Spocket.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Serg\\\\Untitled Folder'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(r\"C:\\Users\\Serg\\Desktop\\Python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions = pd.read_excel(\"KPMG_VI.xlsx\",sheet_name=\"Transactions\", index_col = 0,header=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transaction_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-02-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>41245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-05-21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>41701.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>402</td>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1793.43</td>\n",
       "      <td>248.82</td>\n",
       "      <td>36361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>88</td>\n",
       "      <td>3135</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1198.46</td>\n",
       "      <td>381.10</td>\n",
       "      <td>36145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1765.30</td>\n",
       "      <td>709.48</td>\n",
       "      <td>42226.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                product_id  customer_id transaction_date  online_order  \\\n",
       "transaction_id                                                           \n",
       "1                        2         2950       2017-02-25           0.0   \n",
       "2                        3         3120       2017-05-21           1.0   \n",
       "3                       37          402       2017-10-16           0.0   \n",
       "4                       88         3135       2017-08-31           0.0   \n",
       "5                       78          787       2017-10-01           1.0   \n",
       "\n",
       "               order_status           brand product_line product_class  \\\n",
       "transaction_id                                                           \n",
       "1                  Approved           Solex     Standard        medium   \n",
       "2                  Approved   Trek Bicycles     Standard        medium   \n",
       "3                  Approved      OHM Cycles     Standard           low   \n",
       "4                  Approved  Norco Bicycles     Standard        medium   \n",
       "5                  Approved  Giant Bicycles     Standard        medium   \n",
       "\n",
       "               product_size  list_price  standard_cost  \\\n",
       "transaction_id                                           \n",
       "1                    medium       71.49          53.62   \n",
       "2                     large     2091.47         388.92   \n",
       "3                    medium     1793.43         248.82   \n",
       "4                    medium     1198.46         381.10   \n",
       "5                     large     1765.30         709.48   \n",
       "\n",
       "                product_first_sold_date  \n",
       "transaction_id                           \n",
       "1                               41245.0  \n",
       "2                               41701.0  \n",
       "3                               36361.0  \n",
       "4                               36145.0  \n",
       "5                               42226.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='transaction_id'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0OWCGpKeAu4EPtqcLERERERERrVdX9sjfUWV9bHbJCsovBLZewb3TgdP7rXEREREREbHmqHGvWbvUnj0yIiIiIiIiVqyWQZukcZKukrRA0nxJnyjxHSRdK2mupIskbdDwzPbl3vxyf50Sf085ImB+OVw7IiIiIiKGiiGQPbKumbYu4NO2XwXsAhwpaRvg+8Cxtl8NXAD8G4CkDuAnwOG2twV2A56StAnwVWCPEn+xpD3a3puIiIiIiIgWqWXQZvs+2zeWz49QZXwcQ7VvbUYpdgVwYPm8FzDH9uzyzIO2lwMvA24rmSMBft3wTEREREREDHbubt9Vk9r3tEkaT3U49vVUKf73LbcOAsaVz1sBlnS5pBslfabE7wBeKWl8mY17Z8MzERERERERa7zaDtcGkPQiqgO2P2n7YUkfBr4l6T+BC4FlpWgH8AZgZ+Ax4EpJN9i+UtIRwDlAN/AHqtm33t41BZgCoOEbMmzYyBb2LCIiIiIi2mIIZI+sbdAmaS2qAdtZts8HsH0L1VJIJG0F/HMp3gn81vYD5d4lwGuAK21fBFxU4lOA5b29z/ZUYCpAx4gxblG3IiIiIiIi+lVd2SMF/ABYYPvrDfHR5ecw4D945vy1y4HtJa1XlkG+Cbi56ZmNgY9RJTOJiIiIiIgYFOqaaXs9cAgwV9KsEvt3YEtJR5bv5wNnANj+q6SvA38CDFxi++JS7puSdiifP2/7tnZ0ICIiIiIiBoAaE4S0Sy2DNtu/A7SC299cwTM/oUr73xyf3I9Ni4iIiIiIGFBqTUQSERERERHxggyBRCR17WlbR9IfJc2WNF/S50r8C5LmSJol6VeSNm967iWSHpV0TENsJ0lzJd0h6Vtlv1xERERERMSgUNc5bU8Cu9veAZgA7C1pF+Crtre3PQH4JfCfTc+dClzaFPsOVSr/Lcu1dwvbHRERERERA0kO124NVx4tX9cql20/3FBsJFXSEQAkvRO4C5jfENsM2MD2tbYN/IjqgO2IiIiIiIhBoc5z2oYDNwCvAL5t+/oS/xLwAeDvwJtLbCTwWWBP4JiGasZQneHWo7PEIiIiIiJiKMiettaxvbwsgxwLTJK0XYkfb3sccBZwVCn+OeDUhtm5Hr3tX+v14GxJUyTNlDSzu3tpv/QhIiIiIiKi1WrPHmn7b5KuptqLNq/h1tnAxcCJwGuBd0k6GdgI6Jb0BPBzqkFfj7HA4hW8ZyowFaBjxJheB3YREREREbGGyUxba0jaVNJG5fO6wFuAWyRt2VBsX+AWANu72h5vezzwDeD/2T7N9n3AI5J2KVkjPwD8on09iYiIiIiIaK26Zto2A35Y9rUNA861/UtJP5e0NdAN3A0c3oe6jgDOBNalyizZnF0yIiIiIiIGKw/+RXS1DNpszwF27CV+YB+ePanp+0xgu35rXERERERExABS+562iIiIiIiI1ZY9ba0haR1Jf5Q0W9J8SZ8r8XMkzSrXQkmzSnwTSVdJelTSaU11fUnSvZKaM0tGRERERESs8eqaaXsS2N32o5LWAn4n6VLb7+kpIOlrVGe1ATwBnEC1DLJ5KeRFwGnA7a1vdkREREREDChDYKatrj1tBnpmxtYq19M7CEsmyHcDu5fyS6kGdq/opa7ryjMtbnVERERERET71banrWSOvAF4BfBt29c33N4VuN92Zs8iIiIiImLFPPhn2mrZ0wZge7ntCVQHYk+S1LjscTIwvT/fJ2mKpJmSZnZ3L+3PqiMiIiIiIlqmtkFbD9t/A64G9gaQ1AEcAJzTz++Zanui7YnDho3sz6ojIvrV44uvqbsJbTWU+juU+gpDr79DyVD6tx1KfY2Bq5blkZI2BZ6y/TdJ6wJvAb5Sbr8FuMV2Zx1ti4io27qb71p3E9pqKPV3KPUVhl5/h5Kh9G87lPq6xhoCiUjqmmnbDLhK0hzgT8AVtn9Z7h1ML0sjJS0Evg58UFKnpG1K/GRJncB6JX5SOzoQERERERHRDnVlj5wD7LiCex9cQXz8CuKfAT7TX22LiIiIiIg1iL3qMm0kaW/gm8Bw4Pu2v9x0f0PgJ8BLqMZjp9g+Y2V11pY9MiJemKG2xj7LUyIiImKgKxnyvw3sCXQCf5J0oe2bG4odCdxs+x1l29itks6yvWxF9dY6aCudmgkssv12Sf9AlYBkPLAQeLftv5YDuL8PvIaqzT+y/V+S1gN+BrwcWA5cZPvY9vckov0yiImIiIhgoO1pmwTcYfsuAEk/BfYDGgdtBtYvZ1O/CHgI6FpZpXVnj/wEsKDh+7HAlba3BK4s3wEOAta2/WpgJ+CjksaXe6fYfiXVcsvXS3pbW1oeERERERHxbGOAexu+d5ZYo9OAVwGLgbnAJ+yVHzZX26BN0ljgn6lm0HrsB/ywfP4h8M7y2cDIchzAusAy4GHbj9m+CqBMJ95Ide5bREREREQMBd3dbbsaz34u15Sm1qiXFjZvunsrMAvYHJgAnCZpg5V1sc6Ztm9QJRBpHFW+2PZ9AOXn6BI/D1gK3AfcQzW79lBjZZI2At5BNUMXERERERHRrxrPfi7X1KYincC4hu9jqWbUGn0ION+VO4A/A69c2XtrGbRJejuwxPYNfXxkEtWetc2BLYBPS3pZQ30dVMcEfKtn/Wgv73x6VNzdvfSFdSAiIiIiIgYGd7fvWrU/AVtK2kLSCKrjzC5sKnMPsAeApBcDWwO9jmF61JWI5PXAvpL2AdYBNpD0E+B+SZvZvk/SZsCSUv69wGW2nwKWSPo9MJFnOjcVuN32N1b0wjIKngrQMWLMwMoLGhERERERazzbXZKOAi6nSvk/zfZ8SYeX+6cDXwDOlDSXajnlZ20/sLJ66zqn7TjgOABJuwHH2H6/pK8ChwJfLj9/UR65B9i9DOzWA3ahWl6JpC8CGwL/0r4eRERERETEQODugTUfY/sS4JKm2OkNnxcDez2fOuvOHtnsy8Cekm6nOtug5yC6b1Olw5xHNeV4hu05JZnJ8cA2wI2SZknK4C0iIiIiIgaN2g/Xtn01cHX5/CBlfWdTmUep0v43xzvpPUNLREREREQMBQPrnLaWGGgzbREREREREdGg1kGbpOGSbpL0y6b4MZIsaVT5Pl7S42X54yxJpzeUvUzSbEnzJZ0uaXi7+xERERERETUZWNkjW6Lu5ZGfABYATx8mJ2kc1X62e5rK3ml7Qi91vNv2w5JEdZ7bQcBPW9PciIiIiIiI9qptpq0kEfln4PtNt06lOnS7T2lgbD9cPnYAI/r6XERERERExJqgzuWR36AanD09zyhpX2CR7dm9lN+iLKX8raRdG29IupzqTLdHqGbbIiIiIiJiKOh2+66a1DJok/R2YIntGxpi61Gl7//PXh65D3iJ7R2BTwFnS3p6SaXttwKbAWsDu6/gnVMkzZQ0s7t7af91JiIiIiIiooXq2tP2emBfSfsA61DtafsxsAUwu9qexliqs9cm2f4/4EkA2zdIuhPYCpjZU6HtJyRdCOwHXNH8QttTgakAHSPGZAllRERERMRgkJT/rWH7ONtjbY8HDgZ+Y/tA26Ntjy/xTuA1tv9P0qY9WSElvQzYErhL0oskbVbiHcA+wC119CkiIiIiIqIV6s4e2VdvBD4vqQtYDhxu+yFJLwYulLQ2MBz4DXD6SuqJiIiIiIjBZAjMtNU+aLN9NXB1L/HxDZ9/Dvy8lzL3Azu3rnURERERERH1qn3QFhERERERsdo8+NNV1JnyH0nDSxr/X5bvEyRdJ2lWyfQ4qcT3lHSDpLnl53MyREq6UNK8dvchIiIiIiKileqeafsEsIAqeyTAycDnbF9aMkueDOwGPAC8w/ZiSdsBlwNjeiqRdADwaDsbHhERERERA8AQ2NNW20ybpLHAPwPfbwibZwZwGwKLAWzfZHtxic8H1inJR5D0Iqqz277YjnZHRERERES0U50zbd8APgOs3xD7JHC5pFOoBpT/1MtzBwI32X6yfP8C8DXgsZa1NCIiIiIiBqbu7GlrCUlvB5bYvqHp1hHA0bbHAUcDP2h6blvgK8BHy/cJwCtsX9CHd04p++Rmdncv7YdeREREREREtF5dM22vB/Yt+9bWATaQ9BPgHVT73AB+RsPSybKc8gLgA7bvLOHXATtJWkjVl9GSrra9W/MLbU8FpgJ0jBgz+IfjERERERFDgbOnrSVsH2d7bDmL7WDgN7bfT7WH7U2l2O7A7QCSNgIuBo6z/fuGer5je/NSzxuA23obsEVERERERKyp6s4e2ewjwDcldQBPAFNK/CjgFcAJkk4osb1sL6mhjRERERERMVAMgT1t8hA4jK5ZlkdGRERERDxX17JFqrsNz9djX/lQ2/62X++zZ9Ty+6n1cO2IiIiIiIhYudqWR5bkIY8Ay4Eu2xMlHQScBLwKmGR7Zik7AvguMBHoBj5h++py72pgM+DxUnWWTUZEREREDBEeAodr172n7c22H2j4Pg84gGqA1ugjALZfLWk0cKmkne2nU8W8r2eAFxERERERMZjUPWh7FtsLAKTnLBXdBriylFki6W9Us25/bGf7IiIiIiJigBkCiUjq3NNm4FeSbpA0ZRVlZwP7SeqQtAWwEzCu4f4ZkmZJOkG9jPgiIiIiIiLWVHXOtL3e9uKy3PEKSbfYnrGCstOo9rnNBO4G/gB0lXvvs71I0vrAz4FDgB81V1AGhlMANHxDhg0b2b+9iYiIiIiI9svh2q1je3H5uQS4AJi0krJdto+2PcH2fsBGlIO3bS8qPx8Bzl5RPban2p5oe2IGbBERERERsaaoZdAmaWSZGUPSSGAvqiQkKyq/XimHpD2psk3eXJZLjirxtYC3r6yeiIiIiIgYZLrdvqsmdS2PfDFwQdl+1gGcbfsySfsD/w1sClwsaZbttwKjgcsldQOLqJZAAqxd4msBw4FfA99rb1ciIiIiIiJap5ZBm+27gB16iV9AtVSyOb4Q2LqX+FKqpCQRERERETEUDYFz2urMHhkRERERERGrUNugTdJCSXNLqv6ZJXaSpEUlNkvSPg3lt5d0raT55bl1SnyEpKmSbpN0i6QD6+pTRERERES0Wfa0tdybbT/QFDvV9imNAUkdwE+AQ2zPlrQJ8FS5fTywxPZWkoYB/9DyVkdERERERLRJ3YO2vtoLmGN7NoDtBxvufRh4ZYl3A82DwIiIiIiIGKxyTltLGfiVpBvKwdc9jpI0R9I0SRuX2FaAJV0u6UZJnwGQtFG5/4US/5mkF7evCxEREREREa1V56Dt9bZfA7wNOFLSG4HvAC8HJgD3AV8rZTuANwDvKz/3l7RHiY8Ffl/quhZ41tLKHpKmSJopaWZ399LW9SoiIiIiItpnCOxpq23QZntx+bmEKs3/JNv3215eljl+D5hUincCv7X9gO3HgEuA1wAPAo/xzDEBPyvx3t431fZE2xOHDRvZsn5FRERERET0p1oGbZJGSlq/5zPVnrV5kjZrKLY/MK98vhzYXtJ6JSnJm4CbbRu4CNitlNsDuLkNXYiIiIiIiGiLuhKRvBi4QFJPG862fZmkH0uaQLXfbSHwUQDbf5X0deBP5d4lti8udX0W+LGkbwB/AT7Uxn5ERERERESNPAQO165l0Gb7LmCHXuKHrOSZn1Cl/W+O3w28sV8bGBERERERMUCsKSn/IyIiIiIinqvGBCHtUmf2yIiIiIiIiFiF2mbaJC0EHgGWA122J0raATgdeBHVnrb32X5Y0iRgas+jwEm2LyjJTK5pqHYs8BPbn2xPLyIiIiIiolZDYKat7uWRb7b9QMP37wPH2P6tpA8D/wacQJVFcqLtrpJhcraki2w/QnWmGwCSbgDOb1/zIyIiIiIiWmugLY/cGphRPl8BHAhg+zHbXSW+DlUGyWeRtCUwmmfPvEVERERExGDm7vZdNalz0GbgV5JukDSlxOYB+5bPBwHjegpLeq2k+cBc4PCGQVyPycA55ey2iIiIiIiIQaHO5ZGvt71Y0mjgCkm3AB8GviXpP4ELgWU9hW1fD2wr6VXADyVdavuJhvoOBlZ4ZEAZGE4B0PANGTZsZP/3KCIiIiIi2msI7GmrbabN9uLycwlwATDJ9i2297K9EzAduLOX5xYAS4HtemIlgUmH7RtW8r6ptifanpgBW0RERERErClqGbRJGlkyPyJpJLAXMK/MuiFpGPAfVJkkkbSFpI7y+aVUe98WNlQ5mWqQFxERERERQ4i73barLnUtj3wxcIGknjacbfsySZ+QdGQpcz5wRvn8BuBYSU8B3cDHmrJOvhvYpz1Nj4iIiIiIaB8NxbwdHSPGDL1OR0RERESsQteyRaq7Dc/XI//69rb9bb/+t35Zy+9noKX8j4iIiIiIiAa1DdokbSTpPEm3SFog6XWSviBpjqRZkn4lafOG8sdJukPSrZLe2hCfLGluee4ySaPq6VFERERERLRdd3f7rprUOdP2TeAy268EdgAWAF+1vb3tCcAvgf8EkLQNVUr/bYG9gf+RNLwkJ/km8Gbb2wNzgKPa3pOIiIiIiIgWqSt75AbAG4EfANheZvtvth9uKDaS6gBugP2An9p+0vafgTuASYDKNVJVVpMNgMVt6kZERERERETL1ZU98mXAX4AzyhlrNwCfsL1U0peADwB/B95cyo8Brmt4vhMYY/taSUcAc6nObrsdOJKIiIiIiBgacrh2y3QArwG+Y3tHqgHXsQC2j7c9DjiLZ5Y69palxZLWAo4AdgQ2p1oeeVxvL5Q0RdJMSTO7u5f2a2ciIiIiIiJapa5BWyfQafv68v08qkFco7OBAxvKj2u4N5ZqGeQEANt3ujq74Fzgn3p7oe2ptifanjhs2Mh+6URERERERNSs2+27alLLoM32/wH3Stq6hPYAbpa0ZUOxfYFbyucLgYMlrS1pC2BL4I/AImAbSZuWcntSJTSJiIiIiIgYFOra0wbwceAsSSOAu4APAd8vA7lu4G7gcADb8yWdC9wMdAFH2l4OLJb0OWCGpKfKMx9se08iIiIiIqIW1YK7wU1DoZPNOkaMGXqdjoiIiIhYha5li3rLJTGgPfzRt7btb/sNvnt5Lb+fOmfaIiIiIiIiXphkj2wdSRtJOk/SLZIWSHpdiX9c0q2S5ks6ucTeJ2lWw9UtaUK59x5JcxrLR0REREREDBZ1zrR9E7jM9rvKvrb1JL2Z6iDt7W0/KWk0gO2zqI4AQNKrgV/YniVpE+CrwE62/yLph5L2sH1lPV2KiIiIiIi2ykxba0jaAHgj8AMA28ts/43qzLUv236yxJf08vhkYHr5/DLgNtt/Kd9/zTPHBERERERERKzx6loe+TLgL8AZkm6S9H1JI4GtgF0lXS/pt5J27uXZ9/DMoO0O4JWSxkvqAN7Js89zi4iIiIiIQczdbttVl7oGbR1Uh2l/x/aOwFLg2BLfGNgF+DfgXElPZ2iR9FrgMdvzAGz/lWp27hzgGmAh1ZEAzyFpiqSZkmZ2dy9tVb8iIiIiIiL6VV2Dtk6g0/b15ft5VIO4TuB8V/5IdV7bqIbnDuaZWTYAbF9k+7W2XwfcCtze2wttT7U90fbEYcNG9nN3IiIiIiKiFt1u31WTWgZttv8PuLccpA2wB9XB2f8L7A4gaStgBPBA+T4MOAj4aWNdPclKJG0MfAz4fut7EBERERER0R51Zo/8OHBWyRx5F/AhqmWS0yTNA5YBh/qZ07/fSDU7d1dTPd+UtEP5/Hnbt7Wh7RERERERMRB0192A1qtt0GZ7FjCxl1vvX0H5q6n2ujXHJ/drwyIiIiIiIgaQ2g7XjoiIiIiIiFWr65y2rSXNargelvRJSf8g6QpJt5efG5fy4yU93lD+9Ia6dpI0V9Idkr7VmG0yIiIiIiIGt6T8bxHbt9qeYHsCsBPwGHABVdr/K21vCVxZvve4s+cZ24c3xL8DTAG2LNfe7ehDREREREREOwyE5ZF7UA3I7gb2A35Y4j+kOix7hSRtBmxg+9qSsORHq3omIiIiIiIGkaT8b4vGs9debPs+gPJzdEO5LSTdJOm3knYtsTFUZ7v16CyxiIiIiIiIQaHOlP+UdP/7Asetouh9wEtsPyhpJ+B/JW0L9LZ/rdchsKQpVMso0fANyQHbERERERGDwBBI+V/3TNvbgBtt31++31+WPPYsfVwCYPtJ2w+WzzcAdwJbUc2sjW2obyywuLcX2Z5qe6LtiRmwRURERETEmqLuQdtknlkaCXAhcGj5fCjwCwBJm0oaXj6/jCrhyF1lCeUjknYpWSM/0PNMREREREQMfkMhe2RtyyMlrQfsCXy0Ifxl4FxJhwH3AAeV+BuBz0vqApYDh9t+qNw7AjgTWBe4tFwRERERERGDQm2DNtuPAZs0xR6kyibZXPbnwM9XUM9MYLtWtDEiIiIiIga47GmLiIiIiIiIOtUyaJO0taRZDdfDkj7ZcP8YSZY0qnzfU9INkuaWn7s3lP2SpHslPVpDVyIiIiIiokbZ09Yitm8FJgCUBCOLgAvK93FUe93uaXjkAeAdthdL2g64nGfOY7sIOA24vS2Nj4iIiIiIaKNaz2kr9gDutH13+X4q8BkaskDavqmh/HxgHUlrl6MArgOokkdGRERERMSQkj1tbXEwJe2/pH2BRbZnr6T8gcBNtp9sR+MiIiIiIiLqVOtMm6QRwL7AceUIgOOBvVZSflvgKysrs5JnpwBTADR8Q3LAdkRERETEms+ZaWu5twE32r4feDmwBTBb0kJgLHCjpH8EkDSWat/bB2zf+XxfZHuq7Ym2J2bAFhERERERa4q697RNpiyNtD0XGN1zowzcJtp+QNJGwMXAcbZ/X0M7IyIiIiIialHbTFtZDrkncH4fih8FvAI4oeGYgNGlnpMldQLrSeqUdFLLGh0REREREQNLdxuvmsiu77yBunSMGDP0Oh0RERERsQpdyxatcSnZH/znN7Xtb/tNLv5tLb+fupdHRkRERERErLahkIiklkGbpK2BcxpCLwP+E3gdsHWJbQT8zfaEkmXyu8BEqonJT9i+uiyx/BlVEpPlwEW2j21PLyIiIiIiIlqvlkGb7VuBCQCShgOLgAtsf6OnjKSvAX8vXz9Snnt12ct2qaSdy71TbF9VBnZXSnqb7Uvb05OIiIiIiKjVEJhpqzvlP8AewJ227+4JSBLwbkpmSWAb4EoA20uAv1FllnzM9lUlvgy4keqogIiIiIiIiEFhIAzaDuaZwVmPXYH7bd9evs8G9pPUIWkLYCdgXOMD5ViAd1AGdxERERERMfi5u31XXWpNRFKWNO4LHNd06+nz24ppwKuAmcDdwB+AroZ6Okr5b9m+awXvmgJMAdDwDckB2xERERERsSaoO3vk24Abbd/fEygDsAOoZtMAsN0FHN1Q5g/A7Q31TAVub9wT18z21FIuKf8jIiIiIgaJoZA9su7lkc0zagBvAW6x3dkTkLSepJHl855Al+2by/cvAhsCn2xLiyMiIiIiIlZA0t6SbpV0h6ReM9tL2k3SLEnzJf12VXXWNtNW0vXvCXy06VZve9xGA5dL6qbKNHlIqWMscDxwC3Bjlb+E02x/v4VNj4iIiIiIAWIgzbSVzPjfphrndAJ/knRhz4RTKbMR8D/A3rbvKdnxV6q2QZvtx4BNeol/sJfYQp45v60x3gmscae2R0RERETEoDQJuKMnz4aknwL7ATc3lHkvcL7te+Dp7PgrVffyyIiIiIiIiNVnte9atTHAvQ3fO0us0VbAxpKulnSDpA+sqtLaBm2Sji5rOOdJmi5pHUkHlVi3pIlN5beXdG25P1fSOiV+maTZJX56mZKMiIiIiIjoV5KmSJrZcE1pLtLLY81JEDuoki7+M/BW4ARJW63svbUsj5Q0BvhXYBvbj0s6l2ov2/VUmSO/21S+A/gJcIjt2ZI2AZ4qt99t++FyIPd5wEHAT9vUlYjaPL74mrqb0Fbrbr5r3U2IiIiIAaide9oaM9KvQCfPPk96LLC4lzIP2F4KLJU0A9gBuG1FldaZ8r8DWFfSU8B6wGLbCwBKQpFGewFzbM8GsP1gzw3bDzfUN4LnjmQjBqUMYiIiIiIGnD8BW0ragiqB4sFUe9ga/QI4rUxMjQBeC5y6skprGbTZXiTpFOAe4HHgV7Z/tZJHtgIs6XJgU+Cntk/uuVnik4BLqWbbIga9zLRFREREDCy2uyQdBVwODAem2Z4v6fBy/3TbCyRdBswBuoHv2563snrrWh65MVUWlS2AvwE/k/R+2z9ZwSMdwBuAnYHHgCsl3WD7SgDbby173M4Cdgeu6OWdU4ApABq+IcOGjezfTkW0WQYxEREREeDugZVM3vYlwCVNsdObvn8V+Gpf66wrEclbgD/b/ovtp4DzgX9aSflO4Le2HyhHBVwCvKaxgO0ngAupBoPPYXuq7Ym2J2bAFhERERERa4q6Bm33ALtIWq8kENkDWLCS8pcD25fyHcCbgJslvUjSZvB0spJ9qA7ajoiIiIiIIcDd7bvqUsugzfb1VHvPbgTmlnZMlbS/pE7gdcDFZa8atv8KfJ1qY98s4EbbFwMjgQslzQFmA0uA04mIiIiIiBgkZA+9ZIsdI8YMvU5HRERERKxC17JFA2uDWB8set3ubfvbfsy1v6nl91Pb4doRERERERGxarUN2iQdLWm+pHmSpktaR9JJkhZJmlWufUrZSQ2x2ZL276W+CyWtNFVmREREREQMLkNhT1tdKf/HAP8KbGP7cUnnUh08B3Cq7VOaHpkHTCznHmwGzJZ0ke2uUt8BwKPtan9ERERERES71DJoa3j3upKeAtYDFgPjeytY0vz3WAd4et2qpBcBn6I6g+3cVjU2IiIiIiIGnoF2Tlsr1JU9chFwClXq//uAv9v+Vbl9lKQ5kqaVQ7gBkPRaSfOpsk0e3jPLBnwB+BrVodsRERERERGDSi2DtjIY2w/YAtgcGCnp/cB3gJcDE6gGc1/recb29ba3BXYGjit74CYAr7B9QR/eOUXSTEkzu7uX9neXIiIiIiKiBnb7rrrUlYjkLcCfbf/F9lPA+cA/2b7f9nLb3cD3gEnND9peACwFtqM6z20nSQuB3wFbSbq6txfanmp7ou2Jw4aNbEmnIiIiIiIi+ltde9ruAXaRtB7wOLAHMFPSZrbvK2X2p0pAgqQtgHtLIpKXAlsDC23PpJqdQ9J44Je2d2trTyIiIiIiojZDYU9bLYM229dLOg+4EegCbgKmAt8vSx4NLAQ+Wh55A3BsSVrSDXzM9gPtbndERERERES7yXUuzqxJx4gxQ6/TERERERGr0LVs0Ro3bbVwwp5t+9t+/Kwravn91Ha4dkRERERERKxabYM2SUdLmi9pnqTpJRvkDpKulTRX0kWSNmgof5ykOyTdKumtDfGrS2xWuUbX06OIiIiIiIj+t9I9bZLm0nCQdTPb26/OSyWNAf4V2Mb245LOBQ4GjgSOsf1bSR8G/g04QdI25f62VEcE/FrSVraXlyrfV5KSRERERETEEDIUdnutKhHJ28vPI8vPH5ef7+OFH2bdAaxbkousByymygo5o9y/ArgcOIHqTLef2n4S+LOkO6iOA7j2BbYhIiIiIiJiQFvp8kjbd9u+G3i97c/YnluuY4G3ruzZVdS7CDiFKvX/fcDfbf+KKsX/vqXYQcC48nkMcG9DFZ0l1uOMsjTyBElr3ObJiIiIiIhYPe5W26669HVP20hJb+j5IumfgNU+oVrSxlSzZ1tQLXccKen9wIeBIyXdAKwPLOt5pJdqeiZC32f71cCu5TpkBe+cImmmpJnd3UtXt+kRERERERFt1ddz2g4DpknasHz/G9UAa3W9Bfiz7b8ASDof+CfbPwH2KrGtgH8u5Tt5ZtYNYCzVcsqeWTtsPyLpbKplkz9qfqHtqVRnwSXlf0RERETEIGEP/oV2fZpps32D7R2A7YEdbE+wfeMLeO89wC6S1ivLGfcAFvRkfpQ0DPgP4PRS/kLgYElrS9oC2BL4o6QOSaPKM2tR7cGb9wLaFRERERERMaCsKnvk+23/RNKnmuIA2P766rzU9vWSzgNuBLqAm6hmwQ6X1JP05HzgjFJ+fskweXMpf6Tt5ZJGApeXAdtw4NfA91anTRERERERseZxd90taL1VLY/s2be2fn+/2PaJwIlN4W+Wq7fyXwK+1BRbCuzU322LiIiIiIgYKFY6aLP93fLzcysrJ+k42//Vnw2LiIiIiIhYle7saeuzg57vA5I+IWmepPmSPlliX5V0i6Q5ki6QtFGJv6+k9O+5uiVNKPdGSJoq6bby7IH91KeIiIiIiIja9deg7XkNbyVtB3yEKtPjDsDbJW1JdaD2dra3B24DjgOwfVZJfjKBKqX/QtuzSnXHA0tsbwVsA/z2hXcnIiIiIiLWBLbadtWlvwZtzzeF/quA62w/ZruLaqC1v+1fle8A11Gl9m82GZje8P3DwH8B2O62/cDzbEtERERERMSAVctMG1Va/jdK2kTSesA+PPscNqgGY5f28ux7KIO2nuWTwBck3SjpZ5Je/DzbEhERERERayh3q21XXfp6uPaq/Oz5FLa9QNJXqJZDPgrMpkrlD4Ck48v3sxqfk/Ra4DHbPWexdVDNxv3e9qfK0QSnUC2hpOnZKcAUAA3fkGHDRjYXiVijPL74mrqb0Fbrbr5r3U2IiIiIqIXsVa9slLQp1R608TQM9Gx/uF8aIf0/oNP2/0g6FDgc2MP2Y03lTgX+Yvv/le+iGvStb7tb0jjgMtvbrux9HSPGPN/lnBERERERg17XskVrXCrGBVvu07a/7V91+yW1/H76OtP2C+AaqsOrl/fHiyWNtr1E0kuAA4DXSdob+Czwpl4GbMOoslS+sSdm25IuAnYDfgPsQXUAd0RERERExKDQ10HberY/28/v/rmkTYCngCNt/1XSacDawBXVJBrX2T68lH8j1WzcXU31fBb4saRvAH8BPtTP7YyIiIiIiKhNXwdtv5S0j+1L+uvFtp+zQcX2K1ZS/mpgl17id9Mw+xYREREREUNHnQlC2qWv2SM/QTVwe0LSI+V6uJUNi4iIiIiIiD4O2myvb3uY7XXK5/Vtb/BCXizpE5LmSZov6ZMN8Y9LurXET2565iWSHpV0TPm+vqRZDdcDZZlkREREREQMAd1W26669Dnlv6R9eWYZ4tW2f7m6L5W0HVU2yknAMuAySRdTpe/fD9je9pOSRjc9eioNZ7fZfgSY0FDvDcD5q9uuiIiIiIiIgaZPgzZJXwZ25plz0z4h6Q22j13N976KKsnIY6X+3wL7AxOBL9t+EsD2koY2vBO4C1i6gjZuCYymynIZERERERFDgGucAWuXvu5p2wfY0/Y029OAvUtsdc0D3ihpE0nrlbrGAVsBu0q6XtJvJe0MIGkkVZbIz62kzsnAOe7LwXMRERERERFriD4vjwQ2Ah4qnzd8IS+1vUDSV4ArqA7Hng10lfZsTJUlcmfgXEkvoxqsnWr70XIUQG8OBg5Z0U1JU4ApABq+IcOGjXwhXYiIiIiIiAFgKEzZ9HXQ9l/ATZKuAkS1t+24F/Ji2z8AfgAg6f8BnVTLJs8vs2V/lNQNjAJeC7yrJCbZCOiW9ITt08rzOwAdtm9YyfumAlMBOkaMGQL/tDHYPb54aK0EXnfz55wSEhERETEk9GnQZnu6pKupZr8EfNb2/72QF0sabXuJpJcABwCvA7qB3YGrJW0FjAAeaDzTTdJJwKM9A7ZiMjD9hbQnYk2TQUxEREQEtWZ1bJeVDtokvdL2LZJeU0Kd5efmkja3feMLePfPJW0CPAUcafuvkqYB0yTNo8oqeWgf96i9mxe2xy4iIiIiImJAWtVM26eo9oF9rZd7ppoVWy2Ns2cNsWXA+1fx3Em9xF62uu2IiIiIiIg111DIHrnSQZvtKeXj22w/0XhP0jota1VEREREREQAfU/5/4c+xp5F0jRJS8pyx57YP0i6QtLt5efGDfeOk3SHpFslvbUh/iVJ90p6tKn+l0i6StJNkuZIyhLJiIiIiIghxG7fVZeVDtok/aOknYB1Je0o6TXl2g1Yrw/1n0l1plujY4ErbW8JXFm+I2kbqrT925Zn/kfS8PLMRcCkXur/D+Bc2zuWZ/+nD22KiIiIiIhYY6xqT9tbgQ8CY6n2tfUsGH0Y+PdVVW57hqTxTeH9gN3K5x8CV1MdnL0f8FPbTwJ/lnQH1UDtWtvXAfRyRpuBDcrnDYHFq2pTREREREQMHkM+e6TtHwI/lHSg7Z/30ztfbPu+Uv99kkaX+BjguoZynSW2MicBv5L0cWAk8JZ+amNERERERMSA0Nc9bTtJ2qjni6SNJX2xn9vS2xB5VStHJwNn2h5LlfL/x5J67ZOkKZJmSprZ3b30BTY1IiIiIiIGAlttu+rSp8O1qbJHPr0cspyptg/VnrLn635Jm5VZts2AJSXeCYxrKDeWVS93PIyyZ872tSWj5aiGOp9meyowFaBjxJgatxFG9I/HF19TdxPaKoeJR0RExFDV10HbcElrl/1mSFoXWHs133khcCjw5fLzFw3xsyV9Hdgc2BL44yrqugfYAzhT0quAdYC/rGa7ItYoGcREREREDA19HbT9BLhS0hlUSxY/TJVEZKUkTadKOjJKUidwItVg7VxJh1ENug4CsD1f0rnAzUAXcKTt5aWek4H3AuuVer5fDtn+NPA9SUeXdn3QrjMZZ0REREREtNNQSESivo5xJL2NalZLwK9sX97KhrVSlkdGRERERDxX17JFa9wI6PrND2jb3/avXXx+Lb+fvs60YftS4NIWtiUiIiIiIuJ5GQqzMX3KHilpF0l/kvSopGWSlkt6uNWNi4iIiIiIGOr6mvL/NKr0+rcD6wL/Avz3qh6SNE3SEknzGmL/IOkKSbeXnxuX+CRJs8o1W9L+Dc9MljRX0hxJl0kaVeIvlXRliV8taWzfux4REREREWu6bqttV136OmjD9h3AcNvLbZ8BvLkPj51JScnf4FjgSttbAleW7wDzgIm2J5RnviupQ1IH8E3gzba3B+YAR5VnTgF+VOKfB/6rr/2JiIiIiIhYE/R1T9tjkkYAs0omx/uAkat6yPYMSeObwvtRZZSEKgPl1cBnbT/WUGYdnlmeqnKNlPQgsAFwR7m3DXB0+XwV8L997E/EGi/ntEVERERQ66HX7dLXQdshVLNyR1ENksYBB67mO19s+z6AcsD26J4bkl4LTANeChxiu6vEjwDmAkuplmgeWR6ZXdrxTWB/YH1Jm9h+cDXbFrHGyCAmIiIiYmjo0/JI23fbfsL2w8C3gDPLcsl+Zft629sCOwPHSVpH0lrAEcCOVIduzwGOK48cA7xJ0k3Am4BFVGe8PYekKZJmSprZ3b20v5seERERERE16G7jVZe+Zo+8WtIGkv6BanbrDElfX8133i9ps1LvZsCS5gK2F1DNqm0HTCixO8vB2ecC/1Rii20fYHtH4PgS+3tvL7U91fZE2xOHDVvlys6IiIiIiIgBoa+JSDYss2wHAGfY3gl4y2q+80Lg0PL5UOAXAJK2KElHkPRSYGtgIdXs2TaSNi3P7AksKOVGSerpw3FUSysjIiIiImKIMGrbVZe+7mnrKLNi76bMaPWFpOlUSUdGSeoETgS+DJwr6TDgHuCgUvwNwLGSnqKaffyY7QdKPZ8DZpR7dwMfLM/sBvyXJAMzeGavW0RERERExKCgasXhKgpJBwEnAL+z/TFJLwO+ant1k5HUqmPEmKFwcHpERERExPPStWzRGpeK8eoXH9S2v+13u/9ntfx++jTTZvtnwM8avt/F6mePjIiIiIiIiD7q06Ct7Cf7CDC+8RnbH17Fc9OAtwNLbG9XYv8AnFPqWgi82/ZfJb0P+LeGx7cHXmN7Vjkj7jSq5ZDdwPG2fy5pbeBHwE7Ag8B7bC/sS58iIiIiImLN113jXrN26Wsikl8AGwK/Bi5uuFblTGDvptixwJW2twSuLN+xfZbtCbYnUJ0Lt9D2rPLM8VQDv62oDtT+bYkfBvzV9iuAU4Gv9LE/ERERERERa4S+JiJZz/Znn2/ltmdIGt8U3o9qxgzgh8DVQHPdk4HpDd8/DLyy1NkNPNBQ10nl83nAaZLkvmzUi4iIiIiIWAP0dabtl5L26ad3vtj2fQDl5+heyryHMmiTtFGJfUHSjZJ+JunFJTYGuLfU1QX8Hdikn9oZERERERED3FBI+d/XQdsnqAZuj0t6WNIjkh5uRYMkvRZ4zPa8EuoAxgK/t/0a4FrglJ7ivVTR6yybpCmSZkqa2d29tL+bHRERERER0RJ9GrTZXt/2MNvr2t6gfN9gNd95fznzjfJzSdP9g3n20sgHgceAC8r3nwGvKZ87gXGlrg6qfXcPraAPU21PtD1x2LCRq9n0iIiIiIgYSLrbeNWlrzNtSNpY0iRJb+y5VvOdFwKHls+HUiU56XnHMKrDtn/aEyv70y7imX1wewA391LXu4DfZD9bREREREQMJn1N+f8vVEskxwKzgF2olinuvornplMNtkZJ6gROBL4MnCvpMOAeqkFajzcCneUcuEafBX4s6RvAX4APlfgPSvwOqhm2g/vSn4iIiIiIGBzq3GvWLurLxJSkucDOwHW2J0h6JfA52+9pdQNboWPEmMzGRUREREQ06Vq2aI0bAf3qxQe37W/7ve7/aS2/n76m/H/C9hOSkLS27Vskbd3SlkVERERERKxCnXvN2qWve9o6S+r9/wWukPQLYPGqHpI0TdISSfMaYgdJmi+pW9LEhvgmkq6S9Kik05rquUzS7PLc6ZKGl/gbyzEAXZLe1ce+RERERERErDH6mj1yf9t/s30ScALVXrJ39uHRM4G9m2LzgAOAGU3xJ0rdx/RSz7tt7wBsB2zKM/vg7gE+CJzdh7ZERERERMQgMxSyR65yeWTJ6DjH9nYAtn/b18ptz5A0vim2oNTbXHYp8DtJr+ilnp4z4TqAEZSz2GwvLHUNhVnRiIiIiIgYglY502a7G5gt6SVtaM8KSbqc6ky3R4Dz6mxLREREREQMDEZtu+rS10QkmwHzJf0RWNoTtL1vS1rVC9tvlbQOcBbVUQNXPJ/nJU0BpgBo+IbkgO2IiIiIiFgT9HXQ9iLg7Q3fBXyl/5uzciWD5YXAfjzPQZvtqcBUSMr/iIiIiIjBonuNO6Tg+evroK2jeS+bpHVb0J7nkPQiYH3b90nqAPYBrmnHuyMiIiIiIuq20kGbpCOAjwEvkzSn4db6wO9XVbmk6cBuwChJncCJwEPAf1NlgbxY0izbby3lFwIbACMkvRPYC3gQuFDS2sBw4DfA6aX8zsAFwMbAOyR9zva2fep5RERERESs8bpr3GvWLrJXvFJQ0oZUA6L/Ao5tuPWI7Yda3LaWyfLIiIiIiIjn6lq2aI0bAf3iH9/btr/t9/u/s2v5/ax0ps3234G/A5Pb05yIiIiIiIho1KfDtVeXpGmSlkia1xA7SNJ8Sd2SJjbEx0t6XNKscp3ecG8nSXMl3SHpWyqHvEn6lKSbJc2RdKWkl7ayPxERERERMbC4jVddWjpoA84E9m6KzQMOAGb0Uv5O2xPKdXhD/DtU6fq3LFdPnTcBE21vT3V228n92PaIiIiIiIjatXTQZnsGVeKRxtgC27f2tQ5JmwEb2L7W1Qa8HwHvLHVdZfuxUvQ6YGy/NDwiIiIiItYI3W286tLqmbbnawtJN0n6raRdS2wM0NlQprPEmh0GXNrqBkZERERERLRTX89pa4f7gJfYflDSTsD/StoWes3h+awlpZLeD0wE3rSiyiVNoVpiiYZvyLBhI/ut4RERERERUY9urXEJL5+3ATNos/0k8GT5fIOkO4GtqGbWGpc9jgUW93yR9BbgeOBNpY4V1T8VmApJ+R8REREREWuOAbM8UtKmkoaXzy+jSjhyl+37gEck7VKyRn4A+EUptyPwXWBf20tqanpERERERNRkKGSPbOlMm6TpwG7AKEmdwIlUiUn+G9gUuFjSLNtvBd4IfF5SF7AcOLzhAO8jqDJRrku1b61n79pXgRcBPyunANxje99W9ikiIiIiIqKdVCVkHFqyPDIiIiIi4rm6li1a4zaInbPZ+9r2t/177jurlt/PgFkeGREREREREc/V0kGbpGmSlkia1xA7SNJ8Sd2SJjbE15L0Q0lzJS2QdFyJry9pVsP1gKRvlHuHl/KzJP1O0jat7E9ERERERAws3WrfVZdWz7SdCezdFJsHHADMaIofBKxt+9XATsBHJY23/YjtCT0XcDdwfnnmbNuvLvGTga+3phsRERERERH1aGkiEtszJI1vii0A0HPPUzAwUlIHVcKRZcDDjQUkbQmMBq4pdTXeH0m9SV0iIiIiIqLNuns91nlwGUh72s4DllIdsn0PcEpD9sgek4Fz3JA9RdKR5Uy3k4F/bVdjIyIiIiIimknaW9Ktku6QdOxKyu0sabmkd62qzoE0aJtElep/c2AL4NPlvLZGBwPTGwO2v2375cBngf9YUeWSpkiaKWlmd/fS/m15RA0eX3xN3U2IiIiIqN1AOqetnDv9beBtwDbA5N7ybpRyXwEu70sfB9Kg7b3AZbafKgdl/x5oTFSyA9Bh+4YVPP9T4J0rqtz2VNsTbU8cNmxkPzY7oh7rbr5r3U2IiIiIiGebBNxh+y7by6jGKPv1Uu7jwM+BJX2pdCAN2u4BdldlJLALcEvD/ck0zbKVPW49/hm4veWtjIiIiIiI6N0Y4N6G750l9jRJY4D9gdP7WmlLE5FImg7sBoyS1AmcCDwE/DewKXCxpFm230o1jXgGVXZJAWfYntNQ3buBfZpecZSktwBPAX8FDm1hdyIiIiIiYoBpZyp+SVOAKQ2hqbanNhbp5bHmlZXfAD5re3kvyRl71erskZNXcOuCXso+SpX2f0V1Ne9vw/YnVr91ERERERERfVcGaFNXUqQTGNfwfSywuKnMROCnZcA2CthHUpft/11RpS0dtEVERERERLRSd90NeLY/AVtK2gJYRJVI8b2NBWxv0fNZ0pnAL1c2YIMW72mTNE3SEknzGmJflXSLpDmSLpC0UYnvKekGSXPLz917qe/Cxroa4u+SZEkTm+9FRERERES0g+0u4CiqrJALgHNtz5d0uKTDV7feVs+0nQmcBvyoIXYFcJztLklfAY6jStf/APAO24slbUfV0ac37Uk6AHi0+QWS1qc6n+36VnUiIiIiIiIGpr6k4m8n25cAlzTFek06YvuDfamzpTNttmdQJR5pjP2qjEABrqNa54ntm2z3rPecD6wjaW0ASS8CPgV8sZfXfIHqYO0n+r8HERERERER9ao75f+HgUt7iR8I3GT7yfL9C8DXgMcaC0naERhn+5ctbWVERERERAxI3WrfVZfaBm2Sjge6gLOa4ttSnQ7+0fJ9AvAK2xc0lRsGnAp8uo/vmyJppqSZ3d1LX3gHIiIiIiIi2qCW7JGSDgXeDuxh2w3xsVTHAXzA9p0l/DpgJ0kLqdo7WtLVVCeLbwdcXdJl/iNwoaR9bc9sfmdjes6OEWMG2tLXiIiIiIhYDQMse2RLtH3QJmlvqsQjb7L9WEN8I+BiqiQlv++J2/4O8J1SZjxVSszdyu1RDc9fDRzT24AtIiIiIiJiTdXqlP/TgWuBrSV1SjqMKpvk+sAVkmZJ6smkchTwCuCEEp8laXQr2xcREREREWu27jZedVHD6sQhI8sjIyIiIiKeq2vZohrTbaye7459f9v+tv9o509q+f3UsqctIiIiIiKiP3iNG2Y+f61eHjlN0hJJ8xpiX5V0i6Q5ki4oe9l67m0v6VpJ8yXNlbROiX9J0r2SHm2q/4OS/tKwnPJfWtmfiIiIiIiIdmt1yv8zgb2bYlcA29neHrgNOA5AUgfwE+Bw29sCuwFPlWcuAiat4B3n2J5Qru/3b/MjIiIiImIgGwp72lo6aLM9A3ioKfYr213l63XA2PJ5L2CO7dml3IO2l5fP19m+r5VtjYiIiIiIGIhqO1y7+DBwafm8FWBJl0u6UdJn+ljHgWWp5XmSxrWmmREREREREfWobdAm6XigCzirhDqANwDvKz/3l7THKqq5CBhfllr+GvjhSt43RdJMSTO7u5e+4PZHRERERET9sjyyRSQdCrwdeJ+fOXOgE/it7QfKoduXAK9ZWT1lCeWT5ev3gJ1WUnaq7Ym2Jw4bNvKFdyIiIiIiIqIN2j5ok7Q38Flg3zI463E5sL2k9UpSkjcBN6+irs0avu4LLOjv9kZERERExMDlNl51aXXK/+nAtcDWkjolHQacBqwPXFHS9J8OYPuvwNeBPwGzgBttX1zqOVlSJ7Beqeek8op/LccDzAb+FfhgK/sTERERERHRbnpmdeLQ0TFizNDrdERERETEKnQtW7TGHVX9zZe8v21/23/inp/U8vupO3tkRERERERErERHKyuXNI0q4cgS29uV2FeBdwDLgDuBD9n+m6QRwHeBiVTJWT5h++ryzGTg36mWki4G3m/7gXLv3cBJ5d5s2+9tZZ8iIlrt8cXX1N2EiHie1t1817qb0Fb571QMJHVmdWyXli6PlPRG4FHgRw2Dtr2A39jukvQVANuflXQkMNH2hySNpjq/bWeq2cDFwDa2H5B0MvCY7ZMkbQmcC+xu+6+SRttesqp2ZXlkRERERMRzrYnLI09t4/LIowfj8kjbM4CHmmK/st1Vvl4HjC2ftwGuLGWWAH+jmnVTuUZKErAB1SAO4CPAt0sSE/oyYIuIiIiIiMEj57S13oepZtQAZgP7SeqQtAXVmWvjbD8FHAHMpcy4AT8oz2wFbCXp95KuK8cJREREREREDBq1DdokHQ90AWeV0DSqA7ZnAt8A/gB0SVqLatC2I7A5MAc4rjzTAWwJ7AZMBr4vaaMVvG+KpJmSZnZ3L21BjyIiIiIiot2GwjltLU1EsiKSDqVKULKHy6a6smTy6IYyfwBuByaU+3eW+LnAsaVYJ3BdmY37s6RbqQZxf2p+p+2pwFTInraIiIiIiFhztH2mrSxh/Cywr+3HGuLrSRpZPu8JdNm+GVgEbCNp01J0T2BB+fy/wJvLM6Oolkve1Y5+RERERERE/brVvqsurU75P51q6eIoSZ3AiVRLG9cGrqjyinCd7cOB0cDlkrqpBmqHANheLOlzwAxJTwF3Ax8sr7gc2EvSzcBy4N9sP9jKPkVERERERLRTS1P+D1RZHhkRERER8VxrYsr/L7+0fSn/j717EKb8j4iIiIiIiBempYM2SdMkLZE0ryH2BUlzJM2S9CtJm5f4pBKbJWm2pP0bnrla0q0N90eX+KkNsdsk/a2V/YmIiIiIiGi3Vs+0nQk0n532Vdvb254A/BL4zxKfB0ws8b2B70pq3HP3PtsTyrUEwPbRPTHgv4HzW9aTiIiIiIgYcIZCyv+WDtpszwAeaoo93PB1JKX/th8raf8B1uH5/14mA9NXs6kREREREREDUl3ntH0J+ADwd0rK/hJ/LdUh2y8FDmkYxAGcIWk58HPgi27IoCLppcAWwG/a0PyIiIiIiBggumudA2uPWhKR2D7e9jjgLOCohvj1trcFdgaOk7ROufU+268Gdi3XIU1VHgycZ3v5it4paYqkmZJmdncv7c/uREREREREtEzd2SPPBg5sDtpeACwFtivfF5Wfj5RnJjU9cjCrWBppe6rtibYnDhs2sh+aHhERERERdetu41WXtg/aJG3Z8HVf4JYS36In8UhZ7rg1sFBSh6RRJb4W8HaqpCU99W0NbAxc254eREREREREtE9L97RJmg7sBoyS1AmcCOxTBlrdwN3A4aX4G4BjJT1V7n3M9gOSRgKXlwHbcODXwPcaXjMZ+KmH4inhERERERFD3FAYBLR00GZ7ci/hH6yg7I+BH/cSXwrstJJ3nLS67YuIiIiIiBjoaskeGRERERER0R/q3GvWLi3d0yZpmqQlkhr3oH1B0hxJsyT9StLmJf6+Euu5uiVNkLSepIsl3SJpvqQvN9T1EklXSbqp1LlPK/sTERERERHRbq1ORHImsHdT7Ku2t7c9Afgl8J8Ats+yPaHEDwEW2p5VnjnF9iuBHYHXS3pbif8HcK7tHakySP5PC/sSEREREREDTLfad9WlpYM22zOAh5piDzd8HUnvewcnU1L4237M9lXl8zLgRmBsT3XABuXzhsDifmt8RERERETEAFDLnjZJXwI+APwdeHMvRd4D7NfLcxsB7wC+WUInAb+S9HGqAeBbWtDciIiIiIgYoLqHQP7IWg7Xtn287XHAWcBRjfckvRZ4zPa8pngH1ezbt2zfVcKTgTNtjwX2AX4sqdc+SZoiaaakmd3dS/u5RxEREREREa1Ry6CtwdnAgU2xgylLI5tMBW63/Y2G2GHAuQC2rwXWAUb19iLbU21PtD1x2LCRL7TdERERERExALiNV13aPmiTtGXD132BWxruDQMOAn7a9MwXqfasfbKpunuAPUqZV1EN2v7S742OiIiIiIioSUv3tEmaDuwGjJLUCZwI7CNpa6ojFe4GDm945I1AZ8PyRySNBY6nGtzdKAngNNvfBz4NfE/S0VSD3w/aHvyLWiMiIiIiYsho6aDN9uRewj9YSfmrgV2aYp1Arwk2bd8MvP4FNDEiIiIiItZgOVw7IiIiIiIiatXSQZukaZKWSJrXy71jJFnSqIbYcZLukHSrpLc2xN8jaY6k+ZJOboi/VNKV5d7VZSllREREREQMEd24bVddWj3Tdiawd3NQ0jhgT6pEIj2xbagyR25bnvkfScMlbQJ8FdjD9rbAiyXtUR47BfiR7e2BzwP/1cK+REREREREtF1LB222ZwAP9XLrVOAzPDtz5n7AT20/afvPwB3AJOBlwG22e7JC/ppnjgnYBriyfL6KXg7kjoiIiIiIwSsp/1tA0r7AItuzm26NAe5t+N5ZYncAr5Q0vhyw/U5gXCkzm2cGcPsD65eZuYiIiIiIiEGhpdkjm0lajyp9/1693e4lZtt/lXQEcA5Vcpg/UM2+ARwDnCbpg8AMYBHQtYJ3TwGmAGj4huSA7YiIiIiINd9QyB7Z1kEb8HJgC2B2OW9tLNXZa5OoZtbGNZQdCywGsH0RcBE8PfhaXuKLgQNK/EXAgbb/3tuLbU8FpgJ0jBiTs9wiIiIiImKN0NZBm+25wOie75IWAhNtPyDpQuBsSV8HNge2BP5Yyo22vUTSxsDHgHeX+CjgIdvdwHHAtHb2JyIiIiIi6lVnVsd2aXXK/+nAtcDWkjolHbaisrbnA+cCNwOXAUfaXl5uf1PSzcDvgS/bvq3EdwNulXQb8GLgS63pSURERERERD1kD/6RabMsj4yIiIiIeK6uZYt6yzMxoB09/uC2/W1/6sKf1vL7afeetoiIWIXHF19TdxMi4nlad/Nd625CW+W/UxHt1dJBm6RpwNuBJba3a7p3DNWh2ZvafqAh/hKqJZIn2T5F0vpA438ZxgI/sf1JSWsDPwJ2Ah4E3mN7YSv7FBHRakPtj7+IWPPkv1ODV9eyRXU34XkbCtkjW31O25nA3s1BSeOAPYF7ennmVODSni+2H7E9oecC7gbOL7cPA/5q+xXlua/0a+sjIiIiIiJq1tJBm+0ZwEO93DoV+AxNB4tLeidwFzC/t/okbUmVfbJn5m0/4Ifl83nAHipnCURERERExODnNv6vLq2eaXsOSfsCi2zPboqPBD4LfG4lj08GzvEz2VPGAPcC2O4C/g5s0u+NjoiIiIiIqElbE5FIWg84Htirl9ufA061/ehKJssOBg5prLKXMr0Ogcuh3FMANHxDhg0b2ddmR0RERERE1Kbd2SNfDmwBzC4Ds7HAjZImAa8F3iXpZGAjoFvSE7ZPA5C0A9Bh+4aG+jqBcUCnpA5gQ3pfjontqcBUSMr/iIiIiIjBYigkImnroM32XKo9aQBIWghMLNkjd22InwQ82jNgKyYD05uqvBA4lOoA73cBv/FQPHguIiIiIiIGrVan/J8O7AaMktQJnGj7B6tZ3buBfZpiPwB+LOkOqhm2g1e3rRERERERsebprjFBSLu0dNBme/Iq7o9fQfykXmIv6yX2BHDQajYvIiIiIiJiwGv3nraIiIiIiIh+M/jn2WpI+R8RERERERF919JBm6RpkpZImtfLvWMkWdKo8n0tST+UNFfSAknH9fLMhY11SXqjpBsldUl6Vyv7EhERERERA083bttVl1bPtJ0J7N0clDQO2BO4pyF8ELC27VcDOwEflTS+4ZkDgEebqroH+CBwdn82OiIiIiIiYqBo6aDN9gx6PzftVOAzPHsJqoGR5by1dYFlwMMAkl4EfAr4YlP9C23PYWgczxAREREREU2623jVpe172iTtCyyyPbvp1nnAUuA+qhm0U2z3DPi+AHwNeKxtDY2IiIiIiBgA2po9UtJ6wPHAXr3cngQsBzYHNgaukfRrYAPgFbaPblwuuRrvngJMAdDwDRk2bOTqVhUREREREQOEh0D+yHan/H85sAUwWxLAWOBGSZOA9wKX2X4KWCLp98BEYBNgJ0kLS3tHS7ra9m7P58W2pwJTATpGjBn8/7IRERERETEotHV5pO25tkfbHl8O1u4EXmP7/6iWRO6uykhgF+AW29+xvXkp/wbgtuc7YIuIiIiIiMEpe9peIEnTgWuBrSV1SjpsJcW/DbwImAf8CTijJBlZWf07S+qkyjz5XUnz+6npERERERERA0JLl0fanryK++MbPj9KNfhaWfmFwHYN3/9EtcQyIiIiIiKGoKGwp63t2SMjIiIiIiKi71q9PHKapCWS5jXETpK0SNKscu1T4ntKukHS3PJz94ZnviTpXkmPNtX/KUk3S5oj6UpJL21lfyIiIiIiItqt1TNtZwJ79xI/1faEcl1SYg8A77D9auBQ4McN5S+iOhKg2U3ARNvbU53zdnK/tTwiIiIiIga8JCJ5gWzPAB5aZcGq7E22F5ev84F1JK1d7l1n+75enrnKds+B29eR/W0RERERETHI1LWn7aiypHGapI17uX8gcJPtJ59HnYcBl/ZP8yIiIiIiYk3Qbbftqksdg7bvUB2yPQG4D/ha401J2wJfAT7a1wolvZ/qIO6vrqTMFEkzJc3s7l66Gs2OiIiIiIhov5am/O+N7ft7Pkv6HvDLhu9jgQuAD9i+sy/1SXoLcDzwppXNzNmeCkwF6BgxZvDnBY2IiIiIGAKGwh/2bZ9pk7RZw9f9qQ7TRtJGwMXAcbZ/38e6dgS+C+xre0k/NzUiIiIiIqJ2rU75Px24FthaUqekw4CTS1r/OcCbgaNL8aOAVwAnNBwHMLrUc7KkTmC9Us9J5ZmvAi8CflbKX9jK/kRERERExMDSjdt21UWucUNdXbI8MiIiIiLiubqWLVLdbXi+3vvS/dv2t/3Zd19Qy++n7XvaIqL/PL74mrqb0Dbrbr5r3U2IiIiIAchDYFdbSwdtkqYBbweW2N6uxE4CPgL8pRT7d9uXSBoPLABuLfHrbB9enpkM/DvVPsPFwPttPyDpcOBIYDnwKDDF9s2t7FPEQJKBTERERMTg1+pEJGcCe/cSP9X2hHJd0hC/syHeM2DrAL4JvNn29sAcqv1vAGfbfrXtCcDJwNdb1ZGIiIiIiBh4utt41aWlgzbbM4CHXmA1KtdISQI2oJptw/bDDeVGMjQyfkZERERExBBSx+HaAEdJmiNpmqSNG+JbSLpJ0m8l7Qpg+yngCGAu1WBtG+AHPQ9IOlLSnVQzbf/avi5ERERERETdhkL2yDoGbd8BXg5MAO4Dvlbi9wEvsb0j8CngbEkbSFqLatC2I7A51fLI43oqs/1t2y8HPgv8x4peKmmKpJmSZnZ3L+3/XkVERERERLRA2wdttu+3vdx2N/A9YFKJP2n7wfL5BuBOYCuqwR2273R1PsG5wD/1UvVPgXeu5L1TbU+0PXHYsJH92KOIiIiIiKiL2/i/urR90CZps4av+wPzSnxTScPL55cBWwJ3AYuAbSRtWp7ZkyrLJJK2bKjrn4HbW9v6iIiIiIiI9mp1yv/pwG7AKEmdwInAbpImUCUNWQh8tBR/I/B5SV1UKfwPt/1QqedzwAxJTwF3Ax8szxwl6S3AU8BfgUNb2Z+IiIiIiIh2U7XicGjpGDFm6HU6IiIiImIVupYtUt1teL4OeOm+bfvb/vy7L6zl91NX9siIiIiIiIjog5YO2kpK/yWS5jXETpK0SNKscu1T4iMknSFprqTZknZreOZqSbc2PDO66T3vkmRJE1vZn4iIiIiIGFhst+2qS6tn2s4E9u4lfqrtCeW6pMQ+AmD71VTJRr4mqbF972t4ZklPUNL6VOezXd+SHkRERERERPSRpL3LhNMdko7t5f77ypnVcyT9QdIOq6qzpYM22zOAh/pYfBvgyvLcEuBvQF9mzr5AdbD2E6vRxIiIiIiIWIMNpMO1Szb8bwNvoxrfTJa0TVOxPwNvsr091Vhm6qrqrWtP21FlZDlN0sYlNhvYT1KHpC2AnYBxDc+cUZZGniBJAJJ2BMbZ/mV7mx8REREREfEck4A7bN9lexnVWdL7NRaw/Qfbfy1frwPGrqrSOgZt3wFeTnVo9n3A10p8GtAJzAS+AfwB6Cr33leWTe5arkPK0slTgU/35aWSpkiaKWlmd/fS/ulJRERERETUqruNVx+MAe5t+N5ZYityGHDpqipt+6DN9v22l9vuBr5HNRrFdpfto8uetf2AjSiHZdteVH4+Apxdnlkf2A64WtJCYBfgwhUlI7E91fZE2xOHDRvZ0j5GRERERMTg0zgRVK4pzUV6eazXdZWS3kw1aPvsqt7b0sO1eyNpM9v3la/7A/NKfD2qc+OWStoT6LJ9s6QOYCPbD0haC3g78GvbfwdGNdR7NXCM7Znt7E9ERERERNTHfdhr1m/vsqey8j1onTx7i9dYYHFzIUnbA98H3mb7wVW9t6WDNknTgd2AUZI6gROB3SRNoBpxLgQ+WoqPBi6X1A0sAg4p8bVLfC1gOPBrqhm6iIiIiIiIgeRPwJYlR8ci4GDgvY0FJL0EOB84xPZtfam0pYM225N7Cf9gBWUXAlv3El9KlZRkVe/a7Xk2LyIiIiIi1nB9yerYLra7JB0FXE414TTN9nxJh5f7pwP/CWwC/E/Jr9hle6VZ81XnIXF16RgxZuh1OiIiIiJiFbqWLeptT9aAts9L9mnb3/aX3HNJLb+fliYiKSn9l0ia1xT/eDlwbr6kk0tsUknpP0vSbEn7l/h6ki6WdEsp/+WGej4o6S8Nz/1LK/sTEREREREDi+22XXVpdSKSM4HTgB/1BEqWlP2A7W0/KWl0uTUPmFimFDcDZku6qNw7xfZVkkYAV0p6m+2e1Jjn2D6qxf2IGHAeX3xN3U1oq3U337XuJkRERETUotV72mZIGt8UPgL4su0nS5kl5edjDWXWoaTGLPGryudlkm6kDwfQRQx2GcRERERE9Pn8tDVaHYdrbwXsKul6Sb+VtHPPDUmvlTQfmAscbrur8UFJGwHvAK5sCB8oaY6k8yQ1pteMiIiIiIhY49UxaOsANqY6DPvfgHNV0qbYvt72tsDOwHGS1ul5qJzXNh34lu27SvgiYLzt7amOAvjhil7aeBBed/fSVvQrIiIiIiLazG38X13qGLR1Aue78keqGc1RjQVsLwCWAts1hKcCt9v+RkO5B3uWWVKd3bbCowFsT7U90fbEYcNG9k9PIiIiIiIiWqyOQdv/ArsDSNoKGAE8IGmLMpuGpJdSndm2sHz/IrAh8MnGikrCkh77Agta2/SIiIiIiIj2amkiEknTgd2AUZI6gROBacC0cgzAMuBQ25b0BuBYSU9Rzb59zPYDksYCxwO3ADeWlZSn2f4+8K+S9gW6gIeAD7ayPxERERERMbAMpMO1WyWHa0dEREREBLBmHq79lnFvbdvf9r++9/Jafj+tPqctIiIiIiKiZYbCJFRL97RJmiZpSVkK2Rj/uKRbJc2XdHJDfHtJ15b43J7skZLeU9L6P6t8ufduSTeXe2e3sj8RERERERHt1uqZtjOB04Af9QQkvRnYD9je9pOSRpd4B/AT4BDbsyVtAjxVfn4V2Mn2XyT9UNIetq+UtCVwHPB623/tqSsiIiIiIoaGobCnraUzbbZnUCUIaXQE8OWeVP22l5T4XsAc27NL/EHby4GXAbfZ/ksp92vgwPL5I8C3bf+1qa6IiIiIiIhBoY6U/1sBu0q6XtJvJe3cELekyyXdKOkzJX4H8EpJ48ts3DuBcQ3PbCXp95Kuk7R3OzsSERERERH1GgqHa9eRiKQD2BjYBdgZOFfSy0r8DSX2GHClpBvKMsgjgHOojgL4A9XsW09dW1IdKzAWuEbSdrb/1vxSSVOAKQAaviE5YDsiIiIiItYEdQzaOoHzXaV5+aOkbmBUif/W9gMAki4BXgNcafsi4KISnwIsb6jrOttPAX+WdCvVIO5PzS+1PRWYCkn5HxERERExWHQne2RL/C+wO4CkrYARwAPA5cD2ktYryyDfBNxcyvUkK9kY+Bjw/Ya63lzujaJaLnlXm/oRERERERHRci2daZM0nWrp4ihJncCJwDRgWjkGYBlwaJl1+6ukr1PNkhm4xPbFpapvStqhfP687dvK58uBvSTdTDX79m+2H2xlnyIiIiIiYuAY/PNsoKFwGF2zLI+MiIiIiHiurmWLVHcbnq9dx+zRtr/tr1l0ZS2/nzr2tEVERERERPSLnNP2AkmaJmlJWQrZEztH0qxyLZQ0q+HecZLukHSrpLc2xEdImirpNkm3SDqwxE9tqOs2SX9rZX8iIiIiIiLardUzbWcCpwE/6gnYfk/PZ0lfA/5ePm8DHAxsC2wO/FrSVuWA7eOBJba3kjQM+IdS19ENdX0c2LHF/YmIiIiIiAEkM20vkO0ZwEO93ZMk4N3A9BLaD/ip7Sdt/5nqUO1J5d6Hgf8qdXb3HAvQZHJDXREREREREYNCHSn/e+wK3G/79vJ9DHBvw/1OYIykjcr3L0i6UdLPJL24sSJJLwW2AH7T4jZHRERERES0VZ2DtuaZsd4ysZhqCedY4Pe2XwNcC5zSVO5g4LyylLJXkqZImilpZnf30hfW8oiIiIiIGBBst+2qSy2DtnJ49gHAOQ3hTmBcw/exwGLgQeAx4IIS/xnwmqYqD2YVSyNtT7U90fbEYcNGvoDWR0REREREtE9dM21vAW6x3dkQuxA4WNLakrYAtgT+WA7evojqkG6APYCbex6StDWwMdUMXEREREREDCHduG1XXVqaPVLSdKrB1ihJncCJtn9ALzNjtudLOpdqQNYFHNmw3PGzwI8lfQP4C/ChhkcnUyUwGfxpYyIiIiIiYsjRUBzrdIwYM/Q6HRERERGxCl3LFvWWZ2JA23nzN7btb/s/LZ5Ry++nzkQkERERERERsQotHbRJmiZpiaR5DbFzJM0q10JJs5qeeYmkRyUd0xC7TNJsSfMlnS5peEPZqyTdJGmOpH1a2Z+IiIiIiBhYkj3yhTsT2LsxYPs9tifYngD8HDi/6ZlTgUubYu+2vQOwHbApcFCJ/wdwru0dqfbJ/U+/tj4iIiIiIqJmLU1EYnuGpPG93ZMk4N3A7g2xdwJ3Ac86SM32w+VjBzACnk7dYmCD8nlDqiMCIiIiIiJiiKgzq2O7tHTQtgq7Avfbvh1A0kiqLJF7Asc0F5Z0OTCJahbuvBI+CfiVpI8DI6mOEoiIWKM9vviaupsQEc/TupvvWncT2ir/nYporzoHbZN5dtr/zwGn2n60moR7NttvlbQOcBbV7NwVpY4zbX9N0uuojgXYznZ38/OSpgBTADR8Q3LAdkQMVEPtj7+IWPPkv1ODV9eyRXU34XkbCtnwaxm0SeoADgB2agi/FniXpJOBjYBuSU/YPq2ngO0nJF0I7Ec1aDuMsmfO9rVlUDcKWNL8TttTgamQlP8REREREbHmqGum7S3ALbY7ewK2n/6/bCSdBDxq+zRJLwLWt31fGeztA/TMyd8D7AGcKelVwDpUh29HRERERMQQMBT2tLU65f904Fpga0mdkg4rtw7m2UsjV2YkcKGkOcBsqlm008u9TwMfkTS71PdBD4X50YiIiIiIGDI0FMc4WR4ZEREREfFcXcsWPTe5xAC3/T++rm1/28/5v2tr+f20+py2iIiIiIiIeAFavTxymqQlkuY1xM6RNKtcCyXNKvG1JP1Q0lxJCyQd1/DMTiV+h6RvlTPekPRSSVdKmiPpakljW9mfiIiIiIiIdmv1TNuZlOyOPWy/x/YE2xOAnwPnl1sHAWvbfjVVVsmPNhzM/R2qdP1blqunzlOAH9neHvg88F8t60lERERERAw43Xbbrrq0dNBmewbwUG/3ymzZu3kmIYmBkSVD5LrAMuBhSZsBG9i+tiQZ+RHwzvLMNsCV5fNVVEcBREREREREDBp17mnbFbjf9u3l+3nAUuA+qlT+p9h+CBgDdDY811liUGWTPLB83h9YX9ImrW54REREREQMDG7j/+pS56BtMs9O+z8JWA5sDmwBfFrSy4DeMrT0/MaOAd4k6SbgTcAioKu3l0maImmmpJnd3Uv7qQsRERERERGtVcvh2mUJ5AFUe9d6vBe4zPZTwBJJvwcmUh2k3ZhgZCywGMD24lIP5RDuA23/vbd32p4KTIWk/I+IiIiIGCzq3GvWLnXNtL0FuMV247LHe4DdVRkJ7FLK3Ac8ImmXsg/uA8AvACSNktTTh+OAae3rQkREREREROu1OuX/dOBaYGtJnZIOK7cO5tlLIwG+DbwImAf8CTjD9pxy7wjg+8AdwJ3ApSW+G3CrpNuAFwNfalFXIiIiIiJiABoKe9rkITCd2CzLIyMiIiIinqtr2aLe8kkMaK8cvXPb/ra/Zcmfavn91LKnLSIiIiIioj9kT9sLJGmapCWS5jXEJki6TtKsks1xUolvIukqSY9KOq2pnsmS5kqaI+kySaNKfG1J50i6Q9L1DYdxR0REREREDAqtTkRyJrB3U+xk4HO2JwD/Wb4DPAGcQJXG/2kl0+Q3gTfb3h6YAxxVbh8G/NX2K4BTga/0fxciIiIiImKgGgp72lo6aLM9A3ioOQxsUD5vyDPp+5fa/h3V4K2RyjWyZI/coOcZYD/gh+XzecAepUxERERERMSgUMeetk8Cl0s6hWrQ+E8rK2z7KUlHAHOBpcDtwJHl9hjg3lKuS9LfgU2AB1rT9IiIiIiIGEiyp601jgCOtj0OOBr4wcoKS1qrPLMjsDnV8sjjem738kiv/2qSppQ9dDO7u5eubtsjIiIiIiLaqo5B26HA+eXzz4BJqyg/AcD2na7OJziXZ2bnOoFx8PTetw157nJMyvNTbU+0PXHYsJEvqAMRERERETEwZE9baywG3lQ+70613HFlFgHbSNq0fN8TWFA+X0g1CAR4F/AbD8WD5yJiUHl88TV1N6GthlJ/h1JfYej1dygZSv+2Q6mv8f/bu/N4Sar67uOf7wz7MgwGVJBdWYLIJpsGWUQQeGRRFEVAFpUg+oCiRlAMbhHkQRMkRkQQERSVCBEMEQhBhrAJw7CJGBQRAQ0iq8M6zPf5o6qZnsudlVt9+lZ937z6dbtOdTe/mr63u351zvmd4dXo4tqSzgG2A1YA/hc4FvgVVTXIRaiKjhxme2r9+LupCo0sBjwC7GT7dkmHAkcAzwK/Aw60/WdJSwBnUQ2dfAh4l+275hVXFteOiIiIiHih8bi49lorbDKwc/u7HpxW5N+n0aRtWCVpi4iIiIh4ofGYtK35VxsN7Nz+t3++uci/T4nhkRERERERETGfGk3aJH1L0gOSbutr21jStZJuqqs5blG3ryHpybr9Jkmn9D3nZ5J+1bfvpXX7NpJulDRD0tubPJaIiIiIiBg+M/HAbqU0vU7bt4F/Br7T13YC8Fnb/yFp13p7u3rfb2xvPIfX2tf2DSPa7gEOBD42RvFGREREREQMlUaTNttTJK0xspmq2AhUJfrvfxGvfzeApJkL+xoRERERETF+daFGR9M9baP5MHCxpBOphme+vm/fmpKmAY8Bx9jur7F6hqTngB8BX0hp/4iIiIiI6IIShUg+AHzE9qrAR4DT6/Y/AKvZ3gQ4EviepF6P3L62XwO8ob7tv6D/U0mH1HPobpg5c/qLPoiIiIiIiCivC3PaSiRtBwDn1ffPBbYAsP207T/X96cCvwHWqbfvq38+Dnyv95wFYftU25vZ3mzChKVf9EFEREREREQMQomk7X5g2/r+G4E7ASStKGlifX8tYG3gLkmLSFqhbl8UeAtw2wteNSIiIiIiOsf2wG6lNDqnTdI5VJUhV5B0L3As8H7gJEmLAE8Bh9QP3wb4nKQZwHPAobYfkrQ01Ry4RYGJwH8C36xff3PgfGB5YDdJn7X96iaPKSIiIiIiYpDUxXoeiyz2iu4ddERERETEPMx45j6VjmFBrTR5/YGd2//hkduL/PuUGB4ZERERERER86nRpE3StyQ9IOm2vraNJV0r6aa6muMWdftiks6QdKukmyVtV7cvJenfJd0h6ReSju97rSMl3S7pFkmXSVq9yeOJiIiIiIjh4gH+V0rTPW3fBnYe0XYC8FnbGwN/X29DNdeNurT/jsCXJfXiO9H2esAmwN9I2qVunwZsZntD4F/7XisiIiIiIqIVGk3abE8BHhrZDPTWX1uOqpokwPrAZfXzHgAeoUrInrB9ed3+DHAjsEq9fbntJ+rnX9trj4iIiIiIbuhC9cgSc9o+DPw/Sb8HTgSOrttvBvaoS/yvCbwWWLX/iZImA7tRJ3cjvBf4j4ZijoiIiIiIKKLRkv9z8AHgI7Z/JGlv4HTgTcC3gL8GbgB+B1wNzOg9qV4i4Bzgq7bv6n9BSfsBmzFr/bcXkHQI9fICmrgcWWA7IiIiIiLGg8ZL/ktaA/iJ7Q3q7UeBybYtScCjtieN8ryrgffZvr3e/hbwF9uHj3jcm4CTgW3rYZXzlJL/EREREREvNB5L/q+43LoDO7f/06O/6kzJ//uZ1SP2RuBOeL5K5NL1/R2BGX0J2xeo5r99uP+FJG0CfAPYfX4TtoiIiIiIiPGk0eGRks4BtgNWkHQvcCxVlciT6uGOT1EPWQReClwsaSZwH7B//RqrAJ8C7gBurDrn+GfbpwH/D1gGOLduv8f27k0eU0REREREDI+SBUIGpfHhkcMowyMjIiIiIl5oPA6PXGHSOgM7t3/wsf8p8u9TohBJRERERETEmJjZgU6oEnPaIiIiIiIiYj41mrRJ+pakByTd1te2kaRrJN0q6UJJk+r2HSVNrdunSnpj33PeKekWSb+QdEJf+6H142+S9N+S1m/yeCIiIiIiYrhkce0X79vAziPaTgOOsv0a4Hzg43X7g8BudfsBwFkAkv6KquDIDrZfDbxM0g71c75n+zW2NwZOAL7S4LFEREREREQMXKNz2mxPqddp67cuMKW+fylwMfBp29P6HvMLYAlJiwNrAf9j+0/1vv8E9gIus/1Y33OWBto/oDWi9uT9V5YOYaCWXPkNpUOIiIiIITSzAylAiUIktwG7Az8G3gGsOspj9gKm2X5a0q+B9erk715gT2Cx3gMlfRA4sm574wtfKqKdksREREREdEOJQiQHAx+UNBVYFnimf6ekVwNfAv4WwPbDwAeAHwBXAncDM3qPt/01268EPgEcM6f/qaRDJN0g6YaZM6eP6QFFREREREQZXZjT1vg6bXUP2U9sbzDKvnWAs21vUW+vAvwXcJDtq+bweocAr7L9dyPaJwAP215uXjFlnbaIiIiIiBcaj+u0TVp6rYGd2z82/a5urNMm6aW2H6iTrGOAU+r2ycC/A0ePTNj6nrM8cBiwd92+tu0764f9H+BOIiIiIiKiM7qwTlujSZukc4DtgBUk3QscCyxTz0MDOA84o77/IeBVwKclfbpu28n2A8BJkjaq2z5n+396z5H0JuBZ4GGqqpMRERERERGt0fjwyGGU4ZERERERES80HodHLr3UGgM7t5/+xN1F/n1KFCKJiIiIiIiI+dRo0ibpW5IekHRbX9tGkq6RdKukCyVN6tu3Yb3vF/X+Jer2xSSdKul/JN0haa8R/5+3S7KkzZo8noiIiIiIiEFruqft28DOI9pOA46y/RrgfODjAJIWAc4GDrX9aqq5cM/Wz/kU8IDtdYD1gSt6LyZpWeBw4LrGjiIiIiIiIobSTHtgt1IaTdpsTwEeGtG8LjClvn8p1ULaADsBt9i+uX7un20/V+87GDiubp9p+8G+1/s8cALw1NgfQURERERERFkl5rTdBuxe338HsGp9fx3Aki6WdKOkv4PnlwIA+Hzdfq6kl9X7NgFWtf2TwYUfERERERHDoguLa5dI2g4GPihpKrAs8EzdvgiwNbBv/fOtknao21cBrrK9KXANcGK9zts/Ah+dn/+ppEMk3SDphpkzp4/pAUVERERERDRl4Emb7Tts72T7tcA5wG/qXfcCV9h+0PYTwEXApsCfgSeo5r8BnFu3LwtsAPxM0t3AVsAFcypGYvtU25vZ3mzChKUbOrqIiIiIiBgkD/C/UgaetEl6af1zAnAMcEq962JgQ0lL1UVJtgVud9UPeSFVYRKAHer2R22vYHsN22sA1wK7275hcEcTERERERHRrEWafHFJ51AlWytIuhc4FlhG0gfrh5wHnAFg+2FJXwGuBwxcZPvf68d9AjhL0j8BfwIOajLuiIiIiIgYH0rONRsUdeEgR1pksVd076AjYtx48v4rWXLlN5QOY2C6dLxdOlbo3vF2SZfe2y4dK8CMZ+5T6RgW1GKLrzKwc/tnnr63yL9PkraIiIiIiADGZ9K26ADP7Z+dj38fSTsDJwETgdNsHz9iv+r9u1LV7jjQ9o1ze80S1SMjIiIiIiJaR9JE4GvALsD6wD6S1h/xsF2AtevbIcDX5/W6SdoiIiIiImLc8gBv82EL4Ne277L9DPB9YI8Rj9kD+I4r1wKTJa00txdN0hYRERERETE2XgH8vm/73rptQR8zm0arRw6rUmN1JR1i+9QS/+9B69KxQreOt0vHCt063i4dK3TreLt0rNCt4+3SsUK3jrdLx/piDfLcXtIhVEMae04d8T6NFsvITrr5ecxs0tM2WIfM+yGt0aVjhW4db5eOFbp1vF06VujW8XbpWKFbx9ulY4VuHW+XjnXcsH2q7c36biMT63uBVfu2VwHuX4jHzCZJW0RERERExNi4Hlhb0pqSFgPeBVww4jEXAO9RZSvgUdt/mNuLdnJ4ZERERERExFizPUPSh4CLqUr+f8v2LyQdWu8/BbiIqtz/r6lK/h80r9dN0jZYXRqX3KVjhW4db5eOFbp1vF06VujW8XbpWKFbx9ulY4VuHW+XjrVVbF9ElZj1t53Sd9/ABxfkNTu5uHZERERERMR4kTltERERERERQyxJW0RERERExBBL0hYRcyVpgqTXl44jImJ+SFq6dAwREWMtc9oaIulk5rJInu3DBxhOoyRtOrf9tm8cVCyDJGlrYG3bZ0haEVjG9m9Lx9UESdfYfl3pOAZF0hHAGcDjwGnAJsBRti8pGtgYkvS2ue23fd6gYhkkSesAXwdeZnsDSRsCu9v+QuHQGiHpHcBPbT8u6RhgU+ALbfxcri8unUb1WbyapI2Av7V9WOHQGiNpSWA1278qHUuTJF1me4d5tbVJl84xYv6kp605NwBTgSWoviTvrG8bA8+VC6sRX65vXwOuo6p29M36/lcLxtUYSccCnwCOrpsWBc4uF1HjLpG0lySVDmRADrb9GLATsCJVKd7jy4Y05narb+8FTgf2rW+nAfsVjKtp36T6u30WwPYtVGvotNWn64Rta+DNwJlUSWsb/SPVMf4ZwPbNwDZFI2qQpN2Am4Cf1tsbSxq5FtS4JmkJSS8BVpC0vKSX1Lc1gJULh9eYDp5jxHxIyf+G2D4TQNKBwPa2n623TwFac7UewPb2AJK+Dxxi+9Z6ewPgYyVja9BbqXpfbgSwfb+kZcuG1KgjgaWB5yQ9CYiqYu2ksmE1ppec7gqcYfvmtiWstg8CkPQTYP3eop6SVqK6ANNWS9n++Yi3c0apYAagd5Hw/wBft/1jSZ8pGE+jbP9+xHvbtouk/T4DbAH8DMD2TXUy0yZ/C3yYKkGbyqzP5sdo9+dU184xYj4kaWveysCywEP19jK09+rQer2EDcD2bZI2LhhPk56xbUmG9s+hsN21L4upki4B1gSOrr8sZxaOqSlr9BK22v8C65QKZgAelPRK6uHrkt4O/GHuTxnX7pP0DeBNwJckLU57R9n8vh4iaUmLAYcDvywcU5Nm2H60ZdeTZmP7JOAkSf/X9sml4xmgTp1jxPxJ0ta844Fpki6vt7elujrWRr+UdBpVF76phli19Qvzh/WJ0GRJ7wcOphpW1kp1L9O+wJq2Py9pVWAl2z8vHFpT3ks1lPku209I+iuqIZJt9DNJFwPnUP3dvgu4fO5PGdc+SDWEez1J9wG/pd3DQfcGdgZOtP1I3ZP68cIxNeVQ4CTgFcC9VKNaFmjx2nHmNknvBiZKWpsqSb26cExN+aOkZbswN7PWqXOMmD8pRDIAkl4ObFlvXmf7jyXjaYqkJYAPMGsOwRSq4ThPlYuqOZJ2pJrzJOBi25cWDqkxkr5O1dP0Rtt/LWl54BLbmxcOrRGSRp0HY3vKoGMZhLooyRvqzSm2zy8ZzyDUV64n2H68dCxNqnsV77X9tKTtgA2B79h+pGRcTZC0qu3fj2h7eYu/c5cCPkX1PQRwMVUi07rvXEm32N6wnpt5HHAi8EnbW87jqeNWl84xYv4kaWuIpPVs3zGnyootvjrUCZK+ZPsT82prC0k32t5U0jTbm9RtN9veqHRsTZB0Yd/mElTzRqbafmOhkGKM1MMD9wLWoG+0ie3PlYqpSZJuAjajOt6LgQuAdW3vWjCsRkiaAZxLVUjoybrtRttzrXAcw6/33SPpOOBW29/r/z5qm66dY8T8aeu49mFwZP3zy6PcTiwVVBMk/bD+eaukW0beSsfXkB1Hadtl4FEMzrOSJjJrHtCKtHeOF7Z367vtCGxANderdSS9TdKdkh6V9JikxyU9VjquBv0Y2IOq+Mj0vltbzbQ9A3gb8E+2PwKsVDimptwKXAn8d93DCLMKV7SOpEslTe7bXr4e6txGvbmZewMXtXxuJnTvHCPmQ+a0NcT2IfXP7ef2OEk7tqDL+4j651uKRjEAkj4AHAasNSIhXRa4qkxUA/FV4HzgpZL+AXg7cEzZkAbqXqrErY1OAHaz3db5pyOtYnvn0kEM0LOS9gHeQ7XEA1Tlw9vItv9F0s3AhZI+wVzWS22BFfqHudp+WNJLC8bTpE7MzezwOUbMhwyPLKxLQzfUggWaJS0HLE81pv6ovl2P235o9Ge1g6T1gB2orlxf1uaTfEknM+tkbwJVUZK7bbeuYIWkq2z/Tek4BkXSqcDJ/ZVu20zS+lQFOq6xfY6kNYF32m7buoPPD6Gr768E/ADYzPZSZSNrhqSpwFtt31Nvrw6c39ZzClWLpffm3l5Zr8PXKl0+x4h5S9JWWJvHZI/UxmOtr2ou0dvufXm2hapFTeeorV8ikg7o25xBlbC18iqnpJOAlwP/Bjzda7d9XqmYmiTpduBVVFUjn2bWmoMbFg0sXjRJK/UvXyFpEeD1LS4gtDNVJdQr6qZtqNZKbd0QSUlHAO8Hep9LbwVObfsyAG0/x4gFk+GR5XUpa27NsUraDfgK1Zp7DwCrUy1v8OqScTVgKtX7JmA14OH6/mTgHqp1zNpocr0+0PMkHTGyrSUmAU8wqwIdVO95K5M2OjYvpC4FfxywPrOf/K1VLKgxJmk/22cD+8xhzbJWJm22f1oXO9uK6nP5I7YfLBxWU94LbGl7OlRFOYBrgFYmbR06x4gFkKQtYuF8geqL8j/rilbbA/sUjmnM2V4TQNIpwAW2L6q3d6FarLetDqBa76nfgaO0jXu227r+3GwkTbL9GNDqEv+jOAM4FvhHYHuq9QbbVpyjt/DwskWjGJBRqlPfX/9cTdJqLa1OLeC5vu3naN/vcb9OnGPEgknS1jBJi9t+ei5tdw8+qmLa9AH7rO0/S5ogaYLty+srf221ue1Dexu2/0PS50sG1IS6YMO7gTUlXdC3a1ngz2Wiala9vuJ7qa7g9vfEHFwsqGZ8j6pYUn/vcY+B1vQ8jbCk7cskyfbvgM9IupIqkWsF29+of362dCwDciRwCFU16pEMtHFpkjOA6yT11pDcEzi9XDiN69o5RsyHJG3NuwYYOSn4+Tbbbxt4RA2oy8FfbHtuvS/7DyqeAXhE0jJUw26+K+kBqrlPbfWgpGOAs6lOCvajnUnM1cAfgBWY/YTocaCty1ecBdwBvBn4HLAv1TCcVrH9lvpnW4f0zslTkiYAd0r6EHAf0MoKg5JOoOqheBL4KbAR8OF66GRr2D6kfk+Paetc25Fsf0XSz4CtqS64HGR7WtmoGtW1c4yYDylE0hBJLwdeQXWS+25mXdWdBJxie71SsTWl7pnY3/ajpWNpmqSlgaeo3td9geWA79puYyLTK0hyLNVEd1N9kXyurYVIuqRv0dpbbG8oaVGqCzCtulrfN5RsVC0dUoakzamS8MnA56k+q06wfW3JuJog6SbbG0t6K1VPzEeAy21vVDayZrShIvP8krQV8Avbj9fbywLr276ubGTN6No5Rsyf9LQ1581Uc2BWobpi30vaHgM+WSimpj0F3CrpUvoWq7V9eLmQmtGbDF07s1ggA1D3on61jeXu56Q+QTgZ+GtgMWAiMN32pKKBNePZ+ucjkjYA/gisUS6cxow2lKynrUPKsH19ffcvVPPZ2qy3/tyuwDm2H5pDYZK2uETSXsB5bv8V+K8z+6il6aO0tUaXzjFi/iVpa4jtM4EzJe1l+0el4xmQf69vrSXpceZSBbONJ/W2n5O0oqTFbD9TOp4B+WfgXcC5wGZUCxO/qmhEzTlV0vJUi6VfACwDfLpsSGPP9vbz8zhJO9q+tOl4mibpQub+WbX7AMMZlAsl3UE1PPIwSStSXUxsqyOpirA8J+lJZi1f0brvIaqRYc//PtueWS/p0CpdPMeI+de6X/gh9FpJl9l+BKA+Ofqo7WPKhjX2bJ8paUlgNdu/Kh1PE2wvCyDpc1Q9Emcxa/hCmyuX3Q1cVQ+B7e9F/UqxiBpm+9eSJtp+DjhD0tWlYxpr9byYx2w/TDXkta3FOBbEl4Bxn7QBJ5YOYNBsH1UXa3isvtj0BLBHb39bEvKe3vdRR9wl6XCq3jWAw4C7CsbTiA6fY8R8yJy2ho22oLSkG223rku/XlfkRGAx22tK2phq3lPrruhKus72lvNqawtJo1aaa2u1NklTqJY0OI3qi/MPwIFtnBsjaYrtbUrHMSxG+8wez+q5MU/anllvTwQWt/1E2cgGr43fvZLeRlWcw8CVtv+tbETNqBeZ/irVMGYDl1EVmXmgaGAN6do5Rsyf9LQ1b2J/if+6J2rxwjE15TPAFsDPAGzfJKmtldqek7Qv8H2qL5B9mH0NmVbpJWf15G/b/kvhkJq2PzAB+BBVMYNVgVZUeh3FpZI+BvyA2XtRu1pkpm1XMi+jugDR+5tdErgEeH2xiMpp1QQ3Sf9CNWz7nLrp0Lo38YMFw2pEnZy9a077JR1t+7gBhtS0Tp1jxPxJ0ta8s4HLJJ1B9Yd3MO2dVDrD9qMjJn637QSo591UCy2fRHWMV9VtrVQXqDgLeEm9/SDwHtu/KBpYc/a0fRLVfJhewnoELVxcm+ozyVTDjfplqGQ7LNF/kcX2XyQtVTKggtr2fbQtsEFvrpekM4Fby4ZUzDuANiVtnTrHiPkzoXQAbWf7BOAfqKrQvRr4fN3WRrdJejdV7+Lakk6mWveqdWzfbXsP2yvYXtH2nrbv7u2XdHTB8JpwKnCk7dVtrw58FPhm4ZiadMAobQcOOogBWR/4GnAzcBNV1cxXlwyoSZJeMNJhRNvdg4tmIKb3L3cg6bVUhTpi/PsVsFrf9qq0dz3JeWlVL2oHzzFiPmROW4yZ+urtp4CdqD5AL6ZKUttcvWtUbZs7IenmkfO5Rmsb7yTtQ3U1c2vgyr5dk6h6kue2ePy4JOmHVEuRfLdu2geYbHvvclE1Z7S/zbb9vfar12n7PnB/3bQS8E7bU8tF1Yz+qQijtUk6z3ZrhjlLugLYHPh53bQ5cA3wBLS2Quio2vw3PJquHW9UMjyyYSPKty5GtY5MK9d7qie2f6q+dV2rrvpRVe76NNUQSYD9gN8WjKcpV1MVHVmB2df1epz2XsFed0Tyfbmkm4tF0xBJLwdeASwpaRNm/Y1OAlo7XND29ZLWA9alOuY7bPfW5mtbRcVreOG6Xc+3tSlhq/196QCGSNu+c+ela8cbJGlr3MiSvJL2pCrW0TqSNqNaOHwN+n63bG9YKqaC2taFfTDV3K7z6u0ptHChXtu/A34n6U3UFfckrQOsR3vnikyTtJXtawEkbUk1f6Jt3kw1xHUVqoS8d9LzGNXnVmvVSdptc9g97pc46HBCfsXc9ku6xvbrBhVPYeeWDmDA2naOEfMhwyMLkHSt7a1KxzHWJP0K+DjVye3MXnt9ItwpbSsbPi+STrb9f0vHMVYkTQXeACwPXAvcADxhe9+igY0hSbdSffEvStULc0+9vTpwu+0NCobXGEl72f5R6TiGRRs+qyQdQJWQbwZcz+wJ+Zm2z5vDU1utJe/tycx9senDBxjO0GjDexsLLj1tDavXUOmZQPWl0tZM+U+2LygdxJDo2lW/vykdwBiT7SckvRc42fYJkqaVDmqMvaV0AIW8VtJlth8BkLQ88FHbx5QNq5hx/31k+0zgzCTkLzDu31uqC2ZQfcesT7U0CVTVIls3L3MBdO0cI0jSNgi79d2fQVWZbI8yoTTuWEmnUa0L9Pxk8DZe5ZS0IvB+XjgU9OD65xfLRBZjRJJeB+wLvLdua9XnZRd7wGu72H5+OKTthyXtCnQ1aWuTJOQtUyfkSDoQ2L43H1PSKVTrDbbK/PYs5hyjm1p1EjKMbLdu3s9cHEQ192dRZg2PNLPmQbXJj6mqC/4nWfCyjY4AjgbOt/0LSWsBlxeOKcbGxBEVBZcEXrAMQFvMq6Ii7VriIAn57NpUrGJlYFngoXp7mbqtbdKzGHOUpK0hHR2HvZHt15QOYkCWsv2J0kEMkTadHGB7ClWxld72XcDzf7Ntm8PXMWcDl0k6g+oz+mDgzLIhNapLFRW7lpB/aeT30Ii2/QuE1ZTjqYom9S6ebQt8plw4zehaz2IsmCRtzeni1ZJrJa1v+/bSgQzATyTtavui0oEMkqSlbU8fZddJAw+mrLbN4euMen7ircAOVBcbPm/74sJhjbmOVlTsWkK+IzDy4uEuvTbbc6oYOq5ImkC1kPiW9Q3gKNt/LBdV47rSsxgLINUjG1ZfFdqp72rJosAltrcvG9nYk/RL4JVU63c9TXWS4DaW/K/X31saeAborXnkNq6/ByDp9cBpwDK2V5O0EfC3tg8rHFoRWdg0hl1XKypK2oVZCfklLU3IPwAcBqwF/KZv17LAVbb3KxJYgzq2fAGSDqLqSZytZ7HXExfdlKStYXUZ/NfZfqjeXh641va6ZSMbe5JWH629wwUPWkPSdcDbgQt6ZYYl3dbWsvDzkqRt/KovuPS++BajmoM7vcUXXFJRsWUkLUe1HMlxwFF9ux7vnWu0jaTPArcA57kjJ651b3mvZ/G6lvcsxnzI8MjmdWIcNlTJWd0D84a66UrbN5eMqUmSdge2qTd/ZvsnJeNpmu3fS7NNXetyAZZWzeHrEtvL9m9L2hPYokw0A9GZiopdSchtPwo8KukY4I+2n5a0HbChpO/03uuWOZJqdMsMSU8xayRPq95bSSMvBv6+/rmypJVt3zjomGJ4pKdtAPqulhj4eVuvlkg6gqoMfm/YzVuBU22fXC6qZkg6Htgc+G7dtA8w1fZRc37W+CXpX4GvAP8MbEVVlGMz2+8qGlgDJE0Ejrf98bk85kDb3x5cVNEkSdfa3qp0HE0YbRHervQU9xLy/oqSbSLpJqrhr2sAFwMXAOva3rVgWPEi9F3gH41tv3FgwcTQSdI2ACN6ZK6wfWHJeJoi6RaqoaDT6+2lgWtaOqftFmBj2zPr7YnAtDYeK4CkFaiKjbyJeq4IcITtPxcNrCGS/gvYoSvDcLpEUn+1xAlUJ73btnW+TP1ZtfmIioo32H512cgGo+UJ+Y22N5X0d8CTtk8eLUlvi7qXeG1giV5bXek3ohMyPLJho/TIHC7p9baPLhhWU8TsQ+aeo93DyCYzq7LTcgXjaJztB6kWmu6KacCPJZ0LPF8ts63FGzpmt777M6jWKdujTCgD0ZmKinNIyNt84eVZSfsA72HW7/WiBeNpjKT3Ua2fuQpwE9WIj2uAVvY81UXrPkDfFAzgG72idtFN6WlrWJd6ZCQdCRwAnF837UlVpewfiwXVkPqL8niqyk6i+mA92vb3iwbWEEkrUg19XYO+iz22Dy4VU5PqE9yR3NbjjXbrQkVFeMHfbS8h/6btB8pE1CxJ6wOHUo1oOUfSmsA7bR9fOLQxVy/TsTlVIbeNJa0HfNb2OwuH1ghJp1El4L0LLPsDz9l+X7moorQkbQ2rk7bt+qpHvoSqaEXrkjZ4fhLt1lQnB1NsTyscUmMkrUT1JSJaXtlJ0tXAlVRrDD7fm5qqdDFeSDqZufS62D58TvsioixJ19vevJ7Ht2VdfOUm2xsXDq0Rkm62vdG82qJbMjyyeV9kVvXI53tkyobUDEln2d4fuHGUtlaQtJ7tO/oqPN1b/2x7ZaelbI9cxLW1JK0DfB14me0NJG0I7G77C4VDi4V3Q/3zb4D1gR/U2++guhjRSl2oqNi1hFzSD23vXfc+veC4W3pR+F5Jk4F/Ay6V9DBwf9GImvWcpFfa/g2ApLXodsXmID1tjZI0gWptqyvpQI/MyIpk9VDQW22vXzCsMSXpVNuHzKHCU2srO0n6AnC17YtKxzIIkq4APk41h6Dz69K1Sf23u1Nvbkg9d+QS29uXjWww2lhRsV5IHOaQkNv+SJHAGiJpJdt/6OraqJK2pZpH/lPbz5SOpwmSdgDOAO6iOndcHTjI9tyqS0bLJWlrmKQptreZ9yPHL0lHA58ElgSe6DUDz1CV/G9dz6KkJWw/Na+2tqiv1i8NPA08S0vXyOnpG4ozrS9pa+1QnC6R9CuqKre9IevLU82TWbdsZIPT1oqKXU/I26ieUjJHbuli4gCSFgfWpfq+vaNXATa6K8Mjm3eppI9RXfnrr0LXmg8a28cBx0k6ro0J2hxcDYxc52i0tlbwiAWJO+BBSa+kHnok6e3AH8qGFGPkeGYNWQfYFvhMuXCa1bGKiisDyzKrqu8ydVurjBjyOtsu2ncxbSrVsQpYDXi4vj8ZuAdYs1hkDZL0DqqexFvqRdSPlfSFFk/BiPmQpK15B1N94Bw2on2tArE07SeSlrY9XdJ+VAnMSW0aqlEvlP4KYElJmzBrSYNJwFLFAmvIKHP4ZtPiL5APAqcC60m6D/gtsF/ZkGIs2D5D0n8AW1J9Nh/V1iHrtS4tcdCJhLxLF9Fsrwkg6RTggt4Q/boi6ptKxtawT9s+V9LWwJuBE6nmWW9ZNqwoKcMjG1YvZHoYVUVFU81vO8X2k0UDa0BdKXMjYEPgLOB04G22ty0a2Biq504cSHW1+npmJW2PUS1v0Kp1vCR90/b7uzaHr6deIH6C7cdLxxJjR9LuzFr/6ArbF5aMJ8ZOfWGtl5D/vOUJeWdImmr7tSPabrC9WamYmtQbmi/pOKraAN9r88LpMX+StDVM0g+pTuh7i2vvA0y2vXe5qJrRK0Qi6e+B+2yfPrI4SVtI2ivl7tunXmtwjmx/ZVCxRDMkHU9VGKr/M/mGtg3t7lpFxZ4k5O0k6WKqi95nU/1e7wdsY/vNRQNriKSfAPdR9Sa+FniS6iJESv53WIZHNm/dEX9kl0u6uVg0zXq8LkqyH7BNXT1y0cIxNeW1ki6z/Qg8X8zgo7aPKRvW2BoxH+YF2tazSDUfBqrJ35sDF9TbuwFTikQUY21XYGPbMwEknQlMo31LsXRuiYNREvLDJb2+bQl5R+0DHAucX29Pqdvaam9gZ+BE24/U68J+vLdT0vK2Hy4WXRSRnraGSfo21XDIa+vtLYEDbI+c4zbu1cNS3g1cb/tKSatRLSz+ncKhjbnRhim0sVdR0hlz2W3bBw8smAGSdAmwV29YpKRlgXNt71w2snix6mHc2/VVj3wJ8LOWrm3VqYqK9Xvbn5BPBKa19b2N7mrj+UbMW3ramrcl8B5J99TbqwG/7C2K2aYvk3ruwFf6tu8BWpew1SZKWrxXgreeu7h44ZjGnO2DSsdQyGpUS1b0PAOsUSaUGGNfZFaxClENpWtzT0wnKir2mcysY12uYBwxhiStA3yM6nP4+XPXts+rngvN+yHRNknamteZK/MjyhAvRjU08i+22/jFeTZwWd0TZaoqoWeWDak59Xoxe/HCL8zPlYqpYWcBP5d0PtX7+1Za/P52haQJwExgK6phdAI+0fJiFZ2oqFjrWkLeJecCpwCnAc8VjmUYZJhcB2V4ZDRG0p7AFrY/WTqWJtQlh3egOjm4xPbFhUNqjKSfAo9SzYV5/gvT9peLBdUQSQJWAVYE3lA3T7E9rVxUMVYkTbG9zbwf2R5dqKhYJ+RvpypW0UvIr2vjsXbRaNUjuyzDI7spSVs0StK1trcqHUe8OJJus71B6TgGJScI7SXp01SV2H4ATO+19+a4tVFXKip2MSHvCkmfAR6gKkTydK+9zX+3c5Py/92UpC3GzIhKgxOo1jLb1vbrCoXUGElbAScDf001FHQiMN32pKKBNUTSqcDJtm8tHcsgSPoa8G3b15eOJcaWpN8yytAi22sVCKdxXVniALqZkHdF/Xc7klv8d3uW7f3n1CbpJfm97p4kbTFmRlQanAHcDZxq+09lImqOpBuAd1GNs98MeA/wKtufKhpYQyTdDqwN3EV1lVO0rJBOv/p416X6HZ5Oy4+3S+qiQYcBW1Mlb1dSVfh9smhgDelSRcWuJeTRXiOHP9Z/t7faXr9gWFFYCpHEWJoAHDFi7bIvUxXpaB3bv5Y00fZzwBmSri4dU4N2AZanb44X8EixaJrXtePtkjOBx4Cv1tv71G17F4uoeZPpRkXF9RklIS8aUYwZSRtQvcdL9NratqRQvdbtJ4ElJT3Wa6aqYHxqscBiKCRpi7G0YS9hA7D9sKS2jrl+QtJiwE2STgD+ACxdOKYm7Qm8DziP6gvkLOCbVENE22hPunW8XbKu7Y36ti+XdHOxaJrXpYqKXUzIO0HSscB2VEnbRVQX1v6bli0rZPs44DhJx7VxCHO8OBkeGWOmPvHZzvbD9fZLqCa9v6ZsZGNP0urA/1LNZ/sI1dXrf7H966KBNaQeYvU629Pr7aWBa9o4xAq6d7xdIunbVMMhr623twQOsH1Y0cAa0LWKipJuHpGQj9oW40+9tu1GVEN7N5L0MuA027sVDq0Rkv4GuMn2dEn7AZsCJ9n+XeHQoqD0tMVY+jJwtaR/pRqasjfwD2VDakbfB+dTkr4KrNrWhK0mZl8b5znavbhn1463S7YE3iPpnnp7NeCX9Ulhq+Yt2p4p6UO2fwhcUDqeAZgmaasRCflVhWOKsfFk/fs8Q9IkqkqSbZ6r+HVgI0kbAX8HnE7Vq7ht0aiiqCRtMWZsf6cu0PFGqhPct9m+vXBYjZD0M2B3qr+hm4A/SbrC9pEl42rQGcB19WLTUA0fPL1cOI3r2vF2yc6lAxiwSyV9jG5UVOxMQt5BN0iaTDVMfSrwF+DnRSNq1gzblrQHVQ/b6ZIOKB1UlJXhkRELobdGiqT3UfWyHSvpljafFEjalGqCv+jAYtNdO95opy5VVKyHrc9Rhpa1g6Q1gEm2bykdS1MkXQH8FDiIah7qn6iGS7ZuuknMvyRtEQuhvnK7E9Uk90/Zvr7tSVtEjD9dW+Ig2knSZbZ3mFdbW0h6OfBu4HrbV0pajapmQKsKr8SCyfDIiIXzOeBi4L/rhG0t4M7CMUVEjJSKijFuSVoCWApYoV5GqDe3eBKwcrHAGlSvyXa27Tf12mzfQ8sqZcaCS09bRERES6WiYoxnko4APkyVoN1HlbQZeBw41fbXykXXHEkXAPvbfrR0LDE80tMWsRAkrQi8H1iDvr8j261cSDwixq1UVIxxy/ZJwEmS/h74J9uPSfo0VQn8a8pG16ingFslXcrsBYQOLxdSlJaetoiFIOlqqrkhU+krDW/7R8WCiogYQdIvgXWB2SoqAjNJRcUYJ3pzxiVtTbVg/JeBT9resnBojZhTpUjbZw46lhgeSdoiFoKkm2xvXDqOiIi5SUXFaIO+is3HAbfa/l6vrXRsEYOS4ZERC+cnkna1fVHpQCIi5iRJWbTEfZK+AbwJ+JKkxYEJhWMac5J+aHvv3tqCI/enZ7zb0tMWsRAkPQ4sDTwNPEs9Odr2pKKBRUREtIykpYCdqXrZ7pS0EvAa25cUDm1MSVrZ9v1z6iHPRZhuS9IWEREREVGYpBttbyrpLNv7l44nhkuGR0YspHrNmLWBJXpttqeUiygiIiLGscXqIiSvl/S2kTttn1cgphgSSdoiFoKk9wFHAKsANwFbUZUffmPBsCIiImL8OhTYF5gM7DZin4EkbR2W4ZERC6GeJLw5cK3tjSWtB3zW9jsLhxYRERHjmKT32j59Lvt3tH3pIGOK8lpXeSdiQJ6y/RSApMVt30G1FlJERETEQptbwlb70kACiaGS4ZERC+deSZOBfwMulfQwcH/RiCIiIqILVDqAGLwMj4x4kSRtCywH/NT2M6XjiYiIiPbqVZksHUcMVnraIhaQpAnALbY3ALB9ReGQIiIiIqLFMqctYgHZngncLGm10rFEREREu0hafB5tdw8umhgWGR4ZsRAk/RdV9cifA9N77bZ3LxZUREREjHujDX/MkMjI8MiIhbMM8Ja+bZFqThEREbGQJL0ceAWwpKRNmFVwZBKwVLHAYigkaYtYOIuMnMsmaclSwURERMS492bgQGAV4MvMStoeAz5ZKKYYEhkeGbEAJH0AOAxYC/hN365lgats71cksIiIiGgFSXvZ/lHpOGK4JGmLWACSlgOWB44Djurb9bjth8pEFREREW0h6YvACbYfqbeXBz5q+5iigUVRSdoiIiIiIoaEpGm2NxnRlkIkHZeS/xERERERw2Nif4n/es78C5YBiG5JIZKIiIiIiOFxNnCZpDMAAwcDZ5YNKUrL8MiIiIiIiCEiaRdgB6oKkpfYvrhwSFFYkraIiIiIiIghluGRERERERFDQtLjVMMiARYDFgWm255ULqooLUlbRERERMSQsL1s/7akPYEtykQTwyLDIyMiIiIihpika21vVTqOKCc9bRERERERQ0LS2/o2JwCbMWu4ZHRUkraIiIiIiOGxW9/9GcDdwB5lQolhkeGRERERERERQyw9bRERERERhUk6mbkMg7R9+ADDiSEzoXQAERERERHBDcBUYAlgU+DO+rYx8Fy5sGIYZHhkRERERMSQkHQ5sJPtZ+vtRYFLbG9fNrIoKT1tERERERHDY2Wgf622Zeq26LDMaYuIiIiIGB7HA9PqHjeAbYHPlAsnhkGGR0ZEREREDBFJLwe2pCpM8nPbfywcUhSWnraIiIiIiOGyBfCG+r6BCwvGEkMgPW0REREREUNC0vHA5sB366Z9gBtsH10uqigtSVtERERExJCQdAuwse2Z9fZEYJrtDctGFiWlemRERERExHCZ3Hd/uVJBxPDInLaIiIiIiOHxRWZVjxSwDZChkR2XpC0iIiIiYghImgDMBLaimtcm4BOpHhmZ0xYRERERMSQkTbG9Tek4YrgkaYuIiIiIGBKSPg08CfwAmN5rt/1QsaCiuCRtERERERFDQtJvqdZmm43ttQqEE0MiSVtERERExJCQtCRwGLA1VfJ2JXCK7SeLBhZFJWmLiIiIiBgSkn4IPMbsi2tPtr13uaiitCRtERERERFDQtLNtjeaV1t0SxbXjoiIiIgYHtMkbdXbkLQlcFXBeGIIpKctIiIiImJISPolsC5wT920GvBLqvXbbHvDUrFFOUnaIiIiIiKGhKTV57bf9u8GFUsMjyRtERERERERQyxz2iIiIiIiIoZYkraIiIiIiIghlqQtIiIiIiJiiCVpi4iIiIiIGGJJ2iIiIiIiIobY/wd896Sz3PHJzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "sns.heatmap(transactions.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions = transactions.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions[\"profit\"] = transactions[\"list_price\"] - transactions[\"standard_cost\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions = transactions.drop([\"list_price\",\"standard_cost\",\"order_status\",\"product_first_sold_date\"], axis = 1 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking if online orders are more pfrofitabe than offline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_order</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>5387899.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>5340916.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 profit\n",
       "online_order           \n",
       "0.00         5387899.64\n",
       "1.00         5340916.69"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking if online orders are more pfrofitabe than offline\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "transactions[[\"online_order\",\"profit\"]].groupby(\"online_order\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Profit')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "transactions[[\"product_line\",\"profit\"]].groupby(\"product_line\").sum().sort_values(\"profit\").plot(kind = \"bar\",figsize = (10,6))\n",
    "plt.xlabel(\"Product Line\")\n",
    "plt.ylabel(\"Profit\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking what brand is most profitable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking what brand is most profitable\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "plt.figure(figsize=(10,8))\n",
    "_=sns.barplot(x=\"brand\",y=\"profit\", data = transactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "demographics = pd.read_excel(\"KPMG_VI.xlsx\",sheet_name=\"CustomerDemographic\", index_col = 0,header=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>default</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>\"'</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eli</td>\n",
       "      <td>Bockman</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>1980-12-16</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>&lt;script&gt;alert('hi')&lt;/script&gt;</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arlin</td>\n",
       "      <td>Dearle</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>1954-01-20</td>\n",
       "      <td>Recruiting Manager</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>2018-02-01 00:00:00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Talbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1961-10-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>() { _; } &gt;_[$($())] { touch /tmp/blns.shellsh...</td>\n",
       "      <td>No</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sheila-kathryn</td>\n",
       "      <td>Calton</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1977-05-13</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>NIL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 first_name  last_name  gender  \\\n",
       "customer_id                                      \n",
       "1                   Laraine  Medendorp       F   \n",
       "2                       Eli    Bockman    Male   \n",
       "3                     Arlin     Dearle    Male   \n",
       "4                    Talbot        NaN    Male   \n",
       "5            Sheila-kathryn     Calton  Female   \n",
       "\n",
       "             past_3_years_bike_related_purchases        DOB  \\\n",
       "customer_id                                                   \n",
       "1                                             93 1953-10-12   \n",
       "2                                             81 1980-12-16   \n",
       "3                                             61 1954-01-20   \n",
       "4                                             33 1961-10-03   \n",
       "5                                             56 1977-05-13   \n",
       "\n",
       "                          job_title job_industry_category     wealth_segment  \\\n",
       "customer_id                                                                    \n",
       "1               Executive Secretary                Health      Mass Customer   \n",
       "2            Administrative Officer    Financial Services      Mass Customer   \n",
       "3                Recruiting Manager              Property      Mass Customer   \n",
       "4                               NaN                    IT      Mass Customer   \n",
       "5                     Senior Editor                   NaN  Affluent Customer   \n",
       "\n",
       "            deceased_indicator  \\\n",
       "customer_id                      \n",
       "1                            N   \n",
       "2                            N   \n",
       "3                            N   \n",
       "4                            N   \n",
       "5                            N   \n",
       "\n",
       "                                                       default owns_car  \\\n",
       "customer_id                                                               \n",
       "1                                                           \"'      Yes   \n",
       "2                                 <script>alert('hi')</script>      Yes   \n",
       "3                                          2018-02-01 00:00:00      Yes   \n",
       "4            () { _; } >_[$($())] { touch /tmp/blns.shellsh...       No   \n",
