Datenanalyse Teen Tech Expo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "099d3993",
   "metadata": {},
   "source": [
    "# Data Analysis from the Teen Tech Expo\n",
    "\n",
    "**File name = Robotik_Expo.csv**\n",
    "\n",
    "This document shows our results from the Teen Tech Expo. We gathered Data from Questionaires and used it to find flaws and also the strongpoints of our Expo. The Goal is to understand our Visitors, and then make the next Expos better.\n",
    "\n",
    "Hier werden die Daten die wir gesammelt Analysiert mit einer Linear Regression Funktion. Der Ziel it Aussgen zu Treffen, wo wir mit den Alter der Kinder sagen können ob die Ausstellung, oder das Austellungsstück empfehlenswert ist oder nicht. Dadurch erleichtert es die Planung der Expo, da man gezielte Ausstellungsstücke haben kann, und die Quote der zufriedenen Schülern steigt\n",
    "\n",
    "**This part of the Document first displays the Data that we collected in diffent forms. We first displayed the raw data in a table form, and then also used a scatter plot to show the spread of the data**\n",
    "\n",
    "**Der obere teil ist nur eine darstellung des Datasets. Weiter unten wird Die lineare Redression durchgeführt. Linear Regeression würde benutz**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "210b3635",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import all the required modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "672a1bee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bring the data into dataframe\n",
    "df = pd.read_csv(\"Robotik_Expo4.csv\",sep=';', header=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d183025f",
   "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>Alter</th>\n",
       "      <th>Frage_1</th>\n",
       "      <th>Frage_2</th>\n",
       "      <th>Frage_3</th>\n",
       "      <th>Frage_4</th>\n",
       "      <th>Sastisfaction_Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Alter  Frage_1  Frage_2  Frage_3  Frage_4  Sastisfaction_Rate\n",
       "0     16        5        5        5        5                 100\n",
       "1     13        5        5        5        5                 100\n",
       "2     15        5        5        5        5                 100\n",
       "3     19        5        5        5        5                 100\n",
