3qrs0jRoyI4cOHV/xeXl4eCxcujNatW0dBQUHGz1daWhqdO3eOzz//PJo1a5bNVtVWW2211VZbbbXVVltttdVWW2211Va71tdOkiSWLFkSHTt2TDltXoPENm3axGabbRbFxcWVxhcXF0f79u2/N31hYWEUFlY+rLpFixbV7qNZs2Y1/gdWW2211VZbbbXVVltttdVWW2211VZb7dpQu3nz5mlNl9ebrdSvXz/69OkTzz77bMW48vLyePbZZyud6gwAAAAA5FfeT20ePnx4nHDCCbHLLrvE//zP/8R1110Xy5Yti5NOOinfrQEAAAAA/1/eg8SjjjoqSkpK4o9//GPMnz8/dtxxx5gwYUK0a9cu57ULCwvjoosu+t7p0jVBbbXVVltttdVWW2211VZbbbXVVltttTek2nm9azMAAAAAsGHI6zUSAQAAAIANgyARAAAAAEhJkAgAAAAApCRIBAAAAABS2iSCxBdeeCEOPvjg6NixYxQUFMRjjz1W6fETTzwxCgoKKv0MHDgw53UjIt5///045JBDonnz5tG4cePYdddd47PPPqt27VGjRsWuu+4aTZs2jbZt28ahhx4aM2fOrDTNt99+G0OHDo3WrVtHkyZN4vDDD4/i4uIaqT1//vw47rjjon379tG4cePYeeed45FHHqmR2rNmzYrDDjssioqKolmzZnHkkUfWyOv+5JNPvrecrfl56KGHqlX7pptuit69e0ezZs2iWbNmsdtuu8X48eMrHr/11ltj7733jmbNmkVBQUEsWrSoWvVS2WKLLap8nUOHDs1qnVSvO1fLeER6y9oaSZLEgQceuM71QC5q5+pv/kPv+cKFC+PXv/519OzZMxo2bBhdunSJs846KxYvXpyV2qnWqUuXLo0zzzwzOnXqFA0bNoxevXrFzTffnJXaERFz586NX/7yl9G6deto2LBhbL/99jF16tSKxy+++OLYeuuto3HjxtGyZcvo379/vP766zVSO1fbsXSWtb333vt7tU8//fRq145I/TdPkiT++Mc/RocOHaJhw4bRv3//+Oijj3Jet7i4OE488cTo2LFjNGrUKAYOHJiVuhHpr1teffXV2HfffaNx48bRrFmz2HPPPeObb76pVu1U69Rc1Y1I73WfdtppseWWW0bDhg2jqKgoBg0aFB988EG1a0ek/oyt7fTTT4+CgoK47rrrql031evO9Xo1k16yLZ33PBv7ydlYjyxcuDCOPfbYaNasWbRo0SKGDBkSS5curZHal1xySfTr1y8aNWoULVq0qLHX/cknn8SQIUOiW7du0bBhw9hyyy3joosuihUrVtTI6z7kkEOiS5cu0aBBg+jQoUMcd9xx8cUXX9RI7ar2Yy+77LKc137++efX+V1hypQp1ar96KOPxoABA6J169ZRUFAQM2bM+N5zrO9+czZqr+9+a3VrV2cdm43Xvb7btWws5x9++GEMGjQo2rRpE82aNYs99tgjJk2aVCO133zzzdh///2jRYsW0bp16/jf//3frKxT03nP1+4zk+9n2dwnXb58eey4444pe8x27X/+85/Rt2/faNiwYbRs2TIOPfTQlLXXZZMIEpctWxY77LBD3HjjjeucZuDAgTFv3ryKn/vvvz/ndWfNmhV77LFHbL311vH888/H22+/HRdeeGE0aNCg2rUnT54cQ4cOjddeey2eeeaZWLlyZQwYMCCWLVtWMc0555wT//jHP+Khhx6KyZMnxxdffBGDBw+ukdrHH398zJw5M5544ol45513YvDgwXHkkUfG9OnTc1p72bJlMWDAgCgoKIjnnnsuXn755VixYkUcfPDBUV5entPanTt3rrSMzZs3L0aOHBlNmjSJAw88sFq1O3XqFJdddllMmzYtpk6dGvvuu28MGjQo3nvvvYiIKCsri4EDB8bvf//7atVJ15QpUyq9zmeeeSYiIo444ois1kn1unO1jEekt5yvcd1110VBQUFW6qZbO1d/8x96z7/44ov44osv4qqrrop333037rzzzpgwYUIMGTIkK7VTrVOHDx8eEyZMiHvuuSfef//9OPvss+PMM8+MJ554otq1v/7669h9992jXr16MX78+Pj3v/8dV199dbRs2bJimq222ipuuOGGeOedd+Kll16KLbbYIgYMGBAlJSU5rx2Rm+1Yusv5qaeeWqn2FVdcUe3aEan/5ldccUX89a9/jZtvvjlef/31aNy4cRxwwAHx7bff5qxukiRx6KGHxn/+8594/PHHY/r06dG1a9fo379/lZ//TKXznr/66qsxcODAGDBgQLzxxhsxZcqUOPPMM6NOnert1qVap+aqbkR6r7tPnz4xZsyYeP/99+Ppp5+OJEliwIABsXr16mrVTvczFhExbty4eO2116Jjx47VqrlGqted6/VqJr1kUzrvebb2k7OxHjn22GPjvffei2eeeSaefPLJeOGFF+J///d/a6T2ihUr4ogjjogzzjijRl/3Bx98EOXl5XHLLbfEe++9F9dee23cfPPNae1XZON177PPPvHggw/GzJkz45FHHolZs2bFz3/+8xqpHRHxpz/9qdJ27de//nXOa/fr1+973xVOOeWU6NatW+yyyy7Vqr1s2bLYY4894vLLL1/nc6zvfnM2aq/vfmt1a1dnHZuN172+27VsLOc/+9nPYtWqVfHcc8/FtGnTYocddoif/exnMX/+/JzW/uKLL6J///7RvXv3eP3112PChAnx3nvvxYknnviDddOpnc57vkam38+yuU/6u9/9LqP9iGzUfuSRR+K4446Lk046Kd566614+eWX45hjjkm7h+9JNjERkYwbN67SuBNOOCEZNGhQjdc96qijkl/+8pc5rbvGggULkohIJk+enCRJkixatCipV69e8tBDD1VM8/777ycRkbz66qs5rZ0kSdK4cePkrrvuqjRdq1atkttuuy2ntZ9++umkTp06yeLFiyumWbRoUVJQUJA888wzOa1dlR133DE5+eSTs1p3jZYtWya33357pXGTJk1KIiL5+uuvc1JzXYYNG5ZsueWWSXl5ec5rrXndNbmMJ8m6/97Tp09PNt9882TevHlVrgdyWTtJauZvXtWytsaDDz6Y1K9fP1m5cmVWa1b1Xm677bbJn/70p0rjdt555+QPf/hDteudd955yR577JHRPIsXL04iIpk4cWLOa9fEdixJql7W9tprr2TYsGE5r/3dv3l5eXnSvn375Morr6wYt2jRoqSwsDC5//77c1Z35syZSUQk7777bsW41atXJ0VFRVnfhiVJ1e953759kwsuuCDrtaqy9ue7Juumsw196623kohIPv7442rVSvfzPWfOnGTzzTdP3n333aRr167JtddeW626VUnndedqvbo+vayvdN7zXOwnr8965N///ncSEcmUKVMqphk/fnxSUFCQzJ07N6e11zZmzJikefPmadfLZu01rrjiiqRbt255qf34448nBQUFyYoVK3JeOxuf72y87hUrViRFRUXf27fJtPbaZs+enUREMn369Erjs7XfvD6111ad/dbq1l5jfdax2aq9Ptu19VnWSkpKkohIXnjhhYppSktLk4jI6Dvx+tS+5ZZbkrZt2yarV6+umObtt99OIiL56KOP1rv22lK959X9fladz/dTTz2VbL311sl7772X9nJR3dorV65MNt9883V+X1sfm8QRiel4/vnno23bttGzZ88444wz4quvvsppvfLy8vjnP/8ZW221VRxwwAHRtm3b6Nu3b1ZOe6zKmsOzW7VqFRER06ZNi5UrV0b//v0rptl6662jS5cu8eqrr+a0dsR//+P2wAMPxMKFC6O8vDzGjh0b3377bey99945rb18+fIoKCiIwsLCimkaNGgQderUiZdeeimntb9r2rRpMWPGjKwfVbB69eoYO3ZsLFu2LHbbbbesPvf6WLFiRdxzzz1x8sknZ/WovO/67uuuyWU8ouq/d1lZWRxzzDFx4403Rvv27bNe84dq14R0lrXFixdHs2bNom7dujnvp1+/fvHEE0/E3LlzI0mSmDRpUnz44YcxYMCAaj/3E088EbvsskscccQR0bZt29hpp53itttuW+f0K1asiFtvvTWaN28eO+ywQ43Uront2LqWtXvvvTfatGkT2223XYwYMSLKysqyXvu7Zs+eHfPnz6/0GW/evHn07ds3J5/xNZYvXx4RUemoqDp16kRhYWHWtyMR33/PFyxYEK+//nq0bds2+vXrF+3atYu99tor67W/+/muqbprpFqvLVu2LMaMGRPdunWLzp07V6tWOp+x8vLyOO644+K3v/1tbLvtttWq90PSWZ/X1Ho1l9uWVO95Te0np7MeefXVV6NFixaVjgjr379/1KlTp1qXr8jXOqw6tRcvXlzt5WF9ai9cuDDuvffe6NevX9SrV69Gal922WXRunXr2GmnneLKK6+MVatWrXfdTGuv8cQTT8RXX30VJ510UrVqp6Om95trs5rcd11btrZr6SxrrVu3jp49e8Zdd90Vy5Yti1WrVsUtt9wSbdu2jT59+uS09vLly6N+/fqVzmZo2LBhRETO9inWlovvZ+l+vouLi+PUU0+Nu+++Oxo1alRjtd98882YO3du1KlTJ3baaafo0KFDHHjggfHuu++ud11BYvz3dLC77rornn322bj88stj8uTJceCBB1b7VJkfsmDBgli6dGlcdtllMXDgwPjXv/4Vhx12WAwePDgmT56c1Vrl5eVx9tlnx+677x7bbbddRPz3GoX169f/3rVW2rVrl/Jw5urWjoh48MEHY+XKldG6desoLCyM0047LcaNGxfdu3fPae0f//jH0bhx4zjvvPOirKwsli1bFr/5zW9i9erVMW/evJzW/q477rgjttlmm+jXr19War7zzjvRpEmTKCwsjNNPPz3GjRsXvXr1yspzV8djjz0WixYtSutw9fWxrtddU8t4xLr/3uecc07069cvBg0alNV66dTOpXSXtS+//DL+/Oc/p3X6VzZcf/310atXr+jUqVPUr18/Bg4cGDfeeGPsueee1X7u//znP3HTTTdFjx494umnn44zzjgjzjrrrPi///u/StM9+eST0aRJk2jQoEFce+218cwzz0SbNm1yXrsmtmPrWtaOOeaYuOeee2LSpEkxYsSIuPvuu+OXv/xl1uquy5rPcbt27SqNz8VnfG1rvliNGDEivv7661ixYkVcfvnlMWfOnKxuRyKqfs//85//RMR/r8l56qmnxoQJE2LnnXeO/fbbLyvXaVzX5zvXddf2Q+u1v/3tb9GkSZNo0qRJjB8/Pp555pmoX79+teql8xm7/PLLo27dunHWWWdVq9YPSWd9XlPr1VxvW1K95zW1n5zOemT+/PnRtm3bSo/XrVs3WrVqVa11Tb7WYetb++OPP47rr78+TjvttBqrfd5550Xjxo2jdevW8dlnn8Xjjz9eI7XPOuusGDt2bEyaNClOO+20uPTSS+N3v/tdjdRe2x133BEHHHBAdOrUqVq10+2vpvaba7Oa3neNyP52LZ1lraCgICZOnBjTp0+Ppk2bRoMGDeKaa66JCRMmVHlZj2zW3nfffWP+/Plx5ZVXxooVK+Lrr7+O888/PyIi6/tRVcnF97N0XneSJHHiiSfG6aefnvJSBdmuvfY+3AUXXBBPPvlktGzZMvbee+9YuHDhetWt2Zi9lvrFL35RMbz99ttH7969Y8stt4znn38+9ttvv5zUXHM9vkGDBsU555wTERE77rhjvPLKK3HzzTfHXnvtlbVaQ4cOjXfffbdGEv50a1944YWxaNGimDhxYrRp0yYee+yxOPLII+PFF1+M7bffPme1i4qK4qGHHoozzjgj/vrXv0adOnXi6KOPjp133jkr13j6odpr++abb+K+++6LCy+8MGs1e/bsGTNmzIjFixfHww8/HCeccEJMnjw572HiHXfcEQceeGDWrif1Xet63TWpqr/3E088Ec8991y1r/u5PrVzLZ1lrbS0NH76059Gr1694uKLL66Rvq6//vp47bXX4oknnoiuXbvGCy+8EEOHDo2OHTtW+i/d+igvL49ddtklLr300oiI2GmnneLdd9+Nm2++OU444YSK6fbZZ5+YMWNGfPnll3HbbbfFkUceWXEUVy5r18R2bF3L2to729tvv3106NAh9ttvv5g1a1ZsueWWWaldm9SrVy8effTRGDJkSLRq1So222yz6N+/fxx44IHx3zNOsqeq93zN/sNpp51WcZTKTjvtFM8++2z8/e9/j1GjRlWr5ro+37muu7YfWq8de+yxsf/++8e8efPiqquuiiOPPDJefvnlal1fOtVnbNq0aTF69Oh48803c3pkfar1eU2uV3O9bUn1ntfkfjKpzZ07NwYOHBhHHHFEnHrqqTVW97e//W0MGTIkPv300xg5cmQcf/zx8eSTT+b0cxjx32sur9G7d++oX79+nHbaaTFq1KhKZzXl0pw5c+Lpp5+OBx98sEbqkZ9914jcbNdSSZIkhg4dGm3bto0XX3wxGjZsGLfffnscfPDBMWXKlOjQoUPOam+77bbxf//3fzF8+PAYMWJEbLbZZnHWWWdFu3btsvp9vCo19f2sKtdff30sWbIkRowYUeO112xT//CHP8Thhx8eERFjxoyJTp06xUMPPbRe/yByRGIVfvSjH0WbNm3i448/zlmNNm3aRN26db8X8myzzTZZuWvzGmeeeWY8+eSTMWnSpEr/zWrfvn2sWLHie3fEKi4uztohvuuqPWvWrLjhhhvi73//e+y3336xww47xEUXXRS77LLLD94QJxu1IyIGDBgQs2bNigULFsSXX34Zd999d8ydOzd+9KMf5bz2Gg8//HCUlZXF8ccfn5WaERH169eP7t27R58+fWLUqFGxww47xOjRo7P2/Ovj008/jYkTJ8Ypp5ySsxrret01sYxHrPvv/dxzz8WsWbOiRYsWUbdu3YrTIw4//PCsncKfzrKWC6mWtSVLlsTAgQOjadOmMW7cuGqdhpSub775Jn7/+9/HNddcEwcffHD07t07zjzzzDjqqKPiqquuqvbzd+jQIa31dePGjaN79+7x4x//OO64446oW7du3HHHHTVSe23Z3o5lsqz17ds3IiKn29CIqPgcf/eOktn+jFelT58+MWPGjFi0aFHMmzcvJkyYEF999VXWtiMR637P1+zc52r/YV2f71zXXSPVsta8efPo0aNH7LnnnvHwww/HBx98EOPGjatWzVSfsRdffDEWLFgQXbp0qViff/rpp3HuuefGFltsUa3aa6R63TW5Xq2JbUuq97ym9pPTWY+0b98+FixYUOnxVatWxcKFC6u1rsnnOiyT2l988UXss88+0a9fv7j11ltrtHabNm1iq622iv333z/Gjh0bTz31VLz22ms1Unttffv2jVWrVsUnn3xSY7XHjBkTrVu3jkMOOWS9a2baX03sN9dW+dh3XSPb27V0lrXnnnsunnzyyRg7dmzsvvvusfPOO8ff/va3aNiw4ffOtsl27Yj/ns0yf/78mDt3bnz11Vdx8cUXR0lJSVb3o6qSq+9n6b7nr776ahQWFkbdunUrzsTcZZddKh2UkIvaVe3DFRYWxo9+9KP13qYKEqswZ86c+Oqrr3KaxNevXz923XXXmDlzZqXxH374YXTt2rXaz58kSZx55pkxbty4eO6556Jbt26VHu/Tp0/Uq1cvnn322YpxM2fOjM8++6za19VLVXvNtbO++x+HzTbbrNp3Tk5Ve21t2rSJFi1axHPPPRcLFiyo9oY6k9p33HFHHHLIIVFUVFStmj+kvLy84lpe+TJmzJho27Zt/PSnP62xmmtedy6X8YjUf+/zzz8/3n777ZgxY0bFT0TEtddeG2PGjMlp7Zq29rJWWloaAwYMiPr168cTTzyR0/+mrm3lypWxcuXKnKxXIiJ233339VpfZ+NzuD61s7UdW59lbc2ynsttaEREt27don379pU+46WlpfH666/X2PVhmzdvHkVFRfHRRx/F1KlTs3KaTKr3fIsttoiOHTvmbP/hu9Ysw7muuz7LWpIkkSRJzj9jxx133PfW5x07dozf/va38fTTT1erdjqvu6bWqzW5bUn1nud6P3mNdNYju+22WyxatCimTZtWMc1zzz0X5eXlFf84yVXtXEm39ty5c2PvvfeuuLNsNo4WWt/XvWZbXp3P+/rWnjFjRtSpU6daZxdkUjtJkhgzZkwcf/zxNRZo5Xq/uTbL175rVbKxXUtnWVvX9/E6depUa785089Yu3btokmTJvHAAw9EgwYNYv/991/v2unI1fezdF73X//613jrrbcq6j711FMREfHAAw/EJZdcktPaffr0icLCwkrb1JUrV8Ynn3yy/tvUrN22pRZbsmRJMn369GT69OlJRCTXXHNNMn369OTTTz9NlixZkvzmN79JXn311WT27NnJxIkTk5133jnp0aNH8u233+asbpIkyaOPPprUq1cvufXWW5OPPvoouf7665PNNtssefHFF6v9ms8444ykefPmyfPPP5/Mmzev4qesrKximtNPPz3p0qVL8txzzyVTp05Ndtttt2S33XbLee0VK1Yk3bt3T37yk58kr7/+evLxxx8nV111VVJQUJD885//zGntJEmSv//978mrr76afPzxx8ndd9+dtGrVKhk+fHi16qZbO0mS5KOPPkoKCgqS8ePHV7vmGueff34yefLkZPbs2cnbb7+dnH/++UlBQUHyr3/9K0mSJJk3b14yffr05Lbbbqu4Q9f06dOTr776Kms9fNfq1auTLl26JOedd17OaqR63blaxpMk/b/32iJLd21Op3au/uY/9J4vXrw46du3b7L99tsnH3/8caXeVq1aVd2XnXKdutdeeyXbbrttMmnSpOQ///lPMmbMmKRBgwbJ3/72t2rXfuONN5K6desml1xySfLRRx8l9957b9KoUaPknnvuSZIkSZYuXZqMGDEiefXVV5NPPvkkmTp1anLSSSclhYWFle7um4vaudyOpVrWPv744+RPf/pTMnXq1GT27NnJ448/nvzoRz9K9txzz2rVXSPV3/yyyy5LWrRokTz++OPJ22+/nQwaNCjp1q1b8s033+S07oMPPphMmjQpmTVrVvLYY48lXbt2TQYPHlzt15sk6X2+r7322qRZs2bJQw89lHz00UfJBRdckDRo0KDady9OtU7NVd0kSf26Z82alVx66aXJ1KlTk08//TR5+eWXk4MPPjhp1apVUlxcXK3aqT5jVcnWXZtTve5cr1cz6SWb0nnPs7WfnI31yMCBA5Oddtopef3115OXXnop6dGjR3L00UfXSO1PP/00mT59ejJy5MikSZMmFc+3ZMmSnNaeM2dO0r1792S//fZL5syZU2mZyPXrfu2115Lrr78+mT59evLJJ58kzz77bNKvX79kyy23TLldq27tV155Jbn22muTGTNmJLNmzUruueeepKioKDn++ONz/rrXmDhxYhIRyfvvv5+yZrq1v/rqq2T69OnJP//5zyQikrFjxybTp0+v9Pdc3/3mbNRe3/3W6tauzjq2urWrs12r7rJWUlKStG7dOhk8eHAyY8aMZObMmclvfvObpF69esmMGTNyWjtJkuT6669Ppk2blsycOTO54YYbkoYNGyajR4/+wbrZeM+rku73s2zvk2ZyN+9s1B42bFiy+eabJ08//XTywQcfJEOGDEnatm2bLFy4MGX9qmwSQeKa28h/9+eEE05IysrKkgEDBiRFRUVJvXr1kq5duyannnpqMn/+/JzWXeOOO+5IunfvnjRo0CDZYYcdkscee6zadZMkqbJuRCRjxoypmOabb75JfvWrXyUtW7ZMGjVqlBx22GFp7Rxko/aHH36YDB48OGnbtm3SqFGjpHfv3sldd91VI7XPO++8pF27dkm9evWSHj16JFdffXVSXl5eI7WTJElGjBiRdO7cudIt76vr5JNPTrp27ZrUr18/KSoqSvbbb7+KL35JkiQXXXRRWr1l09NPP51ERDJz5syc1Uj1unO1jCdJ+n/v786TjSAxndq5+pv/0Hu+rnVeRCSzZ8+uVt0fev4169R58+YlJ554YtKxY8ekQYMGSc+ePbP2+U6SJPnHP/6RbLfddklhYWGy9dZbJ7feemvFY998801y2GGHJR07dkzq16+fdOjQITnkkEOSN954I+e1c7kdS7WsffbZZ8mee+6ZtGrVKiksLEy6d++e/Pa3v00WL15c7dpJkvpvXl5enlx44YVJu3btksLCwmS//fbLyjonVd3Ro0cnnTp1SurVq5d06dIlueCCC5Lly5dXu26SpL9uGTVqVNKpU6ekUaNGyW677ZaVf0KmWqfmqm6SpH7dc+fOTQ488MCkbdu2Sb169ZJOnTolxxxzTPLBBx9kpf4Pfcaqkq0gMdXrzvV6NZNesi2d9zwb+8nZWI989dVXydFHH500adIkadasWXLSSSelDPKyVfuEE06o8jkmTZqU09pjxoxZ5zKR69f99ttvJ/vss0/FtmWLLbZITj/99GTOnDk5rz1t2rSkb9++SfPmzZMGDRok22yzTXLppZem9Y+5bG2zjj766KRfv34p62VSe11/z4suuqjiOdZ3vzkbtdd3v7W6tauzjq1u7eps17KxrE2ZMiUZMGBA0qpVq6Rp06bJj3/84+Spp56qkdrHHXdc0qpVq6R+/foZ5QDZWNa+KyK972fZ3ifNJEjMRu0VK1Yk5557btK2bdukadOmSf/+/at1sENBkmT5yuAAAAAAwEbHNRIBAAAAgJQEiQAAAABASoJEAAAAACAlQSIAAAAAkJIgEQAAAABISZAIAAAAAKQkSAQAAAAAUhIkAgAAAAApCRIBADZhe++9d5x99tn5bgMAgA1AQZIkSb6bAAAgPxYuXBj16tWLpk2bppz2k08+iW7dusX06dNjxx13zH1zKRQUFMS4cePi0EMPzXcrAACbhLr5bgAAgPxp1apVXuquXLky6tWrl5faAACsH6c2AwBswtY+tXmLLbaISy+9NE4++eRo2rRpdOnSJW699daKabt16xYRETvttFMUFBTE3nvvXfHY7bffHttss000aNAgtt566/jb3/5W8dgnn3wSBQUF8cADD8Ree+0VDRo0iHvvvTflfCtWrIgzzzwzOnToEA0aNIiuXbvGqFGjKnqNiDjssMOioKCg4ncAAHLHEYkAAFS4+uqr489//nP8/ve/j4cffjjOOOOM2GuvvaJnz57xxhtvxP/8z//ExIkTY9ttt4369etHRMS9994bf/zjH+OGG26InXbaKaZPnx6nnnpqNG7cOE444YSK5z7//PPj6quvjp122qkiTPyh+f7617/GE088EQ8++GB06dIlPv/88/j8888jImLKlCnRtm3bGDNmTAwcODA222yzvLxfAACbEkEiAAAVDjrooPjVr34VERHnnXdeXHvttTFp0qTo2bNnFBUVRURE69ato3379hXzXHTRRXH11VfH4MGDI+K/Ry7++9//jltuuaVSkHj22WdXTJPOfJ999ln06NEj9thjjygoKIiuXbtWzLumlxYtWlTqBQCA3BEkAgBQoXfv3hXDBQUF0b59+1iwYME6p1+2bFnMmjUrhgwZEqeeemrF+FWrVkXz5s0rTbvLLrtkNN+JJ54Y+++/f/Ts2TMGDhwYP/vZz2LAgAHVfo0AAKwfQSIAABW+ewOUgoKCKC8vX+f0S5cujYiI2267Lfr27Vvpse+ebty4ceOM5tt5551j9uzZMX78+Jg4cWIceeSR0b9//3j44YczfFUAAGSDIBEAgLSsuSbi6tWrK8a1a9cuOnbsGP/5z3/i2GOPTfu50p2vWbNmcdRRR8VRRx0VP//5z2PgwIGxcOHCaNWqVdSrV69SLwAA5JYgEQCAtLRt2zYaNmwYEyZMiE6dOkWDBg2iefPmMXLkyDjrrLOiefPmMXDgwFi+fHlMnTo1vv766xg+fPg6ny/VfNdcc0106NAhdtppp6hTp0489NBD0b59+2jRokVE/PfOzc8++2zsvvvuUVhYGC1btqyhdwIAYNNUJ98NAACwYahbt2789a9/jVtuuSU6duwYgwYNioiIU045JW6//fYYM2ZMbL/99rHXXnvFnXfeGd26dfvB50s1X9OmTeOKK66IXXbZJXbdddf45JNP4qmnnoo6df67C3v11VfHM888E507d46ddtopty8eAIAoSJIkyXcTAAAAAEDt5ohEAAAAACAlQSIAAAAAkJIgEQAAAABISZAIAAAAAKQkSAQAAAAAUhIkAgAAAAApCRIBAAAAgJQEiQAAAABASoJEAAAAACAlQSIAAAAAkJIgEQAAAABI6f8BgD3yocEODsMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['interest'].unique()\n",