       "5                                                          NIL      Yes   \n",
       "\n",
       "             tenure  \n",
       "customer_id          \n",
       "1             11.00  \n",
       "2             16.00  \n",
       "3             15.00  \n",
       "4              7.00  \n",
       "5              8.00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demographics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "address =  pd.read_excel(\"KPMG_VI.xlsx\",sheet_name=\"CustomerAddress\", index_col = 0,header=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "demographics = demographics.drop([\"DOB\",\"first_name\",\"last_name\",\"past_3_years_bike_related_purchases\",\"job_title\",\"default\",\"tenure\"\t],axis = 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_demo = pd.merge(transactions,demographics,on = \"customer_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>profit</th>\n",
       "      <th>gender</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19411</th>\n",
       "      <td>6</td>\n",
       "      <td>2764</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>high</td>\n",
       "      <td>medium</td>\n",
       "      <td>91.15</td>\n",
       "      <td>Female</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1 Wayridge Park</td>\n",
       "      <td>4301</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19412</th>\n",
       "      <td>45</td>\n",
       "      <td>3168</td>\n",
       "      <td>2017-02-21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>356.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>Health</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>88 Rutledge Crossing</td>\n",
       "      <td>4215</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19413</th>\n",
       "      <td>83</td>\n",
       "      <td>3168</td>\n",
       "      <td>2017-04-11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Touring</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1408.91</td>\n",
       "      <td>Male</td>\n",
       "      <td>Health</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>88 Rutledge Crossing</td>\n",
       "      <td>4215</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19414</th>\n",
       "      <td>3</td>\n",
       "      <td>3168</td>\n",
       "      <td>2017-06-03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1702.55</td>\n",
       "      <td>Male</td>\n",
       "      <td>Health</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>88 Rutledge Crossing</td>\n",
       "      <td>4215</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19415</th>\n",
       "      <td>3</td>\n",
       "      <td>3168</td>\n",
       "      <td>2017-09-30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1702.55</td>\n",
       "      <td>Male</td>\n",
       "      <td>Health</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>88 Rutledge Crossing</td>\n",
       "      <td>4215</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_id  customer_id transaction_date  online_order          brand  \\\n",
       "19411           6         2764       2017-07-03          0.00     OHM Cycles   \n",
       "19412          45         3168       2017-02-21          1.00          Solex   \n",
       "19413          83         3168       2017-04-11          0.00          Solex   \n",
       "19414           3         3168       2017-06-03          0.00  Trek Bicycles   \n",
       "19415           3         3168       2017-09-30          0.00  Trek Bicycles   \n",
       "\n",
       "      product_line product_class product_size  profit  gender  \\\n",
       "19411     Standard          high       medium   91.15  Female   \n",
       "19412     Standard        medium       medium  356.50    Male   \n",
       "19413      Touring        medium        large 1408.91    Male   \n",
       "19414     Standard        medium        large 1702.55    Male   \n",
       "19415     Standard        medium        large 1702.55    Male   \n",
       "\n",
       "      job_industry_category     wealth_segment deceased_indicator owns_car  \\\n",
       "19411    Financial Services      Mass Customer                  N      Yes   \n",
       "19412                Health  Affluent Customer                  N       No   \n",
       "19413                Health  Affluent Customer                  N       No   \n",
       "19414                Health  Affluent Customer                  N       No   \n",
       "19415                Health  Affluent Customer                  N       No   \n",
       "\n",
       "                    address  postcode state    country  property_valuation  \n",
       "19411       1 Wayridge Park      4301   QLD  Australia                   1  \n",
       "19412  88 Rutledge Crossing      4215   QLD  Australia                   7  \n",
       "19413  88 Rutledge Crossing      4215   QLD  Australia                   7  \n",
       "19414  88 Rutledge Crossing      4215   QLD  Australia                   7  \n",
       "19415  88 Rutledge Crossing      4215   QLD  Australia                   7  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = pd.merge(by_demo,address,on = \"customer_id\")\n",
    "final.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "final[\"state\"] = final[\"state\"].replace({'New South Wales':\"NSW\",\"Queensland\":\"QLD\",\"Victoria\":\"VIC\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = final.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding most profitable states and putting them on a map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_state= final[[\"profit\",\"state\",]].groupby(\"state\",as_index = False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>state</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NSW</td>\n",
       "      <td>4762838.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>QLD</td>\n",
       "      <td>1956714.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VIC</td>\n",
       "      <td>2260306.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state     profit\n",
       "0   NSW 4762838.61\n",
       "1   QLD 1956714.42\n",
       "2   VIC 2260306.37"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_state.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "import folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "m = folium.Map([-26.4390917,133.281323])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to use folium we need longitude and latitute of the states "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "long = {145.612793:\"NSW\",142.702789:\"QLD\",144.964600:\"VIC\"}\n",
    "lat = {-31.840233:\"NSW\",-20.917574:\"QLD\",-37.020100:\"VIC\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_state[\"long\"] = long\n",
    "by_state[\"lat\"] = lat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "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>state</th>\n",
       "      <th>profit</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NSW</td>\n",
       "      <td>4762838.61</td>\n",
       "      <td>145.61</td>\n",
       "      <td>-31.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>QLD</td>\n",
       "      <td>1956714.42</td>\n",
       "      <td>142.70</td>\n",
       "      <td>-20.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VIC</td>\n",
       "      <td>2260306.37</td>\n",
       "      <td>144.96</td>\n",
       "      <td>-37.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state     profit   long    lat\n",
       "0   NSW 4762838.61 145.61 -31.84\n",
       "1   QLD 1956714.42 142.70 -20.92\n",
       "2   VIC 2260306.37 144.96 -37.02"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in by_state.iterrows():\n",
    "  \n",
    "    \n",
    "    folium.Circle(location=[row['lat'], row['long']],\n",
    "        popup= 'Profit:' +str(row['profit']),\n",
    "        tooltip=row['state'],\n",
    "        color=\"darkred\",\n",
    "        fill=True,\n",
    "        radius = row[\"profit\"]/40,\n",
    "        fill_color=\"lighred\").add_to(m)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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