       "4     14        5        5        5        5                 100\n",
       "5     17        5        5        5        5                 100\n",
       "6     14        5        5        5        5                 100"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show the first seven rows of data\n",
    "df.iloc[0:7]\n",
    "#df['Frage_4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "203d3f80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Frage_1\n",
      "0        5\n",
      "1        5\n",
      "2        5\n",
      "3        5\n",
      "4        5\n",
      "5        5\n",
      "6        5\n"
     ]
    }
   ],
   "source": [
    "# read first 7 rows of Frage_1 \n",
    "x = df.iloc[:, [True, False, False, False, False, False]]\n",
    "y = df.iloc[:, [False, True, False, False, False, False]]\n",
    "x_new = x.head(7)\n",
    "y_new = y.head(7)\n",
    "\n",
    "\n",
    "print (y_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fdfe28c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ich hatte Spaß an der Ausstellung')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Question 1\n",
    "x = df.iloc[:, [False, True, False, False, False, False]]\n",
    "y = df.iloc[:, [True, False, False, False, False, False]]\n",
    "\n",
    "# Use a scatter plot for plotting a graph of TV vs Sales\n",
    "plt.scatter(x, y)\n",
    "\n",
    "# Add axis labels for clarity (x : Frage 1, y : Alter)\n",
    "plt.xlabel(\"Frage 1 Scale: 1(stimme nicht zu) - 5(stimme zu)\")\n",
    "plt.ylabel(\"Alter\")\n",
    "\n",
    "# Add plot title \n",
    "plt.title(\"Ich hatte Spaß an der Ausstellung\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "529d497b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ich habe was neues gelernt an der Ausstellung')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Question 2\n",
    "x = df.iloc[:, [False, False, True, False, False, False]]\n",
    "y = df.iloc[:, [True, False, False, False, False, False]]\n",
    "\n",
    "# Use a scatter plot for plotting a graph of TV vs Sales\n",
    "plt.scatter(x, y)\n",
    "\n",
    "# Add axis labels for clarity (x : Frage 2, y : Alter)\n",
    "plt.xlabel(\"Frage 2 Scale: 1(stimme nicht zu) - 5(stimme zu)\")\n",
    "plt.ylabel(\"Alter\")\n",
    "\n",