    "fig, ax = plt.subplots(figsize=(16,6))\n",
    "sns.barplot(data=df, x=\"interest\", y=\"Approved_Conversion\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IMPRESSIONS VERSUS APPROVED CONVERSIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Impressions', ylabel='Approved_Conversion'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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22GOYPHkyUlJS8O233+Lss8/Gvn37MGbMGNTU+L/4q6uwaB4RUedy1ZlptNiRolFCn8w6MyRezIrmAW0XAOfl5fksV6vVaGpqEtsdERGdYVITmbxQ5xL9NVNOTg727Nnjs/yTTz7BoEGDohETERERUdhEn5mZN28eZs+eDYvFAkEQ8PXXX2PNmjVYvHgxXnrppVjESERERBSQ6GTmlltugVarxQMPPIDm5mbccMMNyMrKwrPPPusuqEdERETUWURfAOypubkZZrMZvXr1imZMUcULgImIiKRHzPFb9DUzjzzyiLsKcGJiYlwnMkRERHTmE53MvPXWWzAYDLj44ovx/PPPx92t2ERERNS9iE5mvv32W5SVlWHMmDF44oknkJWVhSuvvBKvv/46mpubYxEjBdDQbMMPJ80oPVKHH06Z0dBs6+qQiIiIOt1pXTMDANu3b8frr7+Ot956CxaLBSaTKVqxRcWZes1MVX0L5peUeT2htsiox5KpuchK03ZhZERERKcvptfMdJSUlAStVguVSgW7nU9E7QwNzTafRAYAtpbXYEFJGc/QEBFRtxJRMlNZWYlHH30UQ4YMwQUXXIDS0lIsWrQIx48fj3Z85EeN2eaTyLhsLa9BjZnJDBERdR+i68xcdNFF+O9//4vc3FzMnDkT06ZNw1lnnRWL2CgAkyX4GbDGEOuJiIjOJKKTmfHjx2PlypUYPHhwLOKhMOg0yqDrU0KsJyIiOpOITmYeffTRWMRBIuiTVSgy6rHVz1dNRUY99Ml8wBsREXUfopMZh8OB1atXY+PGjTh58iScTqfX+k2bNkUtOPIvNVGFJVNzsaCkzCuhKTLqsXRqLp9WS0RE3YroZOa2227D6tWrceWVV2Lo0KGQyWSxiItCyErTYtm0PNSYbWi02JGiUUKfrGIiQ0RE3Y7oZGbt2rV48803ccUVV8QiHhIhNZHJCxERkehbs1UqFQwGQyxiISIiIhJNdDJz55134tlnn8VpFg4mIiIiigrRXzNt27YNmzdvxscff4whQ4ZAqfS+Dfidd96JWnBEREREoYhOZtLS0jBlypRYxEJEREQkmuhkZtWqVbGIg4iIiCgiopMZl1OnTuHQoUMAgIEDB6Jnz55RC4qIiIgoXKIvAG5qasLNN9+MPn36oKioCEVFRcjKysKsWbPQ3NwcixiJiIiIAhKdzMybNw9btmzB+++/j/r6etTX1+Pdd9/Fli1bcOedd8YiRiIiIqKAZILIe6z1ej3efvttjBkzxmv55s2b8dvf/hanTp2KZnynzWQyITU1FQ0NDdDpdF0dDhEREYVBzPFb9JmZ5uZmZGZm+izv1asXv2YiIiKiTic6mRk9ejQWLlwIi8XiXtbS0oJFixZh9OjRUQ2OiIiIKBTRdzM9++yzuOyyy9C3b18MHz4cAPDtt99Co9Hg008/jXqARERERMGIvmYGaPuq6f/+7/9w8OBBAMCgQYNw4403QqvVRj3A08VrZoiIiKRHzPE7ojoziYmJ+MMf/hBRcERERETRFPY1M7t27cLYsWNhMpl81jU0NGDs2LH49ttvoxocERERUShhJzNPPvkkxo0b5/dUT2pqKi655BI8/vjjUQ2OiIiIKJSwk5mvvvoK11xzTcD1kyZNwn/+85+oBEVEREQUrrCTmZ9++gkpKSkB1ycnJ6O6ujoqQRERERGFK+xkpmfPnu4HS/pz8OBB6PX6qARFREREFK6wk5kJEybg0Ucf9btOEAQ8+uijmDBhQtQCIyIiIgpH2LdmP/DAAxgxYgRGjRqFO++8EwMHDgTQdkbmySefxPfff4/Vq1fHKk4iIiIiv8JOZs455xxs2LABM2bMwPXXXw+ZTAag7azM4MGD8fnnn8NgMMQsUCIiIiJ/RD2b6YILLsC+ffuwe/durF27FmvWrMHu3buxb98+jBw5UvTgW7duxaRJk5CVlQWZTIb169d7rRcEAQ899BD69OkDrVaLCRMmoLy8XPQ4RN1NQ7MNP5w0o/RIHX44ZUZDs63L+4pmTJ1NyrETdQcRVQA+77zzcN555wVto9PpsGfPHpx99tkB2zQ1NWH48OG4+eabce211/qs//vf/47nnnsOr7zyCnJycvDggw/isssuw/79+6HRaCIJneiMV1XfgvklZfiyvMa9rMiox5KpuchKE/fIkWj1Fc2YOpuUYyfqLkQ/NTtc4TzyaeLEiXjkkUcwZcoUv9s/88wzeOCBB3DNNdcgNzcXr776KqqqqnzO4BBRm4Zmm8+BFwC2ltdgQUmZqDMK0eormjF1NinHTtSdxCyZOV2VlZU4fvy41x1SqampGDVqFHbs2BFwO6vVCpPJ5PUi6i5qzDafA6/L1vIa1JjDP/hGq69oxtTZpBw7UXcSt8nM8ePHAQCZmZleyzMzM93r/Fm8eDFSU1Pdr+zs7JjGSRRPTBZ70PWNIdbHoq9oxtTZpBw7UXcSt8lMpO699140NDS4X0ePHu3qkIg6jU6jDLo+JcT6WPQVzZg6m5RjJ+pOYpbMuG7djlTv3r0BACdOnPBafuLECfc6f9RqNXQ6ndeLqLvQJ6tQZPRfibvIqIc+WdXpfUUzps4m5diJupMuvQA4mJycHPTu3RsbN250LzOZTPjqq68wevTo0w2P6IyUmqjCkqm5PgfgIqMeS6fmIjUx/INvtPqKZkydTcqxE3UnMuF0s44Atm3bhpEjR0KtVgdsYzabUVFRAQDIy8vDU089hbFjx6JHjx7o168fli5diiVLlnjdml1WVibq1myTyYTU1FQ0NDTwLA11Gw3NNtSYbWi02JGiUUKfrIr4wButvqIZU2eTcuxEUiXm+B1WMjNv3rywB3/qqafCbvvFF19g7NixPsunT5+O1atXQxAELFy4EP/6179QX1+PgoICPP/88xgwYEDYYzCZISIikp6oJzMdE47du3ejtbXV/Xym77//HnK5HCNGjMCmTZtOI/ToYzJDREQkPWKO32FVAN68ebP756eeegopKSl45ZVXkJ6eDgCoq6vDzJkzUVhYeBphExEREYkn+pqZs846C5999hmGDBnitXzfvn249NJLUVVVFdUATxfPzBAREUmPmOO36LuZTCYTTp065bP81KlTaGxsFNsdERER0WkRncxMmTIFM2fOxDvvvINjx47h2LFjKCkpwaxZs/w+LJKIiIgolkQ/Nfsf//gH7rrrLtxwww2w29tKeSsUCsyaNQuPP/541AMkIiIiCibiOjNNTU344YcfAADnnHMOkpKSohpYtPCamTObq/6HyWJHqlaJJLUCZksrTBY7dFol9EnSrAfiuV9S3g8iokhF/W4mf6qrq1FdXY2ioiJotVoIgnDajzAgEqOqvgXzS8rwZXkNElVyPDctD6u2V2J7Ra27TZFRjyVTc5GVpu3CSMXx3C8XKe4HEVFnEX3NTG1tLcaPH48BAwbgiiuuQHV1NQBg1qxZuPPOO6MeIJE/Dc02rwP+zQU5PokMAGwtr8GCkjI0NNu6IkzROu6Xi9T2g4ioM4lOZu644w4olUocOXIEiYmJ7uW/+93v8Mknn0Q1OKJAasw2rwN+XnaaTyLjsrW8BjVmaSQBHffLk5T2g4ioM4n+mumzzz7Dp59+ir59+3otNxqNOHz4cNQCIwrGZLF7/d3a6gzavrFD+3jVcb86ksp+EBF1JtFnZpqamrzOyLj8/PPPQR8qSRRNOo3S6+9qRfCPckqH9vGq4351JJX9ICLqTKKTmcLCQrz66qvuv8tkMjidTvz973/3+9BIoljQJ6tQZNS7/156tB75hgy/bYuMeuiTpXEnUMf98iSl/SAi6kyib83et28fxo8fj/PPPx+bNm3C1Vdfje+++w4///wztm/fjnPOOSdWsUaEt2afuarqW7CgpAxbPe5mWr29Ets63M20dGou+kjoLiDP/XKR4n4QEZ2OqD81u6OGhgYsX74c3377LcxmM84//3zMnj0bffr0iTjoWGEyc2Zz1WNpbK/H4qoz02ixI0WjhD5ZmvVZPPdLyvtBRBSpmCczUsJkhoiISHpi+qBJg8GAhx9+GOXl5REHSERERBQtopOZ2bNn48MPP8TAgQMxcuRIPPvsszh+/HgsYiMiIiIKKaKief/9739x8OBBXHHFFVixYgWys7Nx6aWXet3lRERERNQZonLNzM6dO3HrrbeirKwMDocjGnFFDa+ZISIikp5OedAkAHz99dd4/fXX8cYbb8BkMuG66647ne6IiIiIRBOdzHz//ff4v//7P6xZswaVlZUYN24cli5dimuvvRbJycmxiJGIiIgoINHJzLnnnouRI0di9uzZuP7665GZmRmLuIi6LVeNGZPFjmS1Akq5DLVNNmiVciSpFUjTKt01Zzzb6rRK6JOkW4/mTNoXIupcopOZQ4cOwWg0xiIWom6vqr4F80vKvJ6cnW/IwMz8HNy05mvk9UvDnHFG/KpHIpyAT9siox5LpuYiS2KVgv3tt1T3hYg6X8QXAO/atQsHDhwAAAwePBjnn39+VAOLFl4ATFLR0GxD8ZpSrwO6S74hA3n90rF8UwXyDRmYM86IFZsq8GWFb9siox7LpuVJ5qxGsP2W2r4QUfTEtGjeyZMnMXbsWIwcORJz587F3LlzccEFF2D8+PE4depUxEETdXc1ZpvfAzoAbK+oRV52mvvnJLXcbyIDAFvLa1BjtsUqzKgLtt9S2xci6hqik5k5c+bAbDa7Hy75888/Y9++fTCZTJg7d24sYiTqFkwWe9D11lan+2ezJXgJhMYQfcWTUPstpX0hoq4h+pqZTz75BBs2bMCgQYPcywYPHowVK1bg0ksvjWpwRN2JTqMMul6t+OX/HskaedC2KSH6iieh9ltK+0JEXUP0mRmn0wml0veXi1KphNPp9LMFEYVDn6xCkVHvd12+IQOlR+vdPzdZHQHbFhn10CdL5xqTYPsttX0hoq4hOpkZN24cbrvtNlRVVbmX/fTTT7jjjjswfvz4qAZH1J2kJqqwZGquz4HddTfTym2V7ot/c3ok+m1bZNRj6dRcSV0wG2i/pbgvRNQ1RN/NdPToUVx99dX47rvvkJ2d7V42dOhQvPfee+jbt29MAo0U72YiqXHVW2m02JHUXmfm5yYbNEo5klQKpCX61plptNiRolFCnyzd2ixn0r4Q0ekTc/yO6NZsQRCwYcMGHDx4EAAwaNAgTJgwIbJoY4zJDBERkfTE7NlMdrsdWq0We/bswSWXXIJLLrnktAIlIiIiOl2irplRKpXo169f3D0Zm4iIiLov0RcA33///bjvvvvw888/xyIeIiIiIlFE15lZvnw5KioqkJWVhf79+yMpKclr/e7du6MWHBEREVEoopOZyZMnxyAMIiIioshE/KBJqeDdTERERNITs7uZPH3zzTdeT80eMWJEpF0RxTVX/ROTxQ6dVgl9UtfUP4mXOCIl9fgpuvh5oGgSncwcO3YM06ZNw/bt25GWlgYAqK+vx8UXX4y1a9fGXdE8otNRVd+C+SVlXk91LjLqsWRqLrLStN0ujkhJPX6KLn4eKNpE3810yy23wG6348CBA+6nZh84cABOpxO33HJLLGIk6hINzTafX7gAsLW8BgtKytDQbOtWcURK6vFTdPHzQLEg+szMli1b8J///AcDBw50Lxs4cCCWLVuGwsLCqAZH1JVqzDafX7guW8trUGO2dcpp8XiJI1JSj5+ii58HigXRZ2ays7Nht9t9ljscDmRlZUUlKKJ4YLL4fs49NYZYf6bFESmpx0/Rxc8DxYLoZObxxx/HnDlz8M0337iXffPNN7jtttvwxBNPRDU4oq6k0yiDrk8Jsf5MiyNSUo+fooufB4oF0cnMjBkzsGfPHowaNQpqtRpqtRqjRo3C7t27cfPNN6NHjx7uF5GU6ZNVKDLq/a4rMuqhT+6cU+HxEkekpB4/RRc/DxQLouvMvPLKK2G3nT59uuiAoo11Zuh0VNW3YEFJGbZ2uOti6dRc9Onku5niIY5IST1+ii5+HigcYo7fLJpHFIKrHkajxY4UjRL65K6tM9PVcURK6vFTdPHzQKHEvGiew+HAunXrvIrmXXPNNVAoIq7BRxS3UhPj45dsvMQRKanHT9HFzwNFk+js47vvvsPVV1+N48ePu2/PXrp0KXr27In3338fQ4cOjXqQRERERIFEVDRvyJAhOHbsGHbv3o3du3fj6NGjyM3NxR//+MdYxEhEREQUkOgzM3v27ME333yD9PR097L09HQ8+uijGDlyZFSDIyIiIgpF9JmZAQMG4MSJEz7LT548CYPBEJWgiIiIiMIlOplZvHgx5s6di7fffhvHjh3DsWPH8Pbbb+P222/H0qVLYTKZ3C8iIiKiWBN9a3ZCwi/5j0wmAwC4uvD8u0wmg8PhiFacEeOt2URERNIT01uzN2/eHHBdWVkZcnNzxXZJncBV08FksUOnVUKfFPq2yEi2iRfxEnuoOLo6zq4eP55wLoikS3Qy8+tf/9rr742NjVizZg1eeukl7Nq1Ky7OxpC3qvoWzC8p83pSbZFRjyVTc5EVoNpmJNvEi3iJPVQcXR1nV48fTzgXRNIm+poZl61bt2L69Ono06cPnnjiCYwbNw47d+6MZmwUBQ3NNp9f0gCwtbwGC0rK0NBsi8o28SJeYg8VxwmTpUvjjJd5igecCyLpE3Vm5vjx41i9ejVefvllmEwm/Pa3v4XVasX69esxePDgWMVIp6HGbPP5Je2ytbwGNWabz6n0SLaJF/ESe6g46pq6Ns54mad4wLkgkr6wz8xMmjQJAwcORFlZGZ555hlUVVVh2bJlsYyNosBksQdd3+hnfSTbxIt4iT1UHCZLa9D1sY4zXuYpHnAuiKQv7DMzH3/8MebOnYtbb70VRqMxljFRFOk0yqDrU/ysj2SbeBEvsYeKQ6cJ/k8v1nHGyzzFA84FkfSFfWZm27ZtaGxsxIgRIzBq1CgsX74cNTX+T81S/NAnq1Bk1PtdV2TUQ5/se/o8km3iRbzEHiqO9KSujTNe5ikecC6IpC/sZOaiiy7Ciy++iOrqavzpT3/C2rVrkZWVBafTic8//xyNjY2xjJMilJqowpKpuT6/rIuMeiydmuv3WoBItokX8RJ7qDgydZoujTNe5ikecC6IpE900TxPhw4dwssvv4zXXnsN9fX1uOSSS/Dee+9FM77TxqJ5bVw1NBotdqRolNAnh19nRsw28SJeYg8VR1fH2dXjxxPOBVF8EXP8Pq1kxsXhcOD999/HypUrmcwQERHRaev0ZCaeMZkhIiKSHjHH74iL5hERERHFAyYzREREJGlMZoiIiEjSmMwQERGRpDGZISIiIkkT9aDJrvDwww9j0aJFXssGDhyIgwcPdlFEFE2u2h4mix3JagVU8gTUt9iQrFFCn+Rb58OzvU7rv42nEyYL6ppsMFlaodMqkJ6oQqZOE9W4w4mjM8RjTEREnSHukxkAGDJkCDZs2OD+u0IhibAphKr6FswvKfN6YnG+IQMz83Mw7cWvcEH/dCyZmousNG3A9kVGvVcbT0dqm3Dvur3YXlHrXlZgyMBjU4ahX0ZSVOMOFkdniMeYiIg6iyS+ZlIoFOjdu7f7pdf7f44KSUdDs83n4AsA2ytqsWp7JW4uyMHW8hosKClDQ7MtYHvPNp5OmCw+iQwAbKuoxX3r9uKEyRLVuAPF0RniMSYios4kiWSmvLwcWVlZOPvss3HjjTfiyJEjAdtarVaYTCavF8WfGrPN5+Drsr2iFnnZaQDaDsg1ZlvQ9q42nuqabD6JjMu2ilrUNUV2gBcbR2eIx5iIiDpT3Cczo0aNwurVq/HJJ5/ghRdeQGVlJQoLCwM+2HLx4sVITU11v7Kzszs5YgqHyWIPut7a6nT/3Gixh2zf2GG9ydIaYvzg6wNvJy6OzhCPMRERdaa4T2YmTpyI6667Drm5ubjsssvw0Ucfob6+Hm+++abf9vfeey8aGhrcr6NHj3ZyxBQOnUYZdL1a8ctHM0WjDNk+pcN6nSb4dVWh1gfeTlwcnSEeYyIi6kxxn8x0lJaWhgEDBqCiosLverVaDZ1O5/Wi+KNPVqHI6P/ap3xDBkqP1gNou4hVn6wK2t7VxlN6kgoFhgy/7QsMGUhPiuwuH7FxdIZ4jImIqDNJLpkxm8344Ycf0KdPn64OhU5DaqIKS6bm+hyEXXczrdxWiSKjHkun5iI1URWwvWcbT5k6DR6bMswnoXHdzRTp7dli4+gM8RgTEVFnivunZt91112YNGkS+vfvj6qqKixcuBB79uzB/v370bNnz5Db86nZ8c1VG6XRYkdSe52ZhhYbktRK6JMD15lptNiRovHfxpNXnRmNAulJ0a0zE24cnSEeYyIiipSY43fcF2w5duwYpk2bhtraWvTs2RMFBQXYuXNnWIkMxT/XWRdvgWvA+G8fWKZOE5Xk5XTj6AzxGBMRUWeI+2Rm7dq1XR0CERERxTHJXTNDRERE5InJDBEREUkakxkiIiKSNCYzREREJGlMZoiIiEjS4v5uJqlz1f4wWezQaZXQJ7XdPit2eSRj+GtX22RDq1OAUxDQbG1FaqLKq30k4weLyWy1Iy1RBbvDCacAv+M2NNtwstGK+hY7ktVyJKsUsLQ6Yba2nlYM4cRnstiR3F7fpr7FhmRN+ON51bDRKpCklKO+xYZEtW8fHec1Wa1Ak7UVDS2RzbO/9wlAVN67QP1H+zNCRBQtTGZiqKq+BfNLyryeaFxk1OORyUPx1w/2Y8OBk+7llwzqhQevGoz71+/zab9kai6y0rSixui4TVV9Cx56dx+uv7AfVm2v9HqitKtSrACE1Ve4+73rcB2em5aHZzeW44ZR/f2O+8jkofjr+/ux4eBJJKrkeG5ant92YmMIJz7P/XRVHp724le4oH96yPGO1Dbh3nV7veIsMGTgwauG4Hf/3IHBfXTuPvyNV2DIwIz8HMxdU4pmm0PUPnbsL1Elx8oZI7FiUwW+rDi99y7Q/Lj6kgG4JwqfESKiaIr7CsCnq6sqADc021C8ptTrl75LgSED5/VLx/JNvzxfqnicAaVH6rwOji5FRj2WTcvzWw030Bie27jaDc9OCzjG4muH4aOyaq+DYajxQ+23a5/y+qUHHNdzLiKZA7GCzVm+IQN57bEEG++EyYJ5b+4JuD8z83Mw65VvUGTU4/HrhuOut74NOV64++gv/mjOW6jP1MRhfXDvO3tPexwiolDEHL95zUyM1Jhtfg8IALCtohZ52Wley/Ky0/wejABga3kNasw2UWN4buNqF2yMXilqv4lMsPH98YzJNV6wcT3nIpI5ECvYnG33iCXYeHVNtqD700undvdR1xTeeKHGDBZ/NOct1GeqV4o6KuMQEUUTk5kYMVnsQddbW51B/95Ro5/+Qo3h2sbVLtgYkYzvj2dMrj5D9R1uu3BjCEbM+xJoPJOlNWgfZosj7LYd9znUPvqLP5rzJvZzG+k4RETRxGQmRnQaZdD1akVC0L93lOKnv1BjuLZxtQs2RiTj++MZk6vPUH2H2y7cGIIR874EGk+nCX6pWbJGHnbbjvscah/9xR/NeRP7uY10HCKiaGIyEyP6ZBWKjHq/6woMGSg9Wu+1rPRoPQoMGX7bFxn10Cf7XosQbAzPbVztSo/WIz/AGCcbrWH1FYpnTK7xgo3rORfB2omJIdz4Osr3iCXYeOlJqoDvVYEhAydNVncf6UnhjRdqzGDxR3PeQn2mTjZaozIOEVE0MZmJkdREFZZMzfU5MBQZ9XhsyjAcqjZ5LT9UbcJjU4b5bb90aq7fCyuDjeG5javdoWoTZubn+Bz4iox6jB3QM6y+xOz3ym2VmJmfg/1VDQHHfWzKMBxsnwtX+46JgtgYwo3Pk+tuppXbKkOOl6nT4LEpw3ziLDBk4KFJQzC/pMzdR6ZO43e8Ao/xxOyjv/hXbqvEnHFGFJ7mexeof8++xgzoedqfESKiaOPdTDHmqsnRaLEjRaOEPtm7nky4yyMZw1+72iYbHE4BDqeAZpsDqVrv9pGMHyymJqsdqVoV7E4nBAF+x3XVmWlosSPJo85Mk7X1tGIIJ75Gix1J7XVmGlpsSFKHP55XnRmNAkkqORpabNCqfPvoOK/JmrY6M6aWyObZ3/sEICrvXaD+o/0ZISIKRszxm8kMERERxR3emk1ERETdBpMZIiIikjQmM0RERCRpTGaIiIhI0pjMEBERkaQxmSEiIiJJC15rnSLmVYNEq0B6ogoaRQJqm2xodQpwCgKara1ITVRBn+S/JonJYkdyew2UumYrNCoFEmQyKBJkyEjyrfthstih0yqhT1LB2urEzx3Gz9Rp/LYFgLpmG1rsTpgtrW21XtQKtNgdMFtbkZGkgt0hwGxtdW/jWRPHM876FhuSNb/023EssbVU6pvtaLK1osnmQJpWiV4par9PD3fNqwABMgFwAu75TVa31XRpaLEHjD/c+CLZRkw/0eo/2LimFjvMNgca28cI9tlg/RgikgImMzFwpLYJ967b6/Uk40KDHnPGGWC2teLlbZVe64qMeiyZmousNC2q6lswv6TM68nFruq0c1/6Gnn90jAzPweLPzqAv14zFALg077QqMdfxpyDWa98g2Zb20MPx5/bEw9NGoIH1u/zabtw0hAs+Wg/Nhw85V5eYMjAzQU5kEGGpz7/3iveSwb1woNXDcb9HfpyxTnrlW+w/IbzsWJThdeTuD33M5Tq+hYc/rkZyzaVe89je7VZVx9V9S146N19uP7Cfnj9q8O4YVR/rNruPb8FhgzMyM/B3DWlaLY5UGTU45HJQ/HXD/Zjw4GTYcfn770Rs0+h+okkJjGO17fAbHNg4Xv7fObn0SnD8PdPDuLDvcdjMjYRUSyxaF6UnTBZMO/NPV4HC5fHpgzFR3ursc3PuiKjHo9fNxx3vfWt10HOJd+Qgbx+6Vi+qcL981lpWnxUVu2VMPhrDwDF4wwoPVLnN64CQwbO82jrUmjIwMRhfXDfun1ey4P1lW/IwM35OVjZIaHw3M9l0/KC/o+/odmGj/YdxwdlVUH7AIDiNaUYnp2G0iN1yOuXHjQuz/kItM+B4mtotqF4Tanf9yacfQqnH7ExidHQbMOB440+yaHn2K5ENNpjExFFgkXzulBdk83vwQJoe6aPv0QGALaW16Cuyeb3IAcA2ytqkZed5vVzrxS130SmY3sAyMtOCxjXtg5tXb6sqEWmTuOzPFhf2ytq0UunDrh+a3kNasw2v+tcasw29EoJ3UeNuW2+XPGEistzHwPtc6D4XGNFuk/h9CM2JjFqzDYkqeVBPwO9dOqYjE1EFGv8minKTJbWgOusrc6It+24fai+xLYPtN7f8lB9mS2OoOsbLfag600We8gxGi12uE4putqK3cdA7f3FZwoRc6h9CrcfMTGJYbLYYbFH9r6d7thERLHGZCbKdJrAU6pWBD8RFmzbjtuH6kts+0Dr/S0P1VeyRh50fYpGGXS9TqPEz03BzwZ49uGKR+w+BmrvLz5diJhD7VO4/YiJSQydRgmFPHiyHOh9O92xiYhijV8zRVl6kgoFhgy/606YLCgMsK7IqEd6kgpFRr3f9fmGDJQerff6+WSjNaz2AFB6tD5gXAUd2roUGjJwwmTxWR6sr3xDBk6arMgPsp+uJzwHok9W4WRj6D70yW3zVXq03r2/gbbpOB+B9jlQfK6xIt2ncPoRG5MY+mQVmqyOoJ+BkyZrTMYmIoo1JjNRlqnT4LEpw3wOGoUGPQy9kjGzIMfngFvUfodOpk6DJVNzfQ52rruEVm6rdP98qNqEsQN6+m1faNSjeKwRK7dVupcdqGrAo1OG+W378NVDcbDK5LW8wJCBmQU56JOq9Yn3ULUJj/npyxXb/JIyzBlnRGGH9a79DHUxaWqiCmMG9MSccUafsQs9+khNVGHJ1FwcqjZhZn4O9lc1YGa+7/wWeMyfK47HpgzDoWrvfQ4Wn2usjvsc7j6F04/YmMRITVThVz0S8fDVQ30+m667mdbtPhaTsYmIYo13M8WIV50ZjQLpSb/UmXE4BTicApptDqRqldAn+68z02ixI8ldZ8YGjVIOeYIM8gB1ZhotdqRo2vrzqjPTPr5nLRHPtkDgOjNN1lb0aK8z02RtdW/jWRPFM86GFhuS1L/023GsSOvMuOYqWJ0ZR3v9HhlkbXV82rdJ1rTVmTG12APGH258kWwjpp9o9R9sXK86Mxpl0M8GExki6ipijt9MZoiIiCju8NZsIiIi6jaYzBAREZGkMZkhIiIiSWMyQ0RERJLGZIaIiIgkjckMERERSRofZxChY3XNaLS0wmyxQ5/c9oC+ZrsDzVYHdFoF0hLb6sr83GQDZO3P3BEAyAAZAKcAmC2tSNEooFYkwOpwQK2QQyGTweJwttVLEQAngCZrK5LUCiQAEGSArL0fuSwB9c02JGkUSFTKYWl1wCkAggC02FqRnqiCpdUJs7VtHK1SjjStEqmJqrY6OM02NFpakaJWIEEGKOUJSFQrYGqxo77FjiSVHImqtpgaba0wWx1IT1RCp1HCanNAkAFNNgcaW+xI0SqRqJIjQQAAASpVW20Xh9MJeUICWjzmRp4gQ4Lsl1o5Dc02nGy0wmSxIz1RBbvDCZOlFUkqOZLUCnfMwfxS16etdopamQAIQI8k/zV8zFY70hJVsNqdaLS2jWtzONHY0lZnJ5xxXXG75sq1DQCv5VqlHK2CALnMuz5QOH0mKuWADLC0OtFsbUVqogp6P/tU22SDQxCgkMna6gPZHEgLUJenM7jm2WSxQ6dV+sRMRBRNTGYicLi2Cfet24vSI/VYccP5aHVasHxzhdcTiQuNeswZZ0RaohJLPj6AG0b1x+tfHcYNo/pj1fZKr7YFhgw8eNUQ/P2Tg5h+cQ7kMgGNVgde3ubdzlVh19WP68+bVn6N0Wf3wD2XD8LfPvgOpUfq8dy0PDz5+ffeMRn0mDPeiN46Ne5bt9frCd7jzu2JBRMH4a439+DLDmMWjzXAYnfi7re/xeO/GY5ktRXpSRosfG+fz348fPUQqBJk+Ot7+/CbC/pBo0zwOzezCnLw1GeHcM/l5+L+dfuw60gdnpuWh6c6xJxvyMCccUb075GIPmlav+/Hkdom3Ltur985vX/dXtx35WBkpWlRVd+C+SVl2HW4DstvyMMTnx7CriP1WH5DHp75/Huf/Q42blV9C+a/Xeb11PJ8QwYWXT0USz7ajw0HT/mdw8UfHcCia4YiK4w+E1VyvDz9Ajy/ucIrtiKjHkum5rr36aF39+HGUf0DzvXS9radxTXPnk8H94yZiCjaWDRPpGN1zZhfUobtFbUoHmdAVqoGH+6t9jqAuBQa9Jg4rDeqGiwoPVKHvH7pKD1S57etu+T+9krMGWfA8k0VXsmGS74hw92P558A3H0XjzMEHMcV033r9nktLx5nwLdH6rwOmp5jXjmsDzJ1GpwwWXB2zyQs21QRcD+KxxnQZHXghMkSdG5m5P8KK9sTu2Ax5xsycFVuFq4Y2tvnf/cnTBbMe3NP0Dl95T//w+PXDcddb32LL8trUDzOgD1H6rCtfVzXz+GO29BsQ/HrpV6JjOeY5/VLx/JNFX7nsKrBgrKj9Vg2LS9kn8HmpMiod+/T8Oy0oJ/DIqPeZ7xYaWi2oXhNqVci0xVxEJH0sWheDDVaWt0HjLzsNGTqNH4PIADwZUUNMnUa5GWnYXtFrftPf7ZV1KKXTo3tFbVIUiv8HlwBePXj+adn38HGccXUUV52mt9ExjVmpk6DXjo1MnUaJKkVQfcjSa1wtw0Wh2t/Q8W8vaIWvVLUqDH7Pkm7rskWck63ltegrsnmPsDmZae559fz53DHrTHb/CYyrjHzstP89uX6LGwtrwmrz2Bz4rlPoT6H/saLlRqzzW8i09lxEFH3wq+ZRDK12N0/W1udIdt7tgnV3mxxeP0Zqs+Of4Y7jr/14WxjtjjcfwbjWh/u/oY7fqPF7rPcZGkNawzPdmLeE3/jmvzE0XGbUMvD6TNUbK59Cudz6G/uYiHU3HRWHETUvTCZEUnXfoEnAKgVoU9sebYJ1T5ZI/f6M1SfHf8Mdxx/68PZJlkjR7MtIWR8rvXNtvD2N9zxUzRKn+U6TfCPsGsMz3Zi3hN/4+r8xNFxm1DLw+kzVGyufQrnc+hv7mIh1Nx0VhxE1L3wayaRUjQKFBgyAAClR+txwmRBfvvfOyo06HHSZEHp0XrkGzLcf/pTYMjASZMV+YYMNFlbURignWc/nn+WHq33iitUTB2VHq0POuYJkwUnTVacMFnQZG11j+VvP5qsre62weOwhhVzviEDJxut7idxe0pPUgWN5aTJiiKjHulJKhQZ9T77Gmq//Y2rT/6lL39jlh6t99vXifbPQpFRH1afwebEc59CfQ79jRcrweamM+Mgou6FyYxIfdMT8eiUYSgwZGDltkr0SdWieKzB50BSaGy7c2jEr3rgQFUDZubnYH/7nx3bFhgy8NCkIXh711HMGWeEXCbDzALfdq67mfZ36G/ltkocqGrAg1cNQX57XP7GKTToMXe8EfkGvU8CsL+qAQ9OGoJCg/eBqO1OHCP6pGoxv6QMfVK1kMuAh68e6tNH291MQ5Gl0+Kd3ceCzs2swhys230Mj00ZhkKjPmDMrruKxg7o6ffC0UydBo+1vx/+5nTd7mNYOjUXmToNlkzNRZFrrIIcFBq8fw533NREFZZMzUWh0Xebh68eioPVpoBzeKjahKVTc8Pqc+W2ShSPNfjEVtR+h5Jrnw5Vm4LOtb/xYsW1Hx0TmqJOjoOIuhfezRQhV52ZJqsdGUnedWZStAqke9SZkbXXCfGpM2NtRbJaAY2rzoxcDkWCDFaHEw6nABlkcEJoqzOjUiBBJoOAtuWCTIDCVWdGrUCiyrfOTFqiqu0aF2tbLRmtyn+dmWS1AnKZDEq5zF1npqHFjsQOdWaarA6k+aszY7EjRROozowAeYLMq86MIkEGmZ86M40WO9K0KtidHnVmVAqkJYqpM+OqqZMAIUidmSarHan+6syIGNcVt2uuXNsA8FquVcrhEASv2jrh9uldZ8aBVK0S+mT/dWac7bVsWuwONNva2nZ1nRnXZ6NjzEREoYg5fjOZISIiorjDW7OJiIio22AyQ0RERJLGZIaIiIgkjckMERERSRqTGSIiIpI0JjNEREQkaXycQYSq6pohOAUIAMx2Bxpb2uqbaBQJsDmccAiA2dIKnVbRVm5eAJyAu95KikYBtTIBSpkMTXYHTC2tSFLLoVHKYXc4IZMBGoUcLXaHuxYMAEAmIEmpgNmjvkuSSo4EAHan4K51o9O2tXcKAjQKOexOJwQBUCkSoJLJYGtva7E5kJGkggDvOjlJSjmcgoAmmwNN7TVL1HIZrK1OOAE0tserVbY9LqCu2YZkjQIahRyN1lZ3DIlKOUwWG1I0KjgFAS12B2QyGSAATba2/ZLJZFAkyJCkbqtN02ixIy1RBZurRo5GAbU8AY3WVjTZHEjrUD/FVdOkocWGxPaaOWpFgvu5Sq5lKrkMLa1OmFra3pf0RJXXQzdd/Zgsdui0SuiT2vqvMdtgtnrH5FrvWTvll1o3duja6+5YHA6YW1qRmqjyaR8uf3HFe80WKcZMRNLFZCYCR2ub4HA6IZMl4IH1e91Pm05UyfHy9Avw/OYKr2WrZ4yEUxCwbHOF15ONCw16FI8zoNnmwOzXd6PZ5kC+IQNzxxmQnqTGfev2ej3Redy5PTH/8kG4t8PyQoMeC68ejMc+OoBNB095LZ9Z8Cv8e+dh/LHwHFhbHXht52EsmHguHvvoAHb++DNW3HA+bA4nlneMzahH8VgDZq7+L5ptDiSq5Fg1YySWbyr3erp2W3VbA+ytAupb7Hh5W6XPPv518hBUN7Tgn1t/xA2j+mPVdu82rsrGa746jBsv6g8ZZPj7p4ewvaIWiSo5npuW57ONq7KtDMA9JWVeT2ouNOjxl7HnYNYr36DZ5gi4rMCQgcemDEO/jCRU1bdgfsd+jHrMHmtA8eu7sWRqrjsmlyKjHkum5iIrTYsjtU24d91er/UFhgw8eNUQ/PG1Xagx27zah8tfXJH005mkGDMRSRuL5ol0rK4Zx+qa0StFg4Xv7vM6sBePM6D0SJ3XAa14nAFZqRp8uLfaa7lLoSEDE4f1QVWDBcs3VQAAHpsyFB/vrfbqO1D/LgWGDJzXL93dh2f/w/ulo/RIHa4a1gc/NViw50gdzuuXDgAhYxve3mfxOAP2HKnzSqJc2kr/G7B8U4Xf9Z77GCj+fEMG8vql49sjdZg4rA/uW7cv5D4XGvW4Ylgf3PvO3oD9ec6Hv2UFhgz8/TfDfQ6+ntvcnJ+DlR2SKRfXQfrut78N+L7MzM/BrFe+cbdfNi0vrLMUDc02FK8p9RuXmH46kxRjJqL4xKJ5MdRoaUWSWgFrq9Mn2cjLTvM5oOVlpyFTp/F7oAOALytqkanTIC87zb0sU6fx6TtQ/y7bKmq9+vDs37Vdr/ZxXG3Dic3Vp2s7f7ZX1CJJrQi43nMfA421vX0sV9tw9vnL8hr0SlEH7S/Usm0VtWhosfs9+Lq26aVTB4xha3kNGlrsQd+XXjq1V/sas81v245qzLaAcYnppzNJMWYikj5+zSSSqcUOpwAkyBw+66ytzrCWhWoTaJtQfYXaznN9OHEF2tYfs8V3PsKJLdRYke5zoHX+lpla7EHHCLVvYrdvtARv7+43RLtw++lMUoyZiKSPyYxIOq0SrU4nFAm+J7XUivCWhWoTaJtQfYXaznN9OHEF2tafZI08othCjRXpPgda52+ZTqsMOkaofRO7fYomeHt3vyHahdtPZ5JizEQkffyaSaQUTdsdN2pFAgoNeq91pUfrkW/I8Fl2wmTxWe5SaMjACZMFpUfr3ctOmCwo9NO+9Gg9CgL0U2DI8OrDs39XXCfbx3G1DSc2V5+lR+v9xgS0XVfSZG0NuN5zHwONld8+lqut5z4HjM+ox8lGa9D+Qi0rMGQgVatEkdH7vfTc5qTJGjCGIqMeqVpl0PflpMnq1V6fHN41I/pkVcC4xPTTmaQYMxFJH5MZkfqmJ+KsVC0SIOCRyUNR6PGLe+W2ShSPNXglOSu3VcLQMxlzxhp9DohtdzMZ0SdVi5XbKgG0HTzP6ZmMBycN8TlA7q9qwINX+S4vNOjx8NVDsb+qwWf5zIIc7K9qwJyxRmSlaXGw2uRuu3JbJfqkalE81uAbm7EtNldcK7dVonic0Wt/XfEWjzXCZhcwsyDHb2x/mzwM5/RMwv6qBszMz/EZy3U304GqBswsyEGfVK27zcptlZiZ76dfox5/n5qLMQN6+hw8Cw16FI/9JfZAy1x3M2WlabFkaq5vP0Y95owzYn5Jmd+4i9rvqMpK0+KxKcN8YiwwZOChSUMwv6TMq324F8CmJqr8xiW2n84kxZiJSPp4N1OEfOrMtNeC0XrWmbH+UnsGAJzCL3VmktuXKxN+qTOT2F63xe5wAjJA61FnJkmtgAzwW2cmUSWHHB51ZmxtdWxk8K4z4xTavmYJWmfG5kCKWoEk1S91ZpptDug0SqgVHnVmLK1IVLXFK0NbnZkkjQJajzozrno1jRYbktUqONFWZyZBJoPQXmcmqb0GjNyjzozZYkeqVgWbw4kmayuSPerMNLfXvPFfZ8beNhcJMqjlCbA6nO445Qkd6sxoFEhP8l9nxjWvrrMINWYbmqzeMbnW+68z0/a+J6nksLQ6YLa0xdyxfbj8xRXvSYEUYyai+CLm+M1khoiIiOIOb80mIiKiboPJDBEREUkakxkiIiKSNCYzREREJGlMZoiIiEjSmMwQERGRpEnicQYrVqzA448/juPHj2P48OFYtmwZLrzwwi6N6XhdMyztz/kRAFhaHZC1VYKBub02SpI8AUiQwWxzwNRiR2qiEslK+S/1YKxt9WASVXK0tDpgbmlFjyQVrA4nTC1t9TmSVHI4nAJ+brIhWdP2dsnaa9C0OgVYWp0wW1qR1F6jRp0gQyuAJpsDZksrdFoFFAkJqG+2IUWrgDKhrQ6OALTVemmvhaOSJ+DnJhsS1QokyNrGkMsSYGqxIS2xPaZmO1K0SiSr5NBplUhNVLnriZgsdqRqlUhWK9Bic6DJ1oommwNp7TVhgLZ6LWarHWmJKthanWi02JGk/mVsrUqOJHVbTZkWu8M9R6mJSmS215XxHC+5fdv6FhuSNUrok1Q+bXTaX5aLFW4/v9S5cc2fDIoEGTIiHFdsbGlaJdRKOUwtdpja3/P0RO8aOvEsWu8XEXVfcZ/MvPHGG5g3bx7+8Y9/YNSoUXjmmWdw2WWX4dChQ+jVq1eXxHS0tgnVDS1ISJCh1SHgX1/+iBtG9ceq7ZXYXlGLRJUcL900Apr0RNy/bq/7icqJKjlenn4Bnt9c4fVU7EKDHrcU5sAhCHhqw/deT2AuMGTg4auHoMnqwE0rv8aIful46OrBqDZZsGxjuVc/+YYMLLp6KJZ8tB8bDp7yWj6rIAcmSyte2/k/r1g928zMz8FNK79GXr80zMzPQcmuo7h9wkA88O4+n5gemTwMTS12PPTBfmw4cBKJKjlW3HA+NMoELN9c4dW+0KhH8VgDZr++G0um5uLvnx4KOPZFZ/fAfVcMwqL3vvPatwmDeuGhqwbj/vX7vJ7K7Np22otfIf+cDDzop02RUY8l7ZV6w1VV34L5JWUh+/HXzhXT4o8OYNE1Q0WNKzY2fbIK/3fLRXjg3W993qPHpgxDv4ykqI4dbeHOMxFRMHFfNG/UqFEYOXIkli9fDgBwOp3Izs7GnDlzsGDBgpDbR7to3rG6Znz5/SkIAGQAPthbjbx+6Sg9Uuc+mBSPM+DavLPwYIckoHicwaudp8VThuLDvdXY5mddgSEDxeMM2FZRi+WbKrB4ylB8tLfa62Dv2fa8fulYvqnCa3mhIQMTh/VBVYMlYAz5hgzktW+bb8jAzfk5WNkh6fEc56/XDMU7pT9h+aYKFI8zICtVgw/3VvttX2jIwIwg/bnGBuA3vmBzF2pboO0AuWxaXlj/429otqF4TanXAdZfP8HauWIqO1of9rjh6Djmy9MvCPoePfnb8+L2DE2480xE3dMZUzTPZrNh165dmDBhgntZQkICJkyYgB07dvjdxmq1wmQyeb2iqdHSil46DTJ1GvTSabC9ohZ52WleB5O87DRYW50+B5iO7Tz10mn8JjIAsK2iFklqBfKy09xt/SUyrraudp6+rKhFpk4TNIbtHttur6hFL506YNttFbWwtjrd7fOy05DZPh/+fBmiP9fYgeILJ+5gbbaW16DGbPO7rqMas83vAbZjP8HauWISM24ksYV6j+qaojd2tIU7z0REocR1MlNTUwOHw4HMzEyv5ZmZmTh+/LjfbRYvXozU1FT3Kzs7O6oxmVrssLY63S8A7j9drK1t17x01LFduOsAwGxxBBwv3L48Yw5nW7PFEbRtY/tchNt3qP6C9RFO3KHaNFp83xN/TCHaufoJ1c4VT7jjhqPjmKHm1GRpjdrY0RbuPBMRhRLXyUwk7r33XjQ0NLhfR48ejWr/Oq0SakWC+wXA/aeLWpEAnVbps23HduGuA4BkjTzgeOH25RlzONsma+RB26a0z0W4fYfqL1gf4cQdqk2Kxvc98UcXop2rn1DtXPGEO244Oo4Zak51mvi9LC7ceSYiCiWukxm9Xg+5XI4TJ054LT9x4gR69+7tdxu1Wg2dTuf1iqYUjQInG604YbLgpMmKfEMGSo/WI9+Q4W5TerQeakUCCjyWuZbnd1jmctJkQWGAdQWGDDRZW1F6tN6jrT5gW1c7T4WGDJwwWYLGkO+xbb4hAydNVp998BxHrUhwty89Wo8TJkvAvgvb+ws1dunRer9jBloezrZA2zUYrqdgh6JPVqHI6H9+PfsJ1s4Vk5hxI4kt1HuUnhS/15yEO89ERKHEdTKjUqkwYsQIbNy40b3M6XRi48aNGD16dJfE1Dc9EQXnZMDQKxnGzCTMGWfE/qoGzMzPcR+oV26rxPH6FjwyeZjXgWbltkoUjzX4JCKFBj2y0rSYWZDjc7Bvu5tpKGx2ASu3VaLQoMcFOT0wZ7xvP/ntbQ9WmXyWzyzIQZ9ULQ50iNWrTX4OVm6rdP/89q6jePCqIX5jenTKMGgSZDhUbXLvW59ULYrHGnzaFxr1KB5nxPySspBj769qwMNXD/XZt4PVJjw2ZZjPwc9z20MB2hQZ9Vg6NTfsi0lTE1VYMjU3ZD+B2rliOlRtEjVuJLHNLynDg1cN8UloXHczxevFv0D480xEFErc3830xhtvYPr06fjnP/+JCy+8EM888wzefPNNHDx40OdaGn+ifTeTi9g6M42WtroxKaogdWYsrUhPVMHmr85Msw1JakXbCDIgsUOdmcT2OjMazzoz7TVklAmuWiwKqNrrzACAs2OdmWYbElVtdWYgAxQd68y0x+Svzkxje40QzzozzTYHUjvUmWmy2pGqbdvHxva41Z51ZlQKqBUd6sxolcjUqX3Gc9WoaWixIUmthD5Z5dMmRfPLcrHC7eeXOjN2JKrkkCfIIO+kOjON7fV9vOrMaBRIT5JenZnTfb+I6Mwi5vgd98kMACxfvtxdNO+8887Dc889h1GjRoW1baySGSIiIoqdMy6ZOR1MZoiIiKTnjKkzQ0RERBQKkxkiIiKSNCYzREREJGlMZoiIiEjSmMwQERGRpDGZISIiIkljMkNERESSxmSGiIiIJI3JDBEREUmaoqsDiDVXgWOTyRSiJREREcUL13E7nAcVnPHJTGNjIwAgOzu7iyMhIiIisRobG5Gamhq0zRn/bCan04mqqiqkpKRAJpNFtW+TyYTs7GwcPXqUz30KgHMUHs5TeDhP4eE8hYfzFJ6umidBENDY2IisrCwkJAS/KuaMPzOTkJCAvn37xnQMnU7HfwghcI7Cw3kKD+cpPJyn8HCewtMV8xTqjIwLLwAmIiIiSWMyQ0RERJLGZOY0qNVqLFy4EGq1uqtDiVuco/BwnsLDeQoP5yk8nKfwSGGezvgLgImIiOjMxjMzREREJGlMZoiIiEjSmMwQERGRpDGZISIiIkljMhPCihUr8Ktf/QoajQajRo3C119/HbT9W2+9hXPPPRcajQbDhg3DRx991EmRdh0xc7R69WrIZDKvl0aj6cRou8bWrVsxadIkZGVlQSaTYf369SG3+eKLL3D++edDrVbDYDBg9erVMY+zq4mdpy+++MLn8ySTyXD8+PHOCbgLLF68GCNHjkRKSgp69eqFyZMn49ChQyG3626/myKZp+74++mFF15Abm6uuyDe6NGj8fHHHwfdJh4/S0xmgnjjjTcwb948LFy4ELt378bw4cNx2WWX4eTJk37b/+c//8G0adMwa9YslJaWYvLkyZg8eTL27dvXyZF3HrFzBLRVkayurna/Dh8+3IkRd42mpiYMHz4cK1asCKt9ZWUlrrzySowdOxZ79uzB7bffjltuuQWffvppjCPtWmLnyeXQoUNen6levXrFKMKut2XLFsyePRs7d+7E559/DrvdjksvvRRNTU0Bt+mOv5simSeg+/1+6tu3L5YsWYJdu3bhm2++wbhx43DNNdfgu+++89s+bj9LAgV04YUXCrNnz3b/3eFwCFlZWcLixYv9tv/tb38rXHnllV7LRo0aJfzpT3+KaZxdSewcrVq1SkhNTe2k6OITAGHdunVB29xzzz3CkCFDvJb97ne/Ey677LIYRhZfwpmnzZs3CwCEurq6TokpHp08eVIAIGzZsiVgm+74u6mjcOaJv5/apKenCy+99JLfdfH6WeKZmQBsNht27dqFCRMmuJclJCRgwoQJ2LFjh99tduzY4dUeAC677LKA7aUukjkCALPZjP79+yM7Ozvo/wC6s+72WTpd5513Hvr06YNLLrkE27dv7+pwOlVDQwMAoEePHgHb8PMU3jwB3fv3k8PhwNq1a9HU1ITRo0f7bROvnyUmMwHU1NTA4XAgMzPTa3lmZmbA7+OPHz8uqr3URTJHAwcOxMqVK/Huu+/i3//+N5xOJy6++GIcO3asM0KWjECfJZPJhJaWli6KKv706dMH//jHP1BSUoKSkhJkZ2djzJgx2L17d1eH1imcTiduv/125OfnY+jQoQHbdbffTR2FO0/d9ffT3r17kZycDLVajT//+c9Yt24dBg8e7LdtvH6WzvinZlN8GT16tFfGf/HFF2PQoEH45z//ib/97W9dGBlJ0cCBAzFw4ED33y+++GL88MMPePrpp/Haa691YWSdY/bs2di3bx+2bdvW1aHEtXDnqbv+fho4cCD27NmDhoYGvP3225g+fTq2bNkSMKGJRzwzE4Ber4dcLseJEye8lp84cQK9e/f2u03v3r1FtZe6SOaoI6VSiby8PFRUVMQiRMkK9FnS6XTQarVdFJU0XHjhhd3i81RcXIwPPvgAmzdvRt++fYO27W6/mzyJmaeOusvvJ5VKBYPBgBEjRmDx4sUYPnw4nn32Wb9t4/WzxGQmAJVKhREjRmDjxo3uZU6nExs3bgz4XeLo0aO92gPA559/HrC91EUyRx05HA7s3bsXffr0iVWYktTdPkvRtGfPnjP68yQIAoqLi7Fu3Tps2rQJOTk5Ibfpjp+nSOapo+76+8npdMJqtfpdF7efpS69/DjOrV27VlCr1cLq1auF/fv3C3/84x+FtLQ04fjx44IgCMJNN90kLFiwwN1++/btgkKhEJ544gnhwIEDwsKFCwWlUins3bu3q3Yh5sTO0aJFi4RPP/1U+OGHH4Rdu3YJ119/vaDRaITvvvuuq3ahUzQ2NgqlpaVCaWmpAEB46qmnhNLSUuHw4cOCIAjCggULhJtuusnd/scffxQSExOFu+++Wzhw4ICwYsUKQS6XC5988klX7UKnEDtPTz/9tLB+/XqhvLxc2Lt3r3DbbbcJCQkJwoYNG7pqF2Lu1ltvFVJTU4UvvvhCqK6udr+am5vdbfi7KbJ56o6/nxYsWCBs2bJFqKysFMrKyoQFCxYIMplM+OyzzwRBkM5niclMCMuWLRP69esnqFQq4cILLxR27tzpXvfrX/9amD59ulf7N998UxgwYICgUqmEIUOGCB9++GEnR9z5xMzR7bff7m6bmZkpXHHFFcLu3bu7IOrO5bqFuOPLNTfTp08Xfv3rX/tsc9555wkqlUo4++yzhVWrVnV63J1N7DwtXbpUOOeccwSNRiP06NFDGDNmjLBp06auCb6T+JsfAF6fD/5uimyeuuPvp5tvvlno37+/oFKphJ49ewrjx493JzKCIJ3PkkwQBKHzzgMRERERRRevmSEiIiJJYzJDREREksZkhoiIiCSNyQwRERFJGpMZIiIikjQmM0RERCRpTGaIiIhI0pjMEBERkWhbt27FpEmTkJWVBZlMhvXr14vuQxAEPPHEExgwYADUajXOOussPProo6L7YTJDRN3O//73P8hkMuzZs6erQyGSrKamJgwfPhwrVqyIuI/bbrsNL730Ep544gkcPHgQ7733Hi688ELR/bACMBEFNWPGDNTX10f0v6545XA4cOrUKej1eigUiq4Oh0jyZDIZ1q1bh8mTJ7uXWa1W3H///VizZg3q6+sxdOhQLF26FGPGjAEAHDhwALm5udi3bx8GDhx4WuPzzAwRxR2HwwGn0xmz/uVyOXr37s1EhiiGiouLsWPHDqxduxZlZWW47rrrcPnll6O8vBwA8P777+Pss8/GBx98gJycHPzqV7/CLbfcgp9//ln0WExmiChsY8aMwZw5c3D77bcjPT0dmZmZePHFF9HU1ISZM2ciJSUFBoMBH3/8sXubL774AjKZDB9++CFyc3Oh0Whw0UUXYd++fe42q1evRlpaGt577z0MHjwYarUaR44cgdVqxV133YWzzjoLSUlJGDVqFL744gv3docPH8akSZOQnp6OpKQkDBkyBB999BEAoK6uDjfeeCN69uwJrVYLo9GIVatWAfD/NdOWLVtw4YUXQq1Wo0+fPliwYAFaW1u99n3u3Lm455570KNHD/Tu3RsPP/ywe70gCHj44YfRr18/qNVqZGVlYe7cuVF+B4ik4ciRI1i1ahXeeustFBYW4pxzzsFdd92FgoIC97/DH3/8EYcPH8Zbb72FV199FatXr8auXbvwm9/8RvR4/G8JEYnyyiuv4J577sHXX3+NN954A7feeivWrVuHKVOm4L777sPTTz+Nm266CUeOHEFiYqJ7u7vvvhvPPvssevfujfvuuw+TJk3C999/D6VSCQBobm7G0qVL8dJLLyEjIwO9evVCcXEx9u/fj7Vr1yIrKwvr1q3D5Zdfjr1798JoNGL27Nmw2WzYunUrkpKSsH//fiQnJwMAHnzwQezfvx8ff/wx9Ho9Kioq0NLS4neffvrpJ1xxxRWYMWMGXn31VRw8eBB/+MMfoNFovBKWV155BfPmzcNXX32FHTt2YMaMGcjPz8cll1yCkpISPP3001i7di2GDBmC48eP49tvv43dG0EUx/bu3QuHw4EBAwZ4LbdarcjIyAAAOJ1OWK1WvPrqq+52L7/8MkaMGIFDhw6J++qpKx/ZTUTxb/r06cI111wjCIIg/PrXvxYKCgrc61pbW4WkpCThpptuci+rrq4WAAg7duwQBEEQNm/eLAAQ1q5d625TW1sraLVa4Y033hAEQRBWrVolABD27NnjbnP48GFBLpcLP/30k1c848ePF+69915BEARh2LBhwsMPP+w37kmTJgkzZ870u66yslIAIJSWlgqCIAj33XefMHDgQMHpdLrbrFixQkhOThYcDofffRcEQRg5cqQwf/58QRAE4cknnxQGDBgg2Gw2v2MSnckACOvWrXP/fe3atYJcLhcOHjwolJeXe72qq6sFQRCEhx56SFAoFF79NDc3CwCEzz77TNT4/JqJiETJzc11/yyXy5GRkYFhw4a5l2VmZgIATp486bXd6NGj3T/36NEDAwcOxIEDB9zLVCqVV9+e/7NLTk52v7Zs2YIffvgBADB37lw88sgjyM/Px8KFC1FWVube/tZbb8XatWtx3nnn4Z577sF//vOfgPt04MABjB49GjKZzL0sPz8fZrMZx44d87vvANCnTx/3fl533XVoaWnB2WefjT/84Q9Yt26d19dURN1JXl4eHA4HTp48CYPB4PXq3bs3gLZ/Y62tre5/zwDw/fffAwD69+8vajwmM0QkiutrIReZTOa1zJUQiL2AV6vVeiUTZrMZcrkcu3btwp49e9yvAwcO4NlnnwUA3HLLLfjxxx9x0003Ye/evbjggguwbNkyAMDEiRNx+PBh3HHHHaiqqsL48eNx1113RbTPLv723bWf2dnZOHToEJ5//nlotVr85S9/QVFREex2+2mNSRSvzGaz+98lAFRWVmLPnj04cuQIBgwYgBtvvBG///3v8c4776CyshJff/01Fi9ejA8//BAAMGHCBJx//vm4+eabUVpail27duFPf/oTLrnkEp+vp0JhMkNEnWLnzp3un+vq6vD9999j0KBBAduH8z87oC2J+POf/4x33nkHd955J1588UX3up49e2L69On497//jWeeeQb/+te//I41aNAg7NixA4JHpYrt27cjJSUFffv2DXsftVotJk2ahOeeew5ffPEFduzYgb1794a9PZGUfPPNN8jLy0NeXh4AYN68ecjLy8NDDz0EAFi1ahV+//vf484778TAgQMxefJk/Pe//0W/fv0AAAkJCXj//feh1+tRVFSEK6+8EoMGDcLatWtFx8ILgImoU/z1r39FRkYGMjMzcf/990Ov13vVpOjI8392Tz75JPLy8nDq1Cls3LgRubm5uPLKK3H77bdj4sSJGDBgAOrq6rB582Z3gvTQQw9hxIgRGDJkCKxWKz744IOAydNf/vIXPPPMM5gzZw6Ki4tx6NAhLFy4EPPmzUNCQnj/51u9ejUcDgdGjRqFxMRE/Pvf/4ZWqxV9upxIKsaMGeP1H4COlEolFi1ahEWLFgVsk5WVhZKSktOOhckMEXWKJUuW4LbbbkN5eTnOO+88vP/++1CpVEG3WbVqFR555BHceeed+Omnn6DX63HRRRfhqquuAtBWj2b27Nk4duwYdDodLr/8cjz99NMA2q7Buffee/G///0PWq0WhYWFAf/Hd9ZZZ+Gjjz7C3XffjeHDh6NHjx6YNWsWHnjggbD3Ly0tDUuWLMG8efPgcDgwbNgwvP/+++47N4godlgBmIhi6osvvsDYsWNRV1eHtLS0rg6HiM5AvGaGiIiIJI3JDBEREUkav2YiIiIiSeOZGSIiIpI0JjNEREQkaUxmiIiISNKYzBAREZGkMZkhIiIiSWMyQ0RERJLGZIaIiIgkjckMERERSdr/Axqsn5+ybrNiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, x='Impressions',y='Approved_Conversion')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CLICSK VS APPROVED CONVERSIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Clicks', ylabel='Approved_Conversion'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, x='Clicks',y='Approved_Conversion')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CLICK VS IMPRESSIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Impressions', ylabel='Clicks'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TYbBD1MlZz8QUjFPgxn7dMWNUT5sk5VxFAu4anurwXu60l2CwQ0SdDXN2iDoxezMxGw6W4sHxfewmKR8sqcHj24sc5uCwvQQRBSMGO0SdmL2ZGLVWj+oGrcMkZWcNQGUu2kewvQQRdUYMdog6MUczMc0teqePczRDI4+WYEyG3O45tpcgos6KwQ5RJ+ZoJqatDUBjIiVYPX2wTcDD9hJE1JkxQZmoEzPOxOy3WsoqvFSHXEUCDtpZynI1Q5MSG4G1M7JQ3aBFfXMLuoWH+ay9hPmuMllEGORRbGtBRO3HYIeoEzPOxCzedsIi4Dl3WYW/3TYIS3ectDju7gxNTKTvgwzW9yEibxEJgiD4exD+plKpEBMTA6VSCZlM5u/hEHnMOCNiPRPj6HigUaq1KNhSaHfb+5gMOev7EJFd7n5+c2aHKAg4monxxwxNW7C+DxF5ExOUicjvWN+HiLyJMztEXUwgJgGzvg8ReRODHaIuJFCTgB3tKgNY34eI2o/LWERdhKsmn45aSPgC6/sQkTdxZoeoiwj0JGB/1vchouDGYIeoE2lPvk1nSALuLLvHiKhzYbBD5GNtDVjam2/DJGAi6qoY7BD5UFsDFlf5Nu4U3WMSMBF1VUxQJvKR9iQIu5Nv4wqTgImoq+LMDpGPtCdBuKPybZgETERdEYMdIh9pT8DSkfk2TAImoq6Gy1hEPtKegMWYb2MP822IiJxjsEPkI+0JWJhvQ0TUdiJBEAR/D8Lf3G0RT9Re5XVNWLzthMWOKGPA0sON7ePGbevMtyEicv/zmzk7RD7U3gTh9uTbBGIDUCIiX2CwQ+Rj/kgQDtQGoEREvsCcHaIAoFRrcb6yAYVltThf1dChTTkDuQGop7z5PhFR8OLMDpGfeXvWJdAbgLqLs1NE1Fac2SHyo46YdXE129EZGoC6EkyzU0Tke5zZIfKjts66KNVa1DRqIQB46oOTOFBSYzpnPdsRDA1Ag2V2ioj8gzM7RH7UllmX8romFGwpxPuFP2OZVaAD2M52BENBwmCYnSIi/2lTsHPs2DEUFRWZvv/ggw8wbdo0PP7449BqOZ1MXZejJSVHxz2ddTFfzslKjcUhq0DHyLw5aDAUJAyG2Ski8p82LWPdd999WLx4MQYNGoQLFy7grrvuwm233YZ3330XarUaL730UgcPkyjwOUqgfWbaQKzcdRqfn6m0OL56+mDTrMt+O0s09mZdzJdzNDqD0/GYz3Z09gagnr5PRETm2jSz88MPP2Do0KEAgHfffRdjxozB22+/jU2bNmHbtm0dOT6iTsFZAu3j24vQr4fM5vjibScAwKNZF/PlHGmo83++jmY7BAAQOX1owAmG2Ski8p82zewIggCDofV/lZ9//jkmT54MAEhNTUV1tf0kQqJg5iyB9mBJDWbnpNscNy419U6MdnvWxXw5p/BSHXIUCXaXsqxnOwJx27anFZ07++wUEflPm4Kd4cOH45lnnsH48eOxb98+vPbaawCA0tJSJCUldegAiToDVwm0jpacjEtN7lZVNl/O2XCwFGtmZAGARcBjPdvhatv22hlZbQoY2tN+oq3Blz+qTxNR59emYOell17CzJkzsWPHDjzxxBNQKBQAgPfeew/XXXddhw6QqDNwlUDraMnJ08Ra43KOsZnoA1sKMSc3HQvGKiANC0FshMRmtsOTbdvuBjDtmSnyVvBFRORIm4KdwYMHW+zGMnruuecgFovbPSiizsZZAm2uIgGFl+psjrc1sdbT5Rx3t227G8C0N1hhzRwi8rU2JSg/99xzdo+HhYXhnnvuadeAiDojZwm0f7ttEM5dVtkcb09ibUykBL0TozE0LQ69E6Od3sedbdueVCh2J1hxhjVziMjX2jSz89xzzyE+Ph5z5841HdPr9bjrrrtw8uTJDhscUWfibMbl+TuH+C2x1p1t257MtrQ3WGHNHCLytTYFO7t378bNN9+MmJgY3HHHHdDpdPj973+Ps2fP4osvvujoMRJ1Go4SaP2ZWGud52NkPrt0obrR6T3MA5j2BiusmUNEvtamYGfEiBHYtm0bpk2bBolEgvXr16OkpARffPEFd2MRBSBXeT6eBDDtDVbcCb6IiDqSSBAEoa0P3rFjB+688070798fe/fuhVxuv/9OoFOpVIiJiYFSqYRMJnP9AKIgo1RrsXBLocMAxjrpuLyuyWGw0sPNuj3GnV+smUNEbeXu57fbwc7tt99u9/hXX30FhUJhEei8//77Hg7XvxjsEHkewDgLVtpTg4eIyF3ufn67vYwVExNj9/iECRM8Hx0ROeXtYMHe/T3d0u4oDykQqzUTUdfWrmWsYMGZHQok3g4WvHl/pVqLgi2Fdnd22VsOIyJqD3c/v9tUZ6e0tBTFxcU2x4uLi/Hjjz+25ZZEBNcF+8zr3QTi/dtbg4eIyBvaFOzMmjULhw8ftjn+9ddfY9asWe0dE1GX5e1gwdv3Z8FAIgpEbdp6XlhYiJycHJvjo0ePRkFBQbsHReQrgZZI6+1gwdv3b28NnkD7+7AW6OMjIvvaFOyIRCLU19fbHFcqldDr9e0eFJEvBGIirberC3v7/u2pwROIfx/mAn18RORYm5axxowZg1WrVlkENnq9HqtWrUJubm6HDY7IW7ydu9JWxmDBno6oLuzt+zvrEeasYGCg/n0YBfr4iMi5Ns3sPPvssxgzZgz69u2LvLw8AMCBAwegUqmwd+/eDh0gkTf4q/O2q2UQb1cX9kX1Yk+3sAOB3wk90MfnbVy+o86uTcFOZmYmTpw4gVdeeQXff/89IiIicM8996CgoADx8fEdPUaiDuePRFp3l0HaEix4wtv3BzzvBRboic2BPj5v4vIdBYM2LWMBQEpKCv72t79h9+7deO+997Bs2TKPA51Vq1ZhxIgR6NatGxITEzFt2jScO3fO4prm5mYsWLAACQkJiI6OxvTp03HlyhWLa8rKyjBp0iRERkYiMTERjzzyCHQ6XVtfGnUBvu687ekySEykBL0TozE0LQ69E6M7/H/R3r6/pwK9E3qgj89buHxHwcLtYOfEiRMwGAymPzv7cte+ffuwYMECfPXVV/jss8/Q0tKCm2++GY2Nv3Vgfvjhh7Fz5068++672LdvH8rLyy1aV+j1ekyaNAlarRaHDx/G5s2bsWnTJixbtsztcVDX463cFaVai/OVDSgsq8X5qgbThwHrzzjn7Vyi9gr08XkLf24pWLhdQTkkJAQVFRVITExESEgIRCIR7D1UJBK1eUdWVVUVEhMTsW/fPowZMwZKpRLdu3fH22+/jTvuuAMAcPbsWfTv3x9HjhzB6NGj8fHHH2Py5MkoLy83dVz/5z//icceewxVVVWQSGx/CWk0Gmg0GtP3KpUKqamprKDcxXREM0vr+zma7q+qb8bUdba1qYz+775sJERLunQuREf/fXS0QB+fNxSW1eK2Vx3/3O6Yfx2GpsX5cEREljq8N1ZpaSm6d+9u+rM3KJVKADAthx09ehQtLS0YP3686Zp+/fohLS3NFOwcOXIEgwYNMgU6QGu/rnnz5uHUqVPIysqyeZ5Vq1ZhxYoVXnkN1Hl0ZO6Kq+n+p6cORKREjDm56chKjYVGZ0B4mBjHymqx4WAp6ptb8Pt/HenSuRC+yCVqj0Afnzd01eU7Cj5uBzs9e/a0++eOYjAY8NBDDyEnJwcDBw4EAFRUVEAikSA2Ntbi2qSkJFRUVJiuMQ90jOeN5+xZsmQJFi1aZPreOLNDXY8nibT2dqQArVP9Gp3e6XR/i8GADbNGYO3eYryyt8R0LkeRgPX5w/HNj7+Yrl287YRfe0j5c+eNp4nNvhbo4+to7ambRBRI3A52PvzwQ7dv+rvf/c7jgSxYsAAnT57EwYMHPX6sp6RSKaRSqdefh4KHvSWqvAw5FtygwJxN3+L5O4c4fbwgAOv2luBQSY3F8UMlNQiBCMN6/bYU4M+tzNx5Q+Z8UaqAyBfcDnamTZvm1nVtydkpKCjArl27sH//flx99dWm48nJydBqtairq7OY3bly5QqSk5NN13zzzTcW9zPu1jJeQ9QejpaoDhRXwyAImJObDmmo81x/vUHAgRL7Mz8HSqoxK6eXxTF/bGV2tRTHjuVdU1dcvqPg4/ZuLIPB4NaXJ4GOIAgoKCjA9u3bsXfvXqSnp1ucHzZsGMLCwrBnzx7TsXPnzqGsrAzZ2dkAgOzsbBQVFaGystJ0zWeffQaZTIbMzEy3x0LkiLMdKYdKapCVGovCS3XIUSTYvWZMhhxqrfNSCBqdweJ7f+RCuLPzxtFuMwpugVaqgMhTHtXZ2bt3LzIzM6FSqWzOKZVKDBgwAAcOHHD7fgsWLMCbb76Jt99+G926dUNFRQUqKirQ1NQEAIiJicHcuXOxaNEifPHFFzh69Chmz56N7OxsjB49GgBw8803IzMzE3fffTe+//57fPrpp1i6dCkWLFjApSrqEK4Kyml0Bmw4WIrZOek2AY9puj/C+YeD+cyQv3IhXL3OuiYtCrYU4sYX9uG2Vw/jxn/sw8IthSiva/LRCImI2sajCsovvfQS/vSnP9nd3hUTE4P77rsPL7zwgqmFhCuvvfYaAGDs2LEWxzdu3IhZs2YBAF588UWEhIRg+vTp0Gg0mDBhAl599VXTtWKxGLt27cK8efOQnZ2NqKgo5OfnY+XKlZ68NCKHXO1IkYaGQK3V44EthZiTm445OemIiQhDXKTENN2vVGsdJnrmKBJQeKkOgH9zIVy9Tk2LgUtcRNQpuV1nB2jdhfXJJ5+gf//+ds+fPXsWN998M8rKyjpsgL7g7j596pzau7tIqdZi4ZZCh4FKVlqcxQ6rMRlyux/+juq0rJw6EKomLaKk/s2FcPY68zLkGJIaa/E6ze1ZdD16J0Z7e4hERBY6vM4O0Jr4Gxbm+H9/oaGhqKqq8uSWRF7VEbuLHO1IMd+NZX5vRzMzzhM9o9r+IjuIs503y383AFPWOt4pGcy9oYio8/Mo2Lnqqqtw8uRJKBQKu+dPnDiBHj16dMjAiNqrI3cXOQpUAGBnQa7bu1QCvU6Lo9dZ06iFWut48wGLyxFRIPMo2Ln11lvx5JNP4pZbbkF4eLjFuaamJixfvhyTJ0/u0AEStZWr3UWV9RqPAg9HgUogBy9t4eh1srgcEXVWHgU7S5cuxfvvv48+ffqgoKAAffv2BdCaq7Nu3Tro9Xo88cQTXhkokadc7S4q+0WNKGmo34rl+bNSsadYXI6IOjOPgp2kpCQcPnwY8+bNw5IlS0yNQEUiESZMmIB169bZtG4g8hdXu4sA+G0nUWesVMzickTUWXkU7ACtO7I++ugj1NbWoqSkBIIgICMjA3Fx7HxLgcVZXx/jdu/vLtaiTt3i0xmWzlypONBzjoiI7PE42DGKi4vDiBEjOnIsRB3KuPRiHVjkKBIwOycdi7edwJoZWVi6owgHzHpWeXuGxZ1KxQwoiIg6TpuDHaLOICU2As9MHYiSqgZodAZIQ0NQeKnOVABw46FSm+ac3p5hcZVL5K1t3J0pR4iIqCMx2KGgFxsZhs2Hf7RZzspyUiTPmzMsrnKJvLGNuzPmCBERdRSPemMRdUbG5awxGXKPHuetGRZjLpE93tjG7SpHiM08iSjYcWaHugR7O4kMLjqleKtQnq+3cTNHiIi6OgY71GVY7yRy1pzTfIbFG7ku7dnG7el4/JUjREQUKBjsUJflzgyLea5LpESMObnpuO6aBEhCQxAXJWlX4GP+OFVzCyCyPW6tLbk3/sgRIiIKJAx2qEtzNsNinusSKRFjzYwsbDxUatPhvK1Jvp4GLm2tz+Os3hBbPRBRV8AEZeryYiIl6J0YjaFpceidGG0KGMxzXVxtU/c0ybctScPu5N44en32ErTZ6oGIugrO7BA5YJ7r4mibeqREjMGpsbisbMaF6ka3c3qcBS6Oqjo3aJzn1tQ1OQ642OqBiLoyBjtEDpjnumh0Bpvz7VnacpQ0bLynvarOK6cORKREDLVWb/exmhYDlGrHO6vY6oGIuiouY1GXoVRrcb6yAYVltSi+Uo+L1Y34/lItzlc12F02Mq+HIw21/afSnqUtR0nDxnsesHPPZR+cxJOTM+0+LkeRgMMXahwuZRERdWUMdiioKdVaXKhqwPmqBhS8fQw3vrAPt716GDe9uB+P7yhCVYMWU9YexMIthSiva7J4rHmuS+GlOuQoEizOZ6XG2gQ6Rs5yaADHhQVd3TMrLdZmHMZeXxsOlnIbORGRHVzGoqBl3O00JDUWhWW1NkGE8fs5uel4ZW8JHtt2As9MG4jYiDDTco8x16WmUYvbsq7CUx+eMuXa2FvaMmcv8DCvkbN0UiaOltXi6V2nHS5NWWto1iErLQ5zctJten2ptXpuIycisoPBDgUl891Os67r5bAH1qGSGszJSQcAHCiuRkllAzYf/tEi58Y81+UVsyTf8DCx0zHIIsIsgpsoSahNcDMmQ46PHsiDqkmLKKnrqs4xEWE4canO7uvhNnIiIvsY7FBQMt/t5GoGxvy8RmfAdxdrse+HKgzvGYcGjc5ih5V54OOsAvNN/RMhEYegYEuhxa6rHEUC1szIMs3EGHNxjDVyXFV1TuwmbVOrCXY8J6KujMEOBSXz3U72kovNmZ+PDPtth9WS94tMx+3tsHJWgfmp3w3A4veLbLaXWy+dAZb9qdyp6hwTCY+2kbPjORF1dQx2KCiZ73YyJhfbS/zNUSSg8FKd6c96QXC6w8q6SrGj+jXO6uiYL50Zmef3uFMTx91t5G2tukxEFEwY7FBQMm+RsOFgKdbMyAIAiyDGuIvpgS2Fpj+LRCKXO6ysgwN7gceF6kan47NeWrNOLO6omjjseE5ExGCHgpT1ctADWwoxJzcdC8YqIA0LgSw8DGFiEX6sVmPtjCzTjqbn7xzi9L7ubu121XzTfOnMm4nF7HhORMRgh4KYO8tBklCxRX6Mq/wed7d2O2u+ab505k5/qvYkF7PjORERgx0KcvaWg6yDh+fuHIJGjQ6qphbERXZMh3BnicYrpw6EqkmL24Ze5bI/lbPk4iiJ2GUQxI7nRESASBBcFPboAlQqFWJiYqBUKiGTyfw9HPIid3Ymldc1OdwN1cPD3UvGwKotzTeVaq3N1nWjvAw5bh3Uw+WOsY5+PUREgcTdz28GO2Cw01U4Cx7GZMgtdia1J0ixfs62LkGdr2zAjS/sc3h+ff5wzN38ndPXYT0OdjwnomDi7uc3l7Goy/BkZ1JH7IZqb30bV8nF9oolerJjjIioq2CwQwHHW9V+XQUPtWotlOqO2YrdEfVtXCUXh4eJUTBOgazUWGh0BoSHiXGsrBaNGvuvk1WUiairYrBDAcWb1X5dBQ/KphYs3FLYIc/VEfVtXO3okkdLUFhWa9EnK08hx7ShV+FCVQMSzIIZVlEmoq7M+T5bIh9yNRuiVGvbdX9j8GCPcTt4Rz1XR9S3Me7osh7zmAw5nri1P176/AebAogHSqqx/MOTeL/wZyzcUojyuiavv69ERIGOMzsUMLxd7dfRdnDzSsod9VwdVd/GUa2gK/XN2Hu2yu5jjO0oXtlbgsXbTuDJyZmsokxEXRqDHQoY7syGtDfvxBg8/FzXhB9r1JCGhpiqJ6u1eovnao+OrG/TnnYU+4urUdfUtlkm5vgQUbBgsEMBw9VsSIREbLN1vC15JzGRrY065791zOE17a0s7E738vZwJ3nZKEoidnKl/dfKHB8iCibM2aGA4SynJi9DDo3OgNk56SgYp0Dkrx/gbc07cfZcHVFZWKnWorlFj6WTM/HJQ3l4f9512LPoeqydkdUhhfyiw0ORq0iwey5XkQDz8llRklCPXitzfIgo2HBmhwJGTKQEq24fhIs1atQ1tZi2Up8pV2LGqJ64699fQa3VI0eRgDUzskxLT/uLq3FZ2YwL1Y1uL7d4c+bF2axIRy0DNWp0mJWTDgG2ndxn5aRDLBKZnjc2Msyj18pO6UQUbBjsUMAor2vC8g9PoV8PGbJSY9HcokdO7wRMHZKCOZu+NeXUGD/c5+Smm7ZdX6huNC1Lubvc4k6jUE91RH0dt56nqcXUyX1OTjo0OoNF/tHzdw6xCGZiIuH2a2WndCIKNgx2KCAo1Vos++Ak7hqZho2HSi1qx+QqErD2D9fiD69/ZRHwLJ7YD5k9ZAgPE0MeLUGkRGya6XEnsPA0Aded6301KyILD4Naq7d4n8xdI4+yef3uVlFmp3QiCjYMdiggVDdo0a+HDBsPldrUjjlYUgMRzlnM5ADApV+aTLM5eXaWtpwFFp4m4Lp7fVtmRdqy68nVbq8eMeFtDqrYKZ2Igg0TlCkgqJpbkJUaaxPoGB0oqUZWaqzFMWloiNn5Gmw8VIo5uemmY862VHuSgOvJ9Z7OipTXNaFgSyFufGEfbnv1MG78xz5TMUBnnBUcbG/OkTfvTUTkD5zZIZ+zO5MREYbLymanjzNvfGmseGzOWEzPyNFyi6dLTZ5c78msSHvze7yRc+SLexMR+RqDHfIpR8tBf7ttEOIjnc+KGGdy8hRy5Of0MlU8NmcMiMwDC+vgStnkfOu09YyQJ0tTnuzy6oj8Hm92M2endCIKFgx2yOuMwYZeEPD0zlM4YLVUtb+4Go9vL8Lfbh+EXEUCDtpZyspTyBEfJcHbfxyFphY9FlpVPDaShoZYBBb2gqu3/zjK6XitZ4Q8XZpyd1aEu56IiHyDOTvkVeY5KZd+UdsEOkb7i6uhaTFg9e2DkWeVK5KjSEB+Ti+8+kUJ0uIjsfWbMruBTl6GHIru0abCfY6WiQ5fqHFYkM9eAm5bChDGRErQOzEaQ9Pi0Dsx2u4MSUftelKqtThf2YDCslqcr2ro0KJ/3rw3EZGvcGaHvMY62DDPubGnvrkFvdPi8MqvsyLKphZESsQQh4ggDhHh+TuHICZSghVTB0Kjs1wmysuQ4+mpAxEbGWYKLBwtE204WIo1M7IgEolsltPsJeB6qwBhR+x68mZbB7aMIKJgwWCHvMY62DDfPWWPcSbDWa6IeRsGg0HAL41aNOsMOFZWi1vXHMDwnnGmD2NHy0RqrR4PbCnEBwtyECISuZWA62hpCgDOVzZ4tG3cuKzXoGnByqkDseyDk20KorxZwNBXxRGJiHyBwQ55jXWwUXipDjmKBLvby53NZCjVWtQ0aiEAeOqDkxZLYTmKBMzOSceGg6U2BQWdLROptXqEiETonRjt9uuxDsLaMvNh/ZhIiRhPTs7EE5P6o0mr92jXkzcLGLJlBBEFEwY75DXWwYZx+Qiw7OfkbCbDGBwMSY1FYVmtTaBkr3WE8cPYm8Xx3J35MN8JFh8lwdLtJ3Gg5LfHqLV6LHm/CGMy5B7PlngzwdmTe7elKCIRkS8x2CGvsQ42jMtHc3LTsWCsAuFhYsREOJ7JMA8oZl3Xy2FrBOv6OsCv+T+J0V5r9unOzEejVm8REK3PH24R6Nh7jCdj8mZbB3fvzbweIuoMGOyQ19hL7FVr9ThxqQ4zR6ahh4sPQ/OAwlVys0ZnQKREjDm56chKjUWLQcD5qgbIoyQeFcdzd5bC1cyHsqkFqz4+gyGpsZidk47QEBHCw8ROH+PpTIw3Z67cuTfzeoios2CwQ17Vnkq85gGFq+TmyDAx1szIsmkiapxlcCc3x5NZClczH5FSsU1T0/X5w50+xtOZGG/tEnP33ucrG5jXQ0SdAoMd8rq2VuI1DyicJTfnKBKgFwS7TUQ96YDubJbiuTuHoKFZZ5rxiQ4PdTrzIQ0NsRlPWxO0nfFnywgWRSSizoLBDnU4d5eCXF1nvpTiKLk5T5GAJ6cMgFZncNhEdH9xNS4rm3GhutHheFzl4JyvbMAf3vjadOym/ol4ZtpALN1hf9u4qrnFZjxtSdB2h79aRngzZ4iIqCMx2KEO5e5SkDvXWS+lGJOb549VQBwiglqrx7GyWkxbdwjP3znE6bguVDdi/lvHHI7H1SxFXZPl+c/OVAKAacbHeubj8kXbruXmCdqP3dIPP9U2oVdCJK6KjeiUyz3ezBkiIupIDHaowzhbCnps2wk8OTkT4hARuklDcaikdYfVjJFpiJKEQmcwQCQS4cxlFRo1OiR2kyImUmKxlFLT2LqccuRCjamujpGrnB7z88bxPH/nECTJwgG4nqWwd//PzlRi8USd3XygmAj7H/RqrR6v7C1BVmos5r91DHsWXd8pAx3AuzlDREQdicEOdRhnS0EHiqtx6Rc1Fm4pxIZZI/Dh8Z9xoKQGkZLfEoutl3aMsy/GD80WvQG//9cRu/cvvFSHvAy53efPUSSg8FKdzXjOVzZAbxCQEhvhdJbC3uONHOWluHO/YJj98GbOEBFRR2EjUOowrpaCNDoD5uSmY+3eYlMV5Dm56U4Ti5VqramZ6K6iy8hx0MDz3GUVVt02yKZhp3mFZWt1TS2m5zDOUlg/Pi9D7vDxgOO8FEf3M47n3GVV0Mx+uNP0lIjInzizQx3GnaWgrNRYi63h1t+b219cjcp6Db67WItZ1/WCVm/ALQOSUfSTEk/vPm1axspRJGBu3jUIDRFZzDKEh4mxq+gyHthSaLdLujQ0xGKLtL1ZiujwUCzdXmT38a5mZszv56ipKREReR+DHeowzpZu8hStMxxavWVxQFfFApVNLdh1otxmB9aO+TkorWlEaIgIhZfqMGfTtxjeMw5rZ2SZcmiUai1OXKqzG6iYL02ZL0XZ231kr8u6u3kp3twpRURE7mGwQx3GUcJqjiIB+Tm98NbXF/HwTX0sHuMqsdjelvIDJTVYsesUstLiLGaFrAvZGcdjnTRtXEp6YEshANdbpJmXQkTUuTHYIY85q4+TEhuB5+4cgvOVDahraoE0NASFl+pMS0nj+ydZJBI7K7SXlyHH4Qv2a+ccKqnBY7f0w7VpcThWVmvanWWdMJwSG4HnnYzH3SRhztAQEXVeDHbII+7Ux2lo1lkU4DP3zO4z2LkwF8s/OImDJTWmQnsiAAfNl6oy5HjqdwMwZe1Bh2P5qbYJ8986hhxFAtbMyMIDWwrtztIkycKhNwjcIk1E1EUx2CG3udv40dmuLLVWj/qmFjw7fTAu1qhR19SCUJEIs3PSseAGBUSi34oFVqma7ebbGBmXwIyzQk9OznQ4S8OlKCKirovBDrnNVUsFY76MO20EroqLRLQ01LRTqblFj0PnbYsF5ioSLGZ8jKxr3xwqqcGyyZlOgxfzc6rmFkBke5yIiIIPgx1ym7uNH91tI2DMgzlf2YDbXztsc61piUskcppgbNTkZBYIcG8JzpiP1KBpQWykBFqdAQ0andMeX0REFNj8WlRw//79mDJlClJSUiASibBjxw6L84IgYNmyZejRowciIiIwfvx4FBcXW1zzyy+/YObMmZDJZIiNjcXcuXPR0NDgw1fRdbjb+NFRQT1HOTKOgihjL6llkzOxZ9H1eO/+bKzPH46stDi7tXPCw8QoLKvF+aoGKNVai3OuluDMixdOeeUgqhq0eHx7EW56cT9ue/UwbvzHPhRsKcRPv6idvgdERBR4/Dqz09jYiCFDhmDOnDm4/fbbbc7//e9/x5o1a7B582akp6fjySefxIQJE3D69GmEh7f2NJo5cyYuX76Mzz77DC0tLZg9ezbuvfdevP32275+OUHPk8aPnuTIOAui1Fo9QkQi9E6MhlKtxcIthXafP1eRgF1Fl01b0a1nbFwtwdWpW7D0g5M4UFyNgnEKu1WdDxRXY/H7J/Ds9MG4Ki7S4ZiJiCiw+DXYmThxIiZOnGj3nCAIeOmll7B06VJMnToVAPCf//wHSUlJ2LFjB+666y6cOXMGn3zyCb799lsMHz4cALB27VrceuuteP7555GSkuKz19IVeNr40d3t2p4se9l7/lxFAmZZLWs5S5qOlIgxJzcdWamx0OgMCA8TQ28QcPRiLQDnVZ0PltTgYo0a0dJQLmkREXUSAZuzU1paioqKCowfP950LCYmBqNGjcKRI0dw11134ciRI4iNjTUFOgAwfvx4hISE4Ouvv8Ztt91m994ajQYajcb0vUql8t4LCRLmtXWenJwJiTgEyiYtuoWHQaMz4Ke6JjSYdSv3hCdBlPWMkbOWEPaSps0bj5oHNHkZctP2dVdVneuaWiyKFxIRUWAL2GCnoqICAJCUlGRxPCkpyXSuoqICiYmJFudDQ0MRHx9vusaeVatWYcWKFR084uBlL7F3fP9ELJ7YH0s/OGnZyuHXAMW4fOQuT5a9zGeMCstqHc7CALZJ04NTYx0uURkEAXNy011WdZaGhjjsdt7RnBVwJCIi93TJrudLliyBUqk0fV26dMnfQwpYjhJ7+/WQYfmHJ+3ntfya8OuptnTP9jRp+rpr7FdrBlq3r2elxpqqOttj3PLuqsVERzAmTN/4wj5TkvTCLYUor2vy+nMTEQWTgA12kpOTAQBXrlyxOH7lyhXTueTkZFRWVlqc1+l0+OWXX0zX2COVSiGTySy+yD5Hib1ZqbEOgwbj8pEvGGds7LGXNB0e5vpHfsPBUszOSbcJeIxb3s9dVrnVYqI93Nk9RkRE7gnYZaz09HQkJydjz549GDp0KIDW3Jqvv/4a8+bNAwBkZ2ejrq4OR48exbBhwwAAe/fuhcFgwKhRo/w19KBiTOy1TupN7CZFwTiFTRFAI18t83iaNJ0QJcX6/OGmxGTzvloAkBYfiZ0FuWjUtOCZqQOhbNbhsrIJEnFrT613vinDyqkDvb6U5G4BRyIics2vwU5DQwNKSn7LtygtLcXx48cRHx+PtLQ0PPTQQ3jmmWeQkZFh2nqekpKCadOmAQD69++PW265BX/605/wz3/+Ey0tLSgoKMBdd93FnVhucpUTIgsPc5jUa96Tyjrg8cUyj5EIwMRBPZB/XS9odAZIQ0NQWa+xua68rglLd5zEgRLLAoXG1zC8Z5xNgrVSrUU3aSjqm1tw29CrIM9N90mQ4W4BRyIics2vwc53332HG264wfT9okWLAAD5+fnYtGkTHn30UTQ2NuLee+9FXV0dcnNz8cknn5hq7ADAW2+9hYKCAtx4440ICQnB9OnTsWbNGp+/ls7InYrC8mgJnpycaTep1/j9nNx0iyDI3U7iRu4m4dq7DgAetbPcYxyHceu5aVmoxPI6875aY/t0b/P2+Y7mbi4SERG5JhIEQfD3IPxNpVIhJiYGSqWyy+TvKNVaFGwpdBkkAEBpVQMuVDc6XPpZnz8cczd/B6B1N9bfpw9GDwe7sawDlvDQECz/8BQ+P/Nb7pV1wAU4DsyemNQfE1464PB17ll0PXonRuN8ZQNufGGfw+s+e3gMMpK6OTzva84KKFr//RARdVXufn4HbM4OeZe7OSHldU1Y9sFJHDCb1bFevpKFh+G9+7MRExHmtM6OvYDFWBDw8PkaU/BkXRDwiqoZP1Y3YsbINMzOSUfRz3UQBGDQVTGobWzBhlkjbAIwI+Nyj6tloUaNzvRnRzNIvtwC7mkuEhEROcZgp4tyJyfkt6Uf58tXsZFhCBGJoGpuQUhIaytx6w9jR7uLDpbUQIDtUtj+4mpU1DejvlmHJe//NoZIiRjr84fj1S9K8NLnv/VJc5Q/ZFzucXdZyDogi5SIsWHWCKzbW2KxBGZv9qmjeVJ7iIiIHGOw00VZf/jbtFCQiFFZr3E4+3OopAZzctKRlyHHdxdrseT9ItM5e4GAs5kk472sqTV6rNx52iJXaE5uOl75osSt/CHz3CF3WlLYC8jm5KZj7d5im+eznn3yFn/lDBERBRMGO12U+Ye/o91W6/OHO7lDqwU3KFDw9jEUjFOYAqWIMDHKflFDrdWhvlkHWUQYal3UhbHXoiFULLIJMpz1rTIPmqyXe9xZFjpf2WATkDl7Pm4BJyLqHBjsdFHmH/6OWii4cnVcBKpVGrz1x1F4Ztdpi6DAmIuzeNsJ3DUyDRMHOC7yCLS2YDCfXQKAEIhsavm46lvVLTwMexZdb3e5x9WykL2lPVfPxy3gRESBj8FOF2b88L+sbLY7e2Fsm2AvCMpRJODYxVoMuCoGK3edtrnGmIuzYdYIPPvJWdNj7N0rTyFH0c9Kt2r5uOpbFRcZht6J0Q7PmwdAquaW1iI9vx63l9fj6vm4BZyIKPAFbLsI8o2YSAmaWmwrIAO/tU3IU1i2Y8hTyDE7Jx1P7z6D+mad015TxvPOWjA8NXUApg1JwWYHtXw2HirFnNzW5SlXfaskYuc/0s76TdlrPeHs+TytJ0RERP7BmR1CfKTEYQuFB7YU4r37s/GoIOCn2ta2CfFREsx842uotXoom5wv4xjPG+81Jzcdc3LSodEZkBofgU9PXUEIAK1esNn1ZWSei7PhYCnW5w9HCEQ2lZBn56RD2aQFEGV/LC76Ta2dkWWT17PhYCk2zBqBEJHIpsYPt4ATEXUODHa6OFctFIalxeGyshlvfX0RmSkxeGVvCdbnDzfl0Lha5knsZtuLauGvS1KvzrwWZy+rEDY8FReq653eJ0oaildnXovU+AjsPVuJYb3iMCvnt/YQhZfq8MCWQuwsyHV4D3dqC/VOjLab1/MKt4ATEXVaDHa6MFctFJZO6o9EWTje+voi/jCqJ0JFIuQpElB4qc50beGlOuQp5Db3AFqrKesMBlNwA1gGUrERYVg6qT+WvF+EWTm9nI61UaPD/LeO4W+3DcR3pbV2n8/VspK7/aYcbfdmcENE1DkxZ6cLc1X7pk9SNzywpRB7z1Zh46FSxEdL8OSUAdhwsNR03elyJf48oa/dXJz863rh3/svYM2MLERKxKb7bjxUiicnZaKpRY8L1WocKKl2mhuTlyFHfFTrUltVgwazc3vZXOvOshL7TRERdU2c2emCjO0Qahqd176prNeYZmQOldRAHCJCaXWj6ViuIgF/GNUTf9z8LZ6dPhjzxyqgbGqxWFZSa/XQ6AwWxf4OldRg8cR+mL3xW7x+z3Cszx8Ord6AWwYk48RPdXhm9xnTc+RlyLHqtkFYsfMUPvu1f5Zxi7oxjyctPtJpmwojdwoLEhFR8GGw08WYt0PYMGuE02ut83FqGrW4OjYC792fjUiJGNHSUFyoasTKqQORKAvHlLUH7d7HXoXky8pmrJ4+GC/895xFYnKeIgE75uegtKYRoSEiKLpH4+r4SKyYOhAaXWvisFqrxyt7S0yzOY6ajlpjvykioq6JwU4XYr4bKVIihjxaglxFAg46qKNjnpsDAKEhInx8qgKFZbWYk5OO7t2kULfoER4mRq2LWSLr4nzJsnA8+8lZm63mB0pqsGLXKWSlxeHEpTqsnZEFoOP6RLHfFBFR18Ngpwsxz9GZk5uOlz7/AbNy0iEAFkFHriIBj0zohxmvf2VxLFoaatqSPn+sAr975ZDp/Ft/HOX0uc1niXIUCdDqDE7r8ywYq8DMkWkWQUhH9Ylivykioq6FwU4XYr4bydjz6asLv1jUvjHm29Q2ai3yZp6cnIm5m741HbOur3PkQo1bs0R5Cjn+MqEvLiubvfIajflIquYWyCLCII9iYENE1NUx2OlCZOFhpuTe7t2keHXmtTa1b4zeuXc0Xp15rSn4Wf3xGSz/3QCHbRs2HCzFmhlZEFkV+8vLkGP5lEycr2rE+vzhKLxUhzV7fsBDN/VxOtamFj0e87CruHk+kpG9DuxERNS1MNjpQuTREmyYNQJr9xY77T8FAA2/1rUxZ9xVVVhWa5PPY6yQvPXe0ZhV31rsL7GbFD9cqcfvXjlkEUgBwPj+ycjLkNvd+m6cCbLXVdzRzI071ZE5w0NE1DUx2Oli1u0tttt/CoBpe7i95GTjdQvGKnBtWiwK3i60OW+c8dl4qBQHS2qwPn84Ht9+0u44nt59Gjvm5+DpXacsdmMZ2z48sKX1/uZdxZ3N3DS36F1WR2awQ0TUNTHYCSKu8lWqG7Qu+0/lZciRf10vU7BhT7VZ/R1zN/VPRGSYGA+N74OCGwSEhIgc3kOt1aO0phGP3NIPs+o1Nm0fjPc3FvpzNXOzdHKm4zcGlkETERF1LQx2goQ7+SrW7RKM+TtZqbHQ6AzoEROOv04biNtfO2w3mAGAZp0Bg1NjbZagxvXrjscm9sfj24tMAdX6/OFOxxwaIsKpchU+LrrsstCfq75WBoPg9LlYHZmIqOtisBME3M1XMbZsAFoDnTUzsrDxUKlF/k5ehhxrZ2Rh7ubvbAKeXEUCBEGAWqPHrYN6YG5OOmIiwxAqFqFJo4dWp8eD4/vgD6M1kIaKIQgCchQJdreY5ygScEXVjJzeclzfp7vLQn+u+lqptXpWRyYiIrsY7AQBd7p5A8Cxi7Wm4GNObjo2Hiq1LepXXA0IrU1AzfNtchQJmJWTDrFIhEiJGE/vOo01M7Lw/H/PobCsDmtmZOFlq3ygcf26Y9nkAVi565TF8TxFAp6cMgAfn7wMZZMWg1PjXBb6c9XXKiYijNWRiYjILgY7QcCdbt4GQcDTu89gzYwsSENDMLZPd4sZHXMHSqrx4PgMrM8fbpFLs3jbCWyYNQIGAfjn/xuGULEIWWlxGJkebzdw2nu2CgAwJycdiyf2Q6NGD7VWj2NltZi2rnWH1uRBKQBcF/pzp69VTKSE1ZGJiMgGg51OyjwZOcJsecqeCIkYZb+oodbqsXjbCbyRPwI1DRqnj9EZBGh0BlMdnq3flGH19MF47pOzNrunlk8egH/tu2D3PnvPVmHmqJ7QtBgwx6woIdAapISGiFBYVuuyAKC7fa1YHZmIiKwx2OmErJORC8YpbKoXG5OPr7smAcomHVJiI1AwToHQEBGe+/SsTWNOa41mdXZyFAnYMGsE/v7JWZsKyYdKavD0rlMWXc3tKa5ssAh08jLkmH+DAhPXHDAdd1UAkH2tiIioLRjsdDL2kpGN1YsB4GBJjcPk41xFAh6b2A8vfV6MrLQ4p8nD5nV2DpXUoL5ZZ7cVBNDavHOWk+Dp6rgI9EyIxLv3ZyNaIkaERIxvf6y1melxpwAgZ26IiMhTDHY6GetkZOMMTohIhPuv742VUwdCqzPg57omzM29BllpcabmnQdLajBb1bp8ZR4gWScP55sV9TOy7oXlivnMUk2DFs06A46V1WLDwVIM6xmH/Ot62X0cCwASEVFHY7DTyZgnI5vP4BiDlyc/OGkRvNhrBQH81t7Buglo925S3PXvr2y2nVv3wrKWKJOaZooczSyZj8UgCA6XvlgAkIiIOhKDnU7GfAu2+fbxgnEKuzuirFtBFF6qQ55CjgMl1VBr9ZY1dhRyDEmLtVtQ0Pxx1vIUchgMwGO39IP216Dp75+cdTkWR3lDLABIREQdyfl/1yngGLdgR0rEGNunuymAyEqNtZt/A7QGGVmpsQBal6+enJyJHEWCxTW5igSsnDYAZ8qVdu9xplxp93F5Cjn+MqEv5m7+Fr975RDu+OcRVNZrnLalMI5FozPYnGcBQCIi6mic2elkYiIleHb6YFz8RW2RR2MvcDBnPJ+VFouPT11GVlqcxfJV4aU6NLfoMWNUTzTrDCgsqzO1kgCAJFk4QkNEWDopE6qmFkRKxBAAfHGuEi/v+QGrpw82LZW5O5aYCMsZHBYAJCIib2Cw0wlFSsRYt7cEs3J6mY65yqmRhoZYJB/bW6rK6S3HA1sKcd/112D55AF4etcpm91cs8web+xQ/q99F6DRGUzLU+6MJUeRgOYWPT55MA/NLXpuIyciIq/hMlYn1Nq9vBpFPyuRp5ADaM2psV5iMspTyNG9mxSP3NLPYaADAN3CQ6HW6tGiF7Bi1ymbpaiDJTXYeKgUc3Jbc20OmX1vvjx1RdWMPAdjMfbEmp2TjoVbCtHcosfQtDj0ToxmoENERF7BYKcTMu7IEomA+Tf0bi36d7AUs3PS7ebi/GVCX9z176/w39NXkJUWa/eeuYoEREnEuLFfd7fzf6y/1+gMyFEkoEdMBGbn2o4lTyHH8ikDUNWgMQVdTEYmIiJv4zJWJ2TckTUwJQYLtxTi2emDkThRisZmPZZOykSICLj4ixpXxURALwim4n2OausYm3z+dfcZPDi+D6rqnbeSsM7JMX6fLo/C5MEpWPB2a+Vl823tMRFhaG7Rm3piAcBN/RMRHR6K85UNUDW3uGwZQURE1BYMdjoh444sAcAb+SPwj09t+1XNzklHs06PNw5cMHU9N6+tM3+sAnpBQIvOgMJLdaaZlj+M7mmTOGzNOidHGhqCXEUCPj1VgYFXxSArrXVmyJjvk5chx4IbFFhotoR2U/9EPDk5E39593uLIomuWkYQERF5isFOJ6TW6lEwToHEblI8seOkw3o2T9zaH/PGKqBq1tnsroqJCMPnZ67g3/svWOTwNLe0diV3t5VEjiIBlapmU+Iy8NuMDgCkxUcisZsUALCzINfU0yo6PNQm0AHcaxlBRETkCQY7nYxSrcWj205gWM84jOwV7zS3Rq3VQxomRsENCiTLwrHsg5MOKxobA57YiDCHy13mu7GM3z8zbRB2F5Xj6d1nTPcwf449i6636EhudL6ywSbQMWLLCCIi6kgMdgLYFVUzahu1UDXrIIsIRVykBI0aHY5erMWDN2ag0kVuzS9qLbpHSxElDcWyD07a7K46VFKDEIiwdkYWFm4pxOhr4tFdJsXr9wxHg0aHxbf0R0gIcLFGjfAwMQRBgFgkwvN3DjHV5qlQNqFvsszhGBy1flC5aAnBlhFERNRRGOwEqLKaRizZXmQzs7Jy6kC88ocsKJtanNaziZSI0Ss+Ck/vOoVZOekOKxofKKnG/WN7Y9OsEYiNkuCpD09ZPOf6/OGY/9Yxh8+T2UOGt76+6LDPlaPdVjIXu7C4S4uIiDoKg50AdEXVjCXbi1BYVoeCcQoMT4tDXJQEYeIQVDdokdgtHCEiET46edlhbs2TkzLx9K+1cmaM6un0+VTNLahrbMHuvcUOl8UckYaG4FBJjd0+V85aPxiTrPfbWcpiywgiIupIDHYCUG2jFucq6rH13tFo0OgQIhJBrdXh0PkabDhYCrVWj7/dNhBny1WYnfNbgT+jXEUChqbFYsn2IgCuqyv3SoiCLlbAPz47h4JxCmSlxkKjM5iWrtxNVjbnqvVDTKQEq6cPxuJtJywCHraMICKijiYSBEHw9yD8TaVSISYmBkqlEjKZ4/wTb1Oqtahu0OKXRg3io6R46kPLPJs8RQKenDwApTWNCA8Vo3s3CV747zn0S4kxBSjxkWG4OjYCDVo9fq5rgkZnQJIsHOcqVHjGLInYKEeRgKy0OHxfVoelkzPx7CdnsPdslen8uH7d8dgt/fGMVUVl4/Z2Y3Lze/dnI1IihlZn8Kj1g/E1G3dpsWUEERG5y93PbwY7CIxgp7yuCY9tO4EDxdUoGKdAYVmtw9mUrLQ4vLK3BOP6dceTkzLRqNWj7Bc1IsPE6JkQiSuqZqz7osQqUJJj/g29MXfzd6aAJ08hx+zcXih4uzVgaZ0RirPJvRnXrzuW3NofZTVqaHQGpMZH4NNTV0yzTMYx3Tb0KvROjPbuG0VERPQrdz+/uYwVAJRqrSnQAYCs1Fi7yb4ALPJj9p6tQovuJBbd3Bfz3zqGVbcNwmVlE3YVXbYJlA6UVAMQsPXe0aiq1yAmIgw/XKlH0c9KU/BzsKTGtCxmbu/ZKswfq8Dczd8BAF6dea1pfMYZnne+KYM81/axRERE/sZgJwBUN2gtas5Yt2OwZn7+QEkNHp0o+nV2JRY/1zU5TDI+UFKD+5t1EADcs+EbqLV6rM8f7vDe5qoaftvm3jM+Eq/OvNa0/fydb8qwcupALj8REVFAYrATAKxrzkRJQm0ShY+V1WLDwVIAQGpcJNbnDzedCwsJwcj0eNSpW1wGSnpBsCgiaH29o2Rmibj1eK4iAXpBgDQ0BBqdAdddk4C7R/dEkiy8Ta+diIjI2xjsBADzmjPyaAlS4yPw7wO1NtWO1/3hWkRKxPj7J2cs83Ey5Fg6qT8EAKFikdPnatEZLJKUzYObXAe7q4y7rnIVCVg2ZQD+8PpXpn5bQGuV5CT/5XUTERE55XxPMvlEdHgo8jLkAIBnpw/Gsg/s97uqUDbhlb3FNgUCDxRXY+Wu09h14jKKrzSY7mXNXl8r4/c5vwYyp8uVFo/Jy5Bj5e8G4taByZidk24T6ACsdkxERIGNMzsBQNOix5OTMrFi1ykkyqQ46CDnJlEW7rAS8qGSGjw8vg/uf/Mo1s7IAgTB4lrrvlZ5igSsmDoQxZUNWJ8/HIWX6vCH17/CXSPT8PBNfVCpak1i7h4thV4QcOuagw7Hz2rHREQUyBjsBAC9QYDOYMDSWzOham5BpERs6lBunrOj1TvPx1E2tWD19MFYuKUQG2aNwLxmHQCgWWew6WtVqWpGWY0aoSEiaHQGXJsWBwA4Xa5ESkw4EmXh+Ne+83j+ziEAwGrHRETUaTHY8bOLNY0WTTp3P5CDNTOysPFQqU3OzoQBSS7vt/FQKe4amYafapsw/61j+OShPDy967TFslhehhzPTBuIFTtPWRQQzFUk4OlpA/Hxycv44mylxQ4rVjsmIqLOisGOH11RNWPp9iKL5Sa9oTVgsZezc/JnFfIUCXaXsoz5N9Z9quqbdFjxuwFQNelQ16SFLDwMOoOAp3eetgh0gNY6O8s+OIVnpg7EzJE9LYKYlNgIrJ2RxWrHRETU6TDY8aM6tRYHSmoslq1EIjisk/P0rtPYMT/H1ODTyLx1g5Ex8ThSKsb4F/abjhvr6nx+ttLucxworobOINgNYmIiGdwQEVHnw2DHT5Tq1hmSh2/KwMQBPfD0rlN4ZW8JXp15rcPHqLV6lNY04pFb+mFWvQYancFU2M+8dk6iTIoNB0uRo0hAaIjlVnRXdXgA7q4iIqLgwmDHx4yNL1XNWiRES9FDFo7vLv6CWTnpmDGqJ1LjI50+PjREhP+evuKwd1aeIgF7zlQiKy0WC8dlYN8PljM4rjqgA9xdRUREwYV1dnyovK4Jf373e2w//jNk4RL8dddpDE2Nw+6iy5i7+TvMf+sYPj1VgVxFgt3HG/NyNhwsxeycdORYXZenkGPp5EwMvToWkwb1QAiAphaDzeMr6zUY46AWD3dXERFRsOHMjo8o1Vr8dfdpPHBjBuqbddDqDeiXEoOnd52ymKHZ+k0Z1uePgEh0zqJfVp4iAfNvyMDczd9CrdXjgS2FmJObbkpGvio2AqFiEc5XNSI0RIRyZTOe2X2mteYOLBt2/r+Rabi+T3furiIioi5BJAiC4O9B+Ju7LeLb40JVAxo0OlyoakC/HjJAAFoMBlz6pclUR+eDwp/x6sxr8fLnP6BfSoypzk5sRBiuiouAzmDAUx9YJifnKRKQn5MOvUGA3iBg/lvHLJ73nXtHI1IqhiAAkRIxukdLTcGMcUmNu6uIiKgzcvfzmzM7PmKAAIk4BLu+L0d0eBg2HyrFgZIayKMleHb6YEwckIzr+3SHQQAeGN8Hl5XNEIlEOH1ZhQ0HS3FtWiweu6U/pgxJweKJ/VGv0UGt1UMeLcFd//4Ka2dk2SQjA0CDRod1X5Tg2emD0SM2wuIcd1cREVFXwGDHB5RqLcQQYfWnZzBjVE9s+jXQ6ZMYjX/dMwxLd1j2wjIuORW8fQxZabFYMyMLD2wphKq5BWW1Tdj5fTmG9YrHtz/+gqy0OGSlxaJSpcHPyiaL583LkEPRPRprZ2QxqCEioi6LwY6Xldc14bH3TmBubjruGtUT4WFi04zOG/nDsWR7kd0CggAwJzfdVEV5Tm46BEHAhAFJyOwhQ8/4SIzrl4jXvixBwQ0ZkHeT4OlXTpvuYcy/sZ7NISIi6moY7HiRUq3FY++dwJkKFZJjwvHM7tP4w6ieAFq7m9c36xwWEDSvhGz8szxaitKaRoSHifHxqQqcvazC47f2xyenLuOGvkl4+0+joTcYEBshYf4NERHRrxjseFGFqhlnKlTYcu9ohIpEmJ2Tju7dpABaC/81aHROH29dAFCrN2Dem60JyMalrmd2n8ZN/ZMRERaClJhwBjhERERWGOx4yRVVM5RqLf7vvmwIgKnZZ8E4Bcb1645QUQhkEc7LHJkXAEyUSVFe12z63jgjlJUWh4FXy2AQwECHiIjIDhYV9JJ6jRaJsgh8faHGoqv51m/KsHhif/zto9Mor2tCnosCgsBvVZGtd1sdKqlBVmosfqptgqqJLR6IiIjsYbDjJZEhYjy5owiJsnAc+jUheX3+cGy9dzSe3tlaK+exbSewYupAm0rIxiWqDQdLkaeQIz8nHSd+qjMFP+Y0OgMk4hC2eCAiInKAy1heoFRrUd+ix4GSGvy/7F54ZEIfjOuXhPK6JjS1GEyzPNUNWpRUNSArLQ5zctKh1RvQPVoKSWgILiubsXZGFuKjJFi7txh/GNXToqu5UUxEGMp+UWN4zzhfv0wiIqJOIWiCnXXr1uG5555DRUUFhgwZgrVr12LkyJF+GYu6qQX1zTpESsRIT4jCm0d+xHOf/gAANl3NxSKRaXu5PbsfyMX4/kkWXc2NchUJiI+SoLc8ivk6REREDgTFMtY777yDRYsWYfny5Th27BiGDBmCCRMmoLKy0vWDO1hVrRoGAFqdAXNy07Fyl2V7B+uu44WX6myWsYzyFHIIBmDUNQkYZjVzk5chx19vG4SUmHAks5YOERGRQ0HRG2vUqFEYMWIEXnnlFQCAwWBAamoqFi5ciMWLF7t8fEf2xrpU0wh1ix47T1zGddck4A9vfG1xvmCcAoVltabdVJESMdbMyMLGQ6U2VZQXjstAD1k4YiNb83HYx4qIiOg3XaY3llarxdGjR7FkyRLTsZCQEIwfPx5Hjhyx+xiNRgONRmP6XqVSddh4GrR6NDTrsOFgKa7v093m/IaDpVjzayfyQyU1pg7mSyf1x9JJmWjQ6BAeFoJoSSjioywDGgY3REREnuv0wU51dTX0ej2SkpIsjiclJeHs2bN2H7Nq1SqsWLHCK+NRNbUgWhoKtVYPO305TcHNnNx0PH5rfzQ06xAlDYVBEHDypzqMviYBsogwBjZEREQdJChydjy1ZMkSKJVK09elS5c67N6yiDBESMTIUSTgQHE18hRym2vUWj0Ky2rx/aU61Gt0+P2/juDvn5zFyGsSkJrAZGMiIqKO1OlnduRyOcRiMa5cuWJx/MqVK0hOTrb7GKlUCqlU6pXxdAsPBQwCCm5Q4PUDFzA7txcAwSJJOS9DjpVTBwACUNOowfvzrkNclARJsnCvjImIiKgr6/TBjkQiwbBhw7Bnzx5MmzYNQGuC8p49e1BQUODz8VwdF4nLNY1IiYnATf2TEAIRFt3cF49OFKG+SYeEaAm6hYkhMggwhIZgeC/7O7GIiIioY3T6YAcAFi1ahPz8fAwfPhwjR47ESy+9hMbGRsyePdsv4+mREIXKWjVyFXI0aPWob25BeFgYUuMiIAGgMQiIieJuKiIiIl8IimDnf/7nf1BVVYVly5ahoqICQ4cOxSeffGKTtOxLiXGRfntuIiIi+k1Q1Nlpr46ss0NERES+4e7nd5fcjUVERERdB4MdIiIiCmoMdoiIiCioMdghIiKioMZgh4iIiIIagx0iIiIKagx2iIiIKKgx2CEiIqKgxmCHiIiIglpQtItoL2MRaZVK5eeREBERkbuMn9uumkEw2AFQX18PAEhNTfXzSIiIiMhT9fX1iImJcXievbEAGAwGlJeXo1u3bhCJRB12X5VKhdTUVFy6dIk9t5zg++Qevk/u4fvkHr5P7uH75Jo/3yNBEFBfX4+UlBSEhDjOzOHMDoCQkBBcffXVXru/TCbjPxI38H1yD98n9/B9cg/fJ/fwfXLNX++RsxkdIyYoExERUVBjsENERERBjcGOF0mlUixfvhxSqdTfQwlofJ/cw/fJPXyf3MP3yT18n1zrDO8RE5SJiIgoqHFmh4iIiIIagx0iIiIKagx2iIiIKKgx2CEiIqKgxmCnndatW4devXohPDwco0aNwjfffOP0+nfffRf9+vVDeHg4Bg0ahI8++shHI/UvT96nTZs2QSQSWXyFh4f7cLS+t3//fkyZMgUpKSkQiUTYsWOHy8d8+eWXuPbaayGVSqFQKLBp0yavj9PfPH2fvvzyS5ufJZFIhIqKCt8M2E9WrVqFESNGoFu3bkhMTMS0adNw7tw5l4/rar+f2vI+dcXfT6+99hoGDx5sKhqYnZ2Njz/+2OljAu1nicFOO7zzzjtYtGgRli9fjmPHjmHIkCGYMGECKisr7V5/+PBhzJgxA3PnzkVhYSGmTZuGadOm4eTJkz4euW95+j4BrZU4L1++bPq6ePGiD0fse42NjRgyZAjWrVvn1vWlpaWYNGkSbrjhBhw/fhwPPfQQ/vjHP+LTTz/18kj9y9P3yejcuXMWP0+JiYleGmFg2LdvHxYsWICvvvoKn332GVpaWnDzzTejsbHR4WO64u+ntrxPQNf7/XT11Vdj9erVOHr0KL777juMGzcOU6dOxalTp+xeH5A/SwK12ciRI4UFCxaYvtfr9UJKSoqwatUqu9f//ve/FyZNmmRxbNSoUcJ9993n1XH6m6fv08aNG4WYmBgfjS7wABC2b9/u9JpHH31UGDBggMWx//mf/xEmTJjgxZEFFnfepy+++EIAINTW1vpkTIGqsrJSACDs27fP4TVd9feTOXfep67++8koLi5OeOONN+yeC8SfJc7stJFWq8XRo0cxfvx407GQkBCMHz8eR44csfuYI0eOWFwPABMmTHB4fTBoy/sEAA0NDejZsydSU1Od/g+iq+qKP0vtMXToUPTo0QM33XQTDh065O/h+JxSqQQAxMfHO7yGP1PuvU9A1/79pNfrsXXrVjQ2NiI7O9vuNYH4s8Rgp42qq6uh1+uRlJRkcTwpKclhPkBFRYVH1weDtrxPffv2xYYNG/DBBx/gzTffhMFgwHXXXYeffvrJF0PuFBz9LKlUKjQ1NflpVIGnR48e+Oc//4lt27Zh27ZtSE1NxdixY3Hs2DF/D81nDAYDHnroIeTk5GDgwIEOr+uKv5/Mufs+ddXfT0VFRYiOjoZUKsX999+P7du3IzMz0+61gfizxK7nFHCys7Mt/sdw3XXXoX///vjXv/6Fp59+2o8jo86mb9++6Nu3r+n76667DufPn8eLL76I//3f//XjyHxnwYIFOHnyJA4ePOjvoQQ0d9+nrvr7qW/fvjh+/DiUSiXee+895OfnY9++fQ4DnkDDmZ02ksvlEIvFuHLlisXxK1euIDk52e5jkpOTPbo+GLTlfbIWFhaGrKwslJSUeGOInZKjnyWZTIaIiAg/japzGDlyZJf5WSooKMCuXbvwxRdf4Oqrr3Z6bVf8/WTkyftkrav8fpJIJFAoFBg2bBhWrVqFIUOG4OWXX7Z7bSD+LDHYaSOJRIJhw4Zhz549pmMGgwF79uxxuI6ZnZ1tcT0AfPbZZw6vDwZteZ+s6fV6FBUVoUePHt4aZqfTFX+WOsrx48eD/mdJEAQUFBRg+/bt2Lt3L9LT010+piv+TLXlfbLWVX8/GQwGaDQau+cC8mfJb6nRQWDr1q2CVCoVNm3aJJw+fVq49957hdjYWKGiokIQBEG4++67hcWLF5uuP3TokBAaGio8//zzwpkzZ4Tly5cLYWFhQlFRkb9egk94+j6tWLFC+PTTT4Xz588LR48eFe666y4hPDxcOHXqlL9egtfV19cLhYWFQmFhoQBAeOGFF4TCwkLh4sWLgiAIwuLFi4W7777bdP2FCxeEyMhI4ZFHHhHOnDkjrFu3ThCLxcInn3zir5fgE56+Ty+++KKwY8cOobi4WCgqKhIefPBBISQkRPj888/99RJ8Yt68eUJMTIzw5ZdfCpcvXzZ9qdVq0zX8/dS296kr/n5avHixsG/fPqG0tFQ4ceKEsHjxYkEkEgn//e9/BUHoHD9LDHbaae3atUJaWpogkUiEkSNHCl999ZXp3PXXXy/k5+dbXP9///d/Qp8+fQSJRCIMGDBA2L17t49H7B+evE8PPfSQ6dqkpCTh1ltvFY4dO+aHUfuOcYu09ZfxfcnPzxeuv/56m8cMHTpUkEgkwjXXXCNs3LjR5+P2NU/fp2effVbo3bu3EB4eLsTHxwtjx44V9u7d65/B+5C99wiAxc8Ifz+17X3qir+f5syZI/Ts2VOQSCRC9+7dhRtvvNEU6AhC5/hZEgmCIPhuHomIiIjIt5izQ0REREGNwQ4REREFNQY7REREFNQY7BAREVFQY7BDREREQY3BDhEREQU1BjtEREQU1BjsEBERkVfs378fU6ZMQUpKCkQiEXbs2OHxPQRBwPPPP48+ffpAKpXiqquuwl//+leP7sFgh4jIjh9//BEikQjHjx/391CIOq3GxkYMGTIE69ata/M9HnzwQbzxxht4/vnncfbsWXz44YcYOXKkR/dgBWUiardZs2ahrq6uTf9rC1R6vR5VVVWQy+UIDQ3193CIOj2RSITt27dj2rRppmMajQZPPPEEtmzZgrq6OgwcOBDPPvssxo4dCwA4c+YMBg8ejJMnT6Jv375tfm7O7BBRp6TX62EwGLx2f7FYjOTkZAY6RF5UUFCAI0eOYOvWrThx4gTuvPNO3HLLLSguLgYA7Ny5E9dccw127dqF9PR09OrVC3/84x/xyy+/ePQ8DHaIqEONHTsWCxcuxEMPPYS4uDgkJSXh9ddfR2NjI2bPno1u3bpBoVDg448/Nj3myy+/hEgkwu7duzF48GCEh4dj9OjROHnypOmaTZs2ITY2Fh9++CEyMzMhlUpRVlYGjUaDv/zlL7jqqqsQFRWFUaNG4csvvzQ97uLFi5gyZQri4uIQFRWFAQMG4KOPPgIA1NbWYubMmejevTsiIiKQkZGBjRs3ArC/jLVv3z6MHDkSUqkUPXr0wOLFi6HT6Sxe+wMPPIBHH30U8fHxSE5OxlNPPWU6LwgCnnrqKaSlpUEqlSIlJQUPPPBAB/8NEHUOZWVl2LhxI959913k5eWhd+/e+Mtf/oLc3FzTv8MLFy7g4sWLePfdd/Gf//wHmzZtwtGjR3HHHXd49Fz8LwsRdbjNmzfj0UcfxTfffIN33nkH8+bNw/bt23Hbbbfh8ccfx4svvoi7774bZWVliIyMND3ukUcewcsvv4zk5GQ8/vjjmDJlCn744QeEhYUBANRqNZ599lm88cYbSEhIQGJiIgoKCnD69Gls3boVKSkp2L59O2655RYUFRUhIyMDCxYsgFarxf79+xEVFYXTp08jOjoaAPDkk0/i9OnT+PjjjyGXy1FSUoKmpia7r+nnn3/GrbfeilmzZuE///kPzp49iz/96U8IDw+3CGg2b96MRYsW4euvv8aRI0cwa9Ys5OTk4KabbsK2bdvw4osvYuvWrRgwYAAqKirw/fffe+8vgiiAFRUVQa/Xo0+fPhbHNRoNEhISAAAGgwEajQb/+c9/TNetX78ew4YNw7lz59xf2vJny3UiCg75+fnC1KlTBUEQhOuvv17Izc01ndPpdEJUVJRw9913m45dvnxZACAcOXJEEARB+OKLLwQAwtatW03X1NTUCBEREcI777wjCIIgbNy4UQAgHD9+3HTNxYsXBbFYLPz8888W47nxxhuFJUuWCIIgCIMGDRKeeuopu+OeMmWKMHv2bLvnSktLBQBCYWGhIAiC8Pjjjwt9+/YVDAaD6Zp169YJ0dHRgl6vt/vaBUEQRowYITz22GOCIAjCP/7xD6FPnz6CVqu1+5xEwQyAsH37dtP3W7duFcRisXD27FmhuLjY4uvy5cuCIAjCsmXLhNDQUIv7qNVqAYDw3//+1+3n5jIWEXW4wYMHm/4sFouRkJCAQYMGmY4lJSUBACorKy0el52dbfpzfHw8+vbtizNnzpiOSSQSi3ub/88wOjra9LVv3z6cP38eAPDAAw/gmWeeQU5ODpYvX44TJ06YHj9v3jxs3boVQ4cOxaOPPorDhw87fE1nzpxBdnY2RCKR6VhOTg4aGhrw008/2X3tANCjRw/T67zzzjvR1NSEa665Bn/605+wfft2i2Uwoq4kKysLer0elZWVUCgUFl/JyckAWv+N6XQ6079nAPjhhx8AAD179nT7uRjsEFGHMy47GYlEIotjxoDB0wTjiIgIi2CjoaEBYrEYR48exfHjx01fZ86cwcsvvwwA+OMf/4gLFy7g7rvvRlFREYYPH461a9cCACZOnIiLFy/i4YcfRnl5OW688Ub85S9/adNrNrL32o2vMzU1FefOncOrr76KiIgIzJ8/H2PGjEFLS0u7npMoUDU0NJj+XQJAaWkpjh8/jrKyMvTp0wczZ87EPffcg/fffx+lpaX45ptvsGrVKuzevRsAMH78eFx77bWYM2cOCgsLcfToUdx333246aabbJa/nGGwQ0QB46uvvjL9uba2Fj/88AP69+/v8Hp3/mcItAYZ999/P95//338+c9/xuuvv2461717d+Tn5+PNN9/ESy+9hH//+992n6t///44cuQIBLNqHYcOHUK3bt1w9dVXu/0aIyIiMGXKFKxZswZffvkljhw5gqKiIrcfT9SZfPfdd8jKykJWVhYAYNGiRcjKysKyZcsAABs3bsQ999yDP//5z+jbty+mTZuGb7/9FmlpaQCAkJAQ7Ny5E3K5HGPGjMGkSZPQv39/bN261aNxMEGZiALGypUrkZCQgKSkJDzxxBOQy+UWNTmsmf/P8B//+AeysrJQVVWFPXv2YPDgwZg0aRIeeughTJw4EX369EFtbS2++OILUwC1bNkyDBs2DAMGDIBGo8GuXbscBlfz58/HSy+9hIULF6KgoADnzp3D8uXLsWjRIoSEuPf/xk2bNkGv12PUqFGIjIzEm2++iYiICI+m44k6k7Fjx1r8B8FaWFgYVqxYgRUrVji8JiUlBdu2bWvXOBjsEFHAWL16NR588EEUFxdj6NCh2LlzJyQSidPHbNy4Ec888wz+/Oc/4+eff4ZcLsfo0aMxefJkAK31eBYsWICffvoJMpkMt9xyC1588UUArTlAS5YswY8//oiIiAjk5eU5/B/jVVddhY8++giPPPIIhgwZgvj4eMydOxdLly51+/XFxsZi9erVWLRoEfR6PQYNGoSdO3eadp4QkXewgjIR+d2XX36JG264AbW1tYiNjfX3cIgoyDBnh4iIiIIagx0iIiIKalzGIiIioqDGmR0iIiIKagx2iIiIKKgx2CEiIqKgxmCHiIiIghqDHSIiIgpqDHaIiIgoqDHYISIioqDGYIeIiIiC2v8HaBzgz8sErlsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, x='Impressions',y='Clicks')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AGE NORMALIZED CLICK VERSUS APPROVED CONVERSIONS \n",
    "AGE RANGE THAT BUYS THE PRODUCT AFTER CLICKING THE ADD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rmahla_admin/.pyenv/versions/3.9.2/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "/Users/rmahla_admin/.pyenv/versions/3.9.2/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "/Users/rmahla_admin/.pyenv/versions/3.9.2/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "/Users/rmahla_admin/.pyenv/versions/3.9.2/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1211.11x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col=\"age\")\n",
    "g.map(plt.scatter, \"Clicks\", \"Approved_Conversion\", alpha=.4)\n",
    "g.add_legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "GENDER NORMALIZED CLICK VERSUS APPROVED CONVERSIONS\n",
    "GNDER THAT BUYS THE PRODUCT AFTER CLICKING THE ADD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rmahla_admin/.pyenv/versions/3.9.2/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "/Users/rmahla_admin/.pyenv/versions/3.9.2/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 611.111x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "g = sns.FacetGrid(df, col=\"gender\")\n",
    "g.map(plt.scatter, \"Clicks\", \"Approved_Conversion\", alpha=.4)\n",
    "g.add_legend();\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CAMPAIGN C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "a=[]\n",
    "b=[]\n",
    "c=[]\n",
    "for i,j,k in zip(df.campaign, df.fb_campaign_id, df.Approved_Conversion):\n",
    "    if i==\"Campaign C\":\n",
    "      a.append(i),b.append(j),c.append(k)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Campaign C</th>\n",
       "      <th>fb_campaign_id</th>\n",
       "      <th>Approved_Conversion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Campaign C</td>\n",
       "      <td>144531</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Campaign C</td>\n",
       "      <td>144531</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Campaign C</td>\n",
       "      <td>144531</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Campaign C</td>\n",
       "      <td>144531</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Campaign C</td>\n",
       "      <td>144531</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>Campaign C</td>\n",
       "      <td>179977</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>Campaign C</td>\n",
       "      <td>179978</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>Campaign C</td>\n",
       "      <td>179979</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>Campaign C</td>\n",
       "      <td>179981</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>Campaign C</td>\n",
       "      <td>179982</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>625 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Campaign C  fb_campaign_id  Approved_Conversion\n",
       "0    Campaign C          144531                   14\n",
       "1    Campaign C          144531                    5\n",
       "2    Campaign C          144531                    1\n",
       "3    Campaign C          144531                    2\n",
       "4    Campaign C          144531                    2\n",
       "..          ...             ...                  ...\n",
       "620  Campaign C          179977                    2\n",
       "621  Campaign C          179978                    0\n",
       "622  Campaign C          179979                    0\n",
       "623  Campaign C          179981                    2\n",
       "624  Campaign C          179982                    2\n",
       "\n",
       "[625 rows x 3 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CREATE A DATAFRAME FOR THE MOST RUN CAMPAIGN ID WITH THIER CONVERSIONS \n",
    "campaign_1178={'Campaign C':a, 'fb_campaign_id':b, 'Approved_Conversion':c}     \n",
    "campaign_1178=pd.DataFrame(campaign_1178)\n",
    "campaign_1178"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rmahla_admin/.pyenv/versions/3.9.2/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning:\n",
      "\n",
      "Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'fb_campaign_id vs. Approved_Conversion for campaign_c')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# relationship between fb_campaign_id and Approved_Conversion for campaign C\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.scatterplot(campaign_1178[\"fb_campaign_id\"], campaign_1178[\"Approved_Conversion\"])\n",
    "plt.title(\"fb_campaign_id vs. Approved_Conversion for campaign_c\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "INFERENCE: SMALLER ID HAVE MORE CONVERSION THAN LARGER CONVERSION ID"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CATEGORICAL DATA THAT CAN FIT INTO MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>date</th>\n",
       "      <th>campaign</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>interest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>Campaign A</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>Campaign A</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>Campaign A</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>Campaign A</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>Campaign A</td>\n",
       "      <td>30-34</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>2022-09-13</td>\n",
       "      <td>Campaign C</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Female</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>2022-09-14</td>\n",
       "      <td>Campaign C</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Female</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>2022-09-15</td>\n",
       "      <td>Campaign C</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Female</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>2022-09-16</td>\n",
       "      <td>Campaign C</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Female</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1142</th>\n",
       "      <td>2022-09-17</td>\n",
       "      <td>Campaign C</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Female</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1143 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date    campaign    age  gender interest\n",
       "0     2021-01-01  Campaign A  30-34    Male       15\n",
       "1     2021-01-02  Campaign A  30-34    Male       16\n",
       "2     2021-01-03  Campaign A  30-34    Male       20\n",
       "3     2021-01-04  Campaign A  30-34    Male       28\n",
       "4     2021-01-05  Campaign A  30-34    Male       28\n",
       "...          ...         ...    ...     ...      ...\n",
       "1138  2022-09-13  Campaign C  45-49  Female      109\n",
       "1139  2022-09-14  Campaign C  45-49  Female      110\n",
       "1140  2022-09-15  Campaign C  45-49  Female      111\n",
       "1141  2022-09-16  Campaign C  45-49  Female      113\n",
       "1142  2022-09-17  Campaign C  45-49  Female      114\n",
       "\n",
       "[1143 rows x 5 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .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>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Spent</th>\n",
       "      <th>Total_Conversion</th>\n",
       "      <th>Approved_Conversion</th>\n",
       "      <th>date_2021-01-02</th>\n",
       "      <th>date_2021-01-03</th>\n",
       "      <th>date_2021-01-04</th>\n",
       "      <th>date_2021-01-05</th>\n",
       "      <th>date_2021-01-06</th>\n",
       "      <th>...</th>\n",
       "      <th>interest_29</th>\n",
       "      <th>interest_30</th>\n",
       "      <th>interest_31</th>\n",
       "      <th>interest_32</th>\n",
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       "      <th>interest_64</th>\n",
       "      <th>interest_65</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1143 rows × 674 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Impressions  Clicks       Spent  Total_Conversion  Approved_Conversion  \\\n",
       "0            7350       1    1.430000                 2                    1   \n",
       "1           17861       2    1.820000                 2                    0   \n",
       "2             693       0    0.000000                 1                    0   \n",
       "3            4259       1    1.250000                 1                    0   \n",
       "4            4133       1    1.290000                 1                    1   \n",
       "...           ...     ...         ...               ...                  ...   \n",
       "1138      1129773     252  358.189997                13                    2   \n",
       "1139       637549     120  173.880003                 3                    0   \n",
       "1140       151531      28   40.289999                 2                    0   \n",
       "1141       790253     135  198.710001                 8                    2   \n",
       "1142       513161     114  165.609999                 5                    2   \n",
       "\n",
       "      date_2021-01-02  date_2021-01-03  date_2021-01-04  date_2021-01-05  \\\n",
       "0                   0                0                0                0   \n",
       "1                   1                0                0                0   \n",
       "2                   0                1                0                0   \n",
       "3                   0                0                1                0   \n",
       "4                   0                0                0                1   \n",
       "...               ...              ...              ...              ...   \n",
       "1138                0                0                0                0   \n",
       "1139                0                0                0                0   \n",
       "1140                0                0                0                0   \n",
       "1141                0                0                0                0   \n",
       "1142                0                0                0                0   \n",
       "\n",
       "      date_2021-01-06  ...  interest_29  interest_30  interest_31  \\\n",
       "0                   0  ...            0            0            0   \n",
       "1                   0  ...            0            0            0   \n",
       "2                   0  ...            0            0            0   \n",
       "3                   0  ...            0            0            0   \n",
       "4                   0  ...            0            0            0   \n",
       "...               ...  ...          ...          ...          ...   \n",
       "1138                0  ...            0            0            0   \n",
       "1139                0  ...            0            0            0   \n",
       "1140                0  ...            0            0            0   \n",
       "1141                0  ...            0            0            0   \n",
       "1142                0  ...            0            0            0   \n",
       "\n",
       "      interest_32  interest_36  interest_63  interest_64  interest_65  \\\n",
       "0               0            0            0            0            0   \n",
       "1               0            0            0            0            0   \n",
       "2               0            0            0            0            0   \n",
       "3               0            0            0            0            0   \n",
       "4               0            0            0            0            0   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "1138            0            0            0            0            0   \n",
       "1139            0            0            0            0            0   \n",
       "1140            0            0            0            0            0   \n",
       "1141            0            0            0            0            0   \n",
       "1142            0            0            0            0            0   \n",
       "\n",
       "      interest_66  interest_7  \n",
       "0               0           0  \n",
       "1               0           0  \n",
       "2               0           0  \n",
       "3               0           0  \n",
       "4               0           0  \n",
       "...           ...         ...  \n",
       "1138            0           0  \n",
       "1139            0           0  \n",
       "1140            0           0  \n",
       "1141            0           0  \n",
       "1142            0           0  \n",
       "\n",
       "[1143 rows x 674 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_objs = df.select_dtypes(include='object')\n",
    "df_nums = df.select_dtypes(exclude='object')\n",
    "df_nums.drop(['ad_id','fb_campaign_id'], axis=1, inplace = True)\n",
    "df_objs = pd.get_dummies(df_objs,drop_first=True)\n",
    "final_df = pd.concat([df_nums,df_objs],axis=1)\n",
    "final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LINEAR REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = final_df.drop('Approved_Conversion',axis=1)\n",
    "y = final_df['Approved_Conversion']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, random_state=101)\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaled_X_train = scaler.fit_transform(X_train)\n",
    "scaled_X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "base_elastic_model = ElasticNet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'alpha':[0.1,1,5,10,50,100],\n",
    "              'l1_ratio':[.1, .5, .7, .9, .95, .99, 1]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_model = GridSearchCV(estimator=base_elastic_model,\n",
    "                          param_grid=param_grid,\n",
    "                          scoring='neg_mean_squared_error',\n",
    "                          cv=5,\n",
    "                          verbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 42 candidates, totalling 210 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=ElasticNet(),\n",
       "             param_grid={&#x27;alpha&#x27;: [0.1, 1, 5, 10, 50, 100],\n",
       "                         &#x27;l1_ratio&#x27;: [0.1, 0.5, 0.7, 0.9, 0.95, 0.99, 1]},\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=ElasticNet(),\n",
       "             param_grid={&#x27;alpha&#x27;: [0.1, 1, 5, 10, 50, 100],\n",
       "                         &#x27;l1_ratio&#x27;: [0.1, 0.5, 0.7, 0.9, 0.95, 0.99, 1]},\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: ElasticNet</label><div class=\"sk-toggleable__content\"><pre>ElasticNet()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ElasticNet</label><div class=\"sk-toggleable__content\"><pre>ElasticNet()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=ElasticNet(),\n",
       "             param_grid={'alpha': [0.1, 1, 5, 10, 50, 100],\n",
       "                         'l1_ratio': [0.1, 0.5, 0.7, 0.9, 0.95, 0.99, 1]},\n",
       "             scoring='neg_mean_squared_error', verbose=1)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_model.fit(scaled_X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.1, 'l1_ratio': 1}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = grid_model.predict(scaled_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error,mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7047461936052075"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8555387921686077"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9249533999984041"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9440069991251093"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df['Approved_Conversion'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,y_pred);\n",
    "plt.xlabel('Actual');\n",
    "plt.ylabel('Predicted');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>Actual Value</th>\n",
       "      <th>Predicted Value</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>0</td>\n",
       "      <td>0.976857</td>\n",
       "      <td>-0.976857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>0</td>\n",
       "      <td>0.361330</td>\n",
       "      <td>-0.361330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>1</td>\n",
       "      <td>1.284620</td>\n",
       "      <td>-0.284620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>0.361330</td>\n",
       "      <td>0.638670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>1</td>\n",
       "      <td>0.361330</td>\n",
       "      <td>0.638670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107</th>\n",
       "      <td>5</td>\n",
       "      <td>3.438963</td>\n",
       "      <td>1.561037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>1</td>\n",
       "      <td>2.515673</td>\n",
       "      <td>-1.515673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>0</td>\n",
       "      <td>0.361330</td>\n",
       "      <td>-0.361330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>1</td>\n",
       "      <td>0.361330</td>\n",
       "      <td>0.638670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>0</td>\n",
       "      <td>0.361330</td>\n",
       "      <td>-0.361330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual Value  Predicted Value  Difference\n",
       "284              0         0.976857   -0.976857\n",
       "448              0         0.361330   -0.361330\n",
       "842              1         1.284620   -0.284620\n",
       "36               1         0.361330    0.638670\n",
       "927              1         0.361330    0.638670\n",
       "...            ...              ...         ...\n",
       "1107             5         3.438963    1.561037\n",
       "584              1         2.515673   -1.515673\n",
       "179              0         0.361330   -0.361330\n",
       "779              1         0.361330    0.638670\n",
       "367              0         0.361330   -0.361330\n",
       "\n",
       "[115 rows x 3 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df=pd.DataFrame({'Actual Value':y_test,'Predicted Value':y_pred,'Difference':y_test-y_pred})\n",
    "pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07354762773939506"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(pred_df['Difference'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MODEL USABILITY\n",
    "THE RMS ERROR IS CLOSE TO THE AVARAGE CONVERSION FEATURE\n",
    "THE MODEL CAN BE USED FOR PREDICTION OF NEW DATA"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}