    "# Add plot title \n",
    "plt.title(\"Ich habe was neues gelernt an der Ausstellung\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b074e7cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ich würde nochmal zur Ausstellung kommen')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Question 3\n",
    "x = df.iloc[:, [False, False, False, True, False, False]]\n",
    "y = df.iloc[:, [True, False, False, False, False, False]]\n",
    "\n",
    "# Use a scatter plot for plotting a graph of TV vs Sales\n",
    "plt.scatter(x, y)\n",
    "\n",
    "# Add axis labels for clarity (x : Frage 3, y : Alter)\n",
    "plt.xlabel(\"Frage 2 Scale: 1(stimme nicht zu) - 5(stimme zu)\")\n",
    "plt.ylabel(\"Alter\")\n",
    "\n",
    "# Add plot title \n",
    "plt.title(\"Ich würde nochmal zur Ausstellung kommen\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "38866ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Die Ausstellung hat meine Interesse an Robotik erweckt')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cAMzMSsoJwMyspJwAzMxKygnAzKyknADMzErKCcDMrKScAMzMSsoJwMyspJwAzMxKqrAXwkhaDBwHbIiIg3PZoaTXQO4EPAC8NSJ+U1QMZdO1urfwV8hZ85VxPnucixnnIo8AlgDHVJV9CvhIRBwKfDh32wToWt3L/GU99Pb1E0BvXz/zl/XQtbq32aHZBCrjfPY4FzfOhSWAiFgBbKouBnbLn3cH1hXVftksXL6G/s0DQ8r6Nw+wcPmaJkVkRSjjfPY4J0WMc2GngOp4F7Bc0qdJyedf6g0o6TTgNID99tuvIcFNZuv6+sdUbpNTGeezx3nk8vFq9EXgtwDvjoh9gXcD59UbMCLOjYjOiOjs6OhoWICT1fSp7WMqt8mpjPPZ4zxy+Xg1OgG8AViWP18IHN7g9lvWvDmzaJ/SNqSsfUob8+bMalJEVoQyzmePc1LEODf6FNA64DnAZcDRwB8b3H7LGrw7oGx3SpRNGeezx7m4cVZETGiFWyqWlgKzgWnAeuBMYA3wBVLi+QfpNtBVI9XV2dkZ3d3dhcRpZtaqJK2KiM56/Qs7AoiIuXV6Pa2oNs3MbPT8S2Azs5JyAjAzKyknADOzknICMDMrKScAM7OScgIwMyspJwAzs5JyAjAzKyknADOzknICMDMrKScAM7OScgIwMyspJwAzs5JyAjAzKyknADOzknICMDMrqcJeCCNpMXAcsCEiDs5l3wMGX2o5FeiLiEOLaL9rdW+pXiEHHueyjHMZlXE+n7ToSq5Yu2lL91EH7MkFpx45oW0UeQSwBDimsiAiXh0Rh+aN/g956AXxE6prdS/zl/XQ29dPAL19/cxf1kPX6t4imtsmeJzLMc5lVMb5XL3xB7hi7SZOWnTlhLZTWAKIiBXAplr9JAl4FbC0iLYXLl9D/+aBIWX9mwdYuHxNEc1tEzzOSauPcxmVcT5Xb/xHKh+vZl0DeBawPiL+WG8ASadJ6pbUvXHjxjFVvq6vf0zlrcDjPHK5TU6ez8VpVgKYywh7/xFxbkR0RkRnR0fHmCqfPrV9TOWtwOM8crlNTp7PxWl4ApC0PXAi8L2i2pg3ZxbtU9qGlLVPaWPenFl1vjH5eZyTVh/nMirjfD7qgD3HVD5ezTgCeD5wY0TcXlQDJxw2g7NPPIQZU9sRMGNqO2efeEhL3zXgcS7HOJdRGefzBace+bCNfRF3ASkiJrTCLRVLS4HZwDRgPXBmRJwnaQmwMiK+Ntq6Ojs7o7u7u5A4zcxalaRVEdFZr39hvwOIiLl1yk8pqk0zMxs9/xLYzKyknADMzErKCcDMrKScAMzMSsoJwMyspJwAzMxKygnAzKyknADMzErKCcDMrKScAMzMSsoJwMyspJwAzMxKygnAzKyknADMzErKCcDMrKQKex+ApMXAccCGiDi4ovwdwNuBB4CfRsT7i4qhbLpW97Jw+RrW9fUzfWo78+bMaum3JpXVSYuu5Iq1m7Z0F/GmqG1NGZftF3z2Mv644b4t3Y/faxcuec/sCW2jyCOAJcAxlQWSngu8FHhyRBwEfLrA9kula3Uv85f10NvXTwC9ff3MX9ZD1+reZodmE6h64w9wxdpNnLToyiZFVLwyLtvVG3+AP264jxd89rIJbaewBBARK4BNVcVvAT4ZEffnYTYU1X7ZLFy+hv7NA0PK+jcPsHD5miZFZEWo3viPVN4KyrhsV2/8Ryofr0ZfA3gC8CxJV0m6XNLT6w0o6TRJ3ZK6N27c2MAQJ6d1ff1jKjebLLxsF6fRCWB7YA/gGcA84PuSVGvAiDg3IjojorOjo6ORMU5K06e2j6ncbLLwsl2cRieA24FlkfwGeBCY1uAYWtK8ObNon9I2pKx9Shvz5sxqUkRWhKMO2HNM5a2gjMv24/faZUzl49XoBNAFHA0g6QnADsBdDY6hJZ1w2AzOPvEQZkxtR8CMqe2cfeIhLX+nRNlccOqRD9vYt/pdQGVcti95z+yHbeyLuAtIETGhFW6pWFoKzCbt4a8HzgS+DSwGDgX+CbwvIn45Ul2dnZ3R3d1dSJxmZq1K0qqI6KzXv7DfAUTE3Dq9Ti6qTTMzGz3/EtjMrKScAMzMSsoJwMyspJwAzMxKygnAzKyknADMzErKCcDMrKScAMzMSsoJwMyspJwAzMxKatgEIKlN0i8aFYyZmTXOsAkgIgaAv0vavUHxmJlZg4zmYXD/AHokXQJseR9ZRLyzsKjMzKxwo0kAP81/ZmbWQkZMABHxTUntwH4R0bpvYTYzK5kR7wKS9BLgauDnuftQST8qOC4zMyvYaE4BnQUcDlwGEBFXS3rMSF+StBg4DtgQEQfnsrOAU4GNebAPRsT/jDnqUeha3cvC5WtY19fP9KntzJszq6VfIQewoKuHpVfdxkAEbRJzj9iXj59wSLPDMttqXp+LWZ9HkwAeiIi/SqosG817JJcAXwa+VVX+uYj49OjCG5+u1b3MX9ZD/+YBAHr7+pm/rAegZReaBV09nL/y1i3dAxFbup0EbDLz+lzc+jyaH4JdJ+m1QJukx0v6EvDrkb4UESuATVsb4HgsXL5my8IyqH/zAAuXt+4ljKVX3TamcrPJwuvzyOXjNZoE8A7gIOB+4DvAX4F/24o23y7pWkmLJe1RbyBJp0nqltS9cePGeoPVtK6vf0zlrWAgah+U1Ss3myy8Po9cPl6jSQAvjogPRcTT898C4PhxtvdV4ADgUOAO4DP1BoyIcyOiMyI6Ozo6xtTI9KntYypvBW1DT9GNWG42WXh9Hrl8vEaTAOaPsmxEEbE+IgYi4kFgEeni8oSbN2cW7VPahpS1T2lj3pxZRTS3TZh7xL5jKjebLLw+j1w+XnUvAkt6EXAsMEPSFyt67QY8MJ7GJO0dEXfkzpcB142nnpEMXhgq010DgxeGfBeQtRqvz8Wtz4o655QkPQU4DPgI8OGKXvcAl0bE3cNWLC0FZgPTgPXAmbn7UNJdRDcDb65ICHV1dnZGd3f3SIOZmVkFSasiorNe/7pHABFxDXCNpPMjYsx7/BExt0bxeWOtx8zMijHcKaAe8v3+eviFh4iIpxQYl5mZFWy4H4IdV6NMwD7AB4sJx8zMGmW4U0C3DH6WdCjwWuBVwJ+BHxYemZmZFWq4U0BPAF4DzAX+AnyPdNH4uQ2KzczMCjTcKaAbgf8DXhIRfwKQ9O6GRGVmZoUb7odgLwfuBC6VtEjS80jXAMzMrAXUTQARcVFEvBp4IulR0O8GHiXpq5Je2KD4zMysICM+CiIi7ouICyLiONIdQFcDHyg6MDMzK9ZongW0RURsiohzIuLoogIyM7PGGFMCMDOz1uEEYGZWUk4AZmYl5QRgZlZSTgBmZiXlBGBmVlJOAGZmJTXcs4C2iqTFpEdKb4iIg6v6vQ9YCHRExF1FxWDWirpW95bq9YhlddKiK7li7aYt3UcdsCcXnHrkhLZR5BHAEuCY6kJJ+wIvAG4tsG2zltS1upf5y3ro7esngN6+fuYv66FrdW+zQ7MJVL3xB7hi7SZOWnTlhLZTWAKIiBXAphq9Pge8n/y2MTMbvYXL19C/eWBIWf/mARYuX9OkiKwI1Rv/kcrHq6HXACQdD/Tm9w2PNOxpkroldW/cuLEB0Zlt+9b19Y+p3Gw4DUsAknYGPgR8eDTDR8S5EdEZEZ0dHR3FBmc2SUyf2j6mcrPhNPII4ADgMcA1km4mPVn0d5Ie3cAYzCa1eXNm0T6lbUhZ+5Q25s2Z1aSIrAhHHbDnmMrHq2EJICJ6ImKviJgZETOB24GnRsSdjYrBbLI74bAZnH3iIcyY2o6AGVPbOfvEQ3wXUIu54NQjH7axL+IuIEUUcy1W0lJgNjANWA+cGRHnVfS/GegczW2gnZ2d0d3dXUicZmatStKqiOis17+w3wFExNwR+s8sqm0zMxuZfwlsZlZSTgBmZiXlBGBmVlJOAGZmJeUEYGZWUk4AZmYl5QRgZlZSTgBmZiXlBGBmVlJOAGZmJeUEYGZWUk4AZmYl5QRgZlZSTgBmZiXlBGBmVlKFvQ9A0mLgOGBDRBycyz4GvBR4ENgAnBIR64qKwVpf1+peFi5fw7q+fqZPbWfenFl+O5a1hJMWXckVazdt6S7ijWBFHgEsAY6pKlsYEU+OiEOBnzDKF8Sb1dK1upf5y3ro7esngN6+fuYv66FrdW+zQzPbKtUbf4Ar1m7ipEVXTmg7hSWAiFgBbKoq+1tF5y5AMe+jtFJYuHwN/ZsHhpT1bx5g4fI1TYrIbGJUb/xHKh+vwk4B1SPpE8Drgb8Czx1muNOA0wD222+/xgRnk8q6vv4xlZvZUA2/CBwRH4qIfYELgLcPM9y5EdEZEZ0dHR2NC9AmjelT28dUbmZDNfMuoO8AL29i+zbJzZszi/YpbUPK2qe0MW/OrCZFZDYxjjpgzzGVj1dDE4Ckx1d0Hg/c2Mj2rbWccNgMzj7xEGZMbUfAjKntnH3iIb4LyCa9C0498mEb+yLuAlJEMddhJS0FZgPTgPXAmcCxwCzSbaC3AKdHxIi3bHR2dkZ3d3chcZqZtSpJqyKis17/wi4CR8TcGsXnFdWemZmNjX8JbGZWUk4AZmYl5QRgZlZSTgBmZiXlBGBmVlJOAGZmJeUEYGZWUk4AZmYl5QRgZlZSTgBmZiXlBGBmVlJOAGZmJeUEYGZWUk4AZmYl5QRgZlZSTgBmZiVV2AthJC0GjgM2RMTBuWwh8BLgn8Ba4I0R0VdUDGatqGt1LwuXr2FdXz/Tp7Yzb86sln8Npse5mHEu8ghgCXBMVdklwMER8WTgD8D8Ats3azldq3uZv6yH3r5+Aujt62f+sh66Vo/4ZtVJy+Nc3DgXlgAiYgWwqars4oh4IHeuBPYpqn2zVrRw+Rr6Nw8MKevfPMDC5WuaFFHxPM5JEePczGsAbwJ+Vq+npNMkdUvq3rhxYwPDMtt2revrH1N5K/A4j1w+Xk1JAJI+BDwAXFBvmIg4NyI6I6Kzo6OjccGZbcOmT20fU3kr8DiPXD5eDU8Akt5Aujh8UkREo9s3m8zmzZlF+5S2IWXtU9qYN2dWkyIqnsc5KWKcC7sLqBZJxwBnAM+JiL83sm2zVjB4F0iZ7ojxOBc3zipqJ1zSUmA2MA1YD5xJuutnR+AvebCVEXH6SHV1dnZGd3d3IXGambUqSasiorNe/8KOACJibo3i84pqz8zMxsa/BDYzKyknADOzknICMDMrKScAM7OScgIwMyspJwAzs5JyAjAzKyknADOzknICMDMrKScAM7OScgIwMyspJwAzs5JyAjAzKyknADOzknICMDMrKScAM7OSKuyFMJIWk979uyEiDs5lrwTOAp4EHB4Rhb3ma0FXD0uvuo2BCNok5h6xLx8/4ZCimrMm6VrdW6pXBUI5l+0yzudGKPIIYAlwTFXZdcCJwIoC22VBVw/nr7yVgfy6y4EIzl95Kwu6eops1hqsa3Uv85f10NvXTwC9ff3MX9ZD1+reZodWmDIu22Wcz41SWAKIiBXApqqyGyJiTVFtDlp61W1jKrfJaeHyNfRvHhhS1r95gIXLC1/EmqaMy3YZ53OjbLPXACSdJqlbUvfGjRvH9N2BOi+6r1duk9O6vv4xlbeCMi7bZZzPjbLNJoCIODciOiOis6OjY0zfbZPGVG6T0/Sp7WMqbwVlXLbLOJ8bZZtNAFtj7hH7jqncJqd5c2bRPqVtSFn7lDbmzZnVpIiKV8Zlu4zzuVEKuwuomQbviCjbnRJlM3gXSJnuDinjsl3G+dwoioLOHUpaCswGpgHrgTNJF4W/BHQAfcDVETFnpLo6Ozuju7uwO0bNzFqSpFUR0Vmvf2FHABExt06vi4pq08zMRq8lrwGYmdnInADMzErKCcDMrKScAMzMSqqwu4AmkqSNwC3j/Po04K4JDGcy8DiXg8e5HLZmnPePiLq/pJ0UCWBrSOoe7jaoVuRxLgePczkUOc4+BWRmVlJOAGZmJVWGBHBuswNoAo9zOXicy6GwcW75awBmZlZbGY4AzMysBicAM7OSaukEIOkYSWsk/UnSB5odT9EkLZa0QdJ1zY6lESTtK+lSSTdI+r2kf2t2TEWTtJOk30i6Jo/zR5odU6NIapO0WtJPmh1LI0i6WVKPpKslFfI45Ja9BiCpDfgD8ALgduC3wNyIuL6pgRVI0rOBe4FvRcTBzY6naJL2BvaOiN9J2hVYBZzQ4vNYwC4Rca+kKcCvgH+LiJVNDq1wkt4DdAK7RcRxzY6naJJuBjojorAfvrXyEcDhwJ8i4qaI+CfwXeClTY6pUBGxgvTOhVKIiDsi4nf58z3ADUBLvyUkkntz55T815p7cRUk7QO8GPh6s2NpJa2cAGYAt1V0306LbxzKTNJM4DDgqiaHUrh8KuRqYANwSUS0/DgDnwfeDzzY5DgaKYCLJa2SdFoRDbRyAqj1luyW31MqI0mPAH4IvCsi/tbseIoWEQMRcSiwD3C4pJY+3SfpOGBDRKxqdiwNdlREPBV4EfC2fIp3QrVyArgdqHxT9j7AuibFYgXJ58F/CFwQEcuaHU8jRUQfcBlwTHMjKdxRwPH5nPh3gaMlnd/ckIoXEevy/w2kNykePtFttHIC+C3weEmPkbQD8BrgR02OySZQviB6HnBDRHy22fE0gqQOSVPz53bg+cCNTQ2qYBExPyL2iYiZpPX4lxFxcpPDKpSkXfKNDUjaBXghMOF397VsAoiIB4C3A8tJFwe/HxG/b25UxZK0FLgSmCXpdkn/r9kxFewo4HWkPcKr89+xzQ6qYHsDl0q6lrSTc0lElOK2yJJ5FPArSdcAvwF+GhE/n+hGWvY2UDMzG17LHgGYmdnwnADMzErKCcDMrKScAMzMSsoJwMyspJwAtiGSBipuZ7w6P96gke3vJqlX0pfr9H+GpKtybDdIOmuc7VwmaVwvuZb0RElXSrpf0vuq+rVLujw/CHCs9d4sadp4Yhpl/WdVx1vR795a5aOoc4mkV9QonynptRXdh0haMo76T5G0sWJ5/NeKfnuP96mc4x3fcbRzgqQDK7o/LenoRrQ9WWzf7ABsiP78E/+HyT96UkQU+SyUjwGXD9P/m8CrIuKavJGdVWAs9WwC3gmcUKPfm4BlETFQRMOSts+/L9nWzQReC3wHICJ6JO0jab+IuHWMdX0vIt5eo/w9wKKtC7O+CZrWJwA/AQafDvslUsy/3Mp6W4aPALZheU/uBklfAX4H7Cvpq5K6q58FL+lYSTdK+pWkLw7uneVfFC6W9Nv8LPWaT0SV9DTSj08uHiakvYA7YMvzaK7P332EpG/kZ5dfK+nlubxmrFXtvjDv0f9O0oX5uT51RcSGiPgtsLlG75OA/8717i1pRd5zvU7Ss0bR3jtyeY+kJ+bhz5J0rqSLgW9VxV6vjWNyPddI+t+KrxyYj35ukvTOOtNjXp5X11bN39fnsmskfbviK8+W9Otc5+DRwCeBZ+W43p3Lfkz6Fe1EeTnw8xzbQUrvKLg6x/j4XH5yRfk5lUdmkj6Rx2WlpEflsiWSPivpUuA/q6bLzpK+n+v/ntKRaGfud2/FcK/I9fwLcDywMLd/QETcAjxS0qMncDpMbhHhv23kDxgArs5/F5H25B4EnlExzJ75fxvpOTBPBnYiPfn0MbnfUuAn+fN/ACfnz1NJ70jYpard7XJd+wKnAF+uE9+HgbtzbG8Gdsrl/wl8vmK4PerFmrsvIz3XfRqwYjAe4Azgw/nzR4Hjh5lWZwHvq+jeAbizovu9wIcq2t91hPZuBt6RP78V+HpFO6uA9hox1Gqjo2pe7FlRz6+BHXMcfwGm5H735v8vJL0AXHme/AR4NnAQsAaYVlXnEuDCPOyBpMefA8wenP8VsR4F/HiMy+MppIR/LfADYN9c/hhgVcVwXwJOqpgP7cCTSElncBy/Arw+fw7gJfnzp4AFFePzE6CtRizvA87Jnw8GHiA9K3/L9MufXwEsqajvFVX1LAJe3ux1fVv58ymgbcuQU0BK1wBuiaEv+3iV0qNhtyc9FuBA0gbgpoj4cx5mKTD4+NgXkh6kNXj+eSdgP9LjMQa9FfifiLgtnWmqLSI+KumCXOdrgbmkjc3zqdi7jIi7h4n12ooqn5HLrsjt7kB6lAUR8eG6gdQ2Deir6P4tsFjpYXFdEXG1pOfUay8bfJjcKuDEivIfRUR/jTZrtTEbWDE4LyKi8v0MP42I+4H7JW0gHXHdXtH/hflvde5+BPB44CnADyK/GKSqzq5IpwWvH9yTrmMDMH2Y/rX8GFgaEfdLOp10CvBo0rzcWDHclcCHlJ7Zvywi/ijpecDTgN/mad2eYwD4J2lDD2lav6Cirguj9im8ZwJfAIiI65QehTEe45kOLcsJYNt33+AHSY8h7Qk9PSLuVrqwtxO1H3295WukPZ41wwxzJOmUwVtJG50dJN0bEQ97jWZErAW+KmkRsFHSI3MbQ54pMkys1bFdEhFzh4lttPor64+IFUqPz30x8G1JC0lHL8O1d3/+P8DQdeO+GsPWa6OP+o8dv7/ic3UbkKbH2RFxzpDCdLpoNHUOtxzsRJpGQxuUvkF6j8K6iBjyHKWI+EtF5yIeOi1TPa2/I+kq0nRYrnSxWMA3I2J+jVg2R94dZ5TTmuHHrXLaVC9j1WpOh7LyNYDJZTfSCvLXvLf3olx+I/BYPXTX0KsrvrOcdG5bAJIOq640Ik6KiP0iPW3xfaRXSj5s4y/pxYP1kPZMB0gbvItJD94bHG6PYWKttBI4StLj8vd2lvSEkSZCLfmoo03STrmu/UnPkF9EemLoUyeyvWHauBJ4Tk6ASNpzDFUuB940eF1C0gxJewH/SzqaeuQo67yHdDqq0hOo8TTJiHhjRBxavfHP7exd0Xk8Dx01/oF0enJwuMeSjkC/SHri7pNzzK/I8SNpzzy9xutXwKtyXQcCh1T0Wy/pSZK2A15WUT7q6VBWPgKYRCLdfbMa+D1wE3BFLu/Pe+8/l3QX6emBgz5GepvStXnjfTMw3vepvg74nKS/k87BnhQRA5I+DvyX0svoB4CPRMSyWrFWjc9GSacASyXtmIsXAH+Q9FGgOyKGPMI7X8DrJiWYByW9Czgw0otgLiadKvgF6dTUPEmbSe9Jfv1w7Y1zetRr4zRgWd4gbWDoKY66IuJiSU8Crsx59l7S9ZvfS/oEcLmkAdIpolOGqepa4AGlJ0kuiYjPAc8FfjrG8XunpONJ83rTYJsRcZ+ktZIeFxF/Iu1wnJynw53ARyNik6QFpDdabUe6aP824JYxxjDoK8A386mf1Xkc/5r7fYB0Suk20sZ98ML+d4FF+QjqFcCtwONIy4/hp4G2DEmPiPSicAH/Bfwxr/ilkY9u3hMRr2t2LNuSnOwuB54ZE3Qbq6SXAU+LiAUTUd8o2msjXVD+h6QDSEcYT4j0vu/R1vEy4KkR8e9FxTnZ+AigdZwq6Q2kC5urgXNGGL7lRMRqSZdKaqtzIbGs9gM+MFEbf4CIuGjwlFSD7Ex6D8IU0vWAt4xl459tD3xmwiObxHwEYGZWUr4IbGZWUk4AZmYl5QRgZlZSTgBmZiXlBGBmVlL/H3apCLNnyWb5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Question 4\n",
    "x = df.iloc[:, [False, False, False, False, True, False]]\n",
    "y = df.iloc[:, [True, False, False, False, False, False]]\n",
    "\n",
    "# Use a scatter plot for plotting a graph of TV vs Sales\n",
    "plt.scatter(x, y)\n",
    "\n",
    "# Add axis labels for clarity (x : Frage 4, y : Alter)\n",
    "plt.xlabel(\"Frage 4 Scale: 1(sehr schlecht) - 5(sehr gut)\")\n",
    "plt.ylabel(\"Alter\")\n",
    "\n",
    "# Add plot title \n",
    "plt.title(\"Die Ausstellung hat meine Interesse an Robotik erweckt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e33b9986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Satisfaction Rate')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Satisfaction rate\n",
    "x = df.iloc[:, [False, False, False, False, False, True]]\n",
    "y = df.iloc[:, [True, False, False, False, False, False]]\n",
    "\n",
    "# Use a scatter plot for plotting a graph of TV vs Sales\n",
    "plt.scatter(x, y)\n",
    "\n",
    "# Add axis labels for clarity (x : Satisfaction rating, y : Alter)\n",
    "plt.xlabel(\"Satisfaction Rating\")\n",
    "plt.ylabel(\"Alter\")\n",
    "\n",
    "# Add plot title \n",
    "plt.title(\"Satisfaction Rate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b812c82",
   "metadata": {},
   "source": [
    "# Making a Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c5ee5469",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from pandas.core.common import random_state\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce2c5677",
   "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>Alter</th>\n",
       "      <th>Frage_1</th>\n",
       "      <th>Frage_2</th>\n",
       "      <th>Frage_3</th>\n",
       "      <th>Frage_4</th>\n",
       "      <th>Sastisfaction_Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.043478</td>\n",
       "      <td>4.673913</td>\n",
       "      <td>4.065217</td>\n",
       "      <td>4.826087</td>\n",
       "      <td>3.369565</td>\n",
       "      <td>84.565217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.421721</td>\n",
       "      <td>0.634362</td>\n",
       "      <td>1.143137</td>\n",
       "      <td>0.437384</td>\n",
       "      <td>1.704356</td>\n",
       "      <td>16.958318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Alter    Frage_1    Frage_2    Frage_3    Frage_4  \\\n",
       "count  46.000000  46.000000  46.000000  46.000000  46.000000   \n",
       "mean   15.043478   4.673913   4.065217   4.826087   3.369565   \n",
       "std     2.421721   0.634362   1.143137   0.437384   1.704356   \n",
       "min    11.000000   3.000000   1.000000   3.000000   0.000000   \n",
       "25%    13.000000   5.000000   4.000000   5.000000   2.250000   \n",
       "50%    15.500000   5.000000   4.000000   5.000000   4.000000   \n",
       "75%    17.000000   5.000000   5.000000   5.000000   5.000000   \n",
       "max    19.000000   5.000000   5.000000   5.000000   5.000000   \n",
       "\n",
       "       Sastisfaction_Rate  \n",
       "count           46.000000  \n",
       "mean            84.565217  \n",
       "std             16.958318  \n",
       "min             30.000000  \n",
       "25%             70.000000  \n",
       "50%             85.000000  \n",
       "75%            100.000000  \n",
       "max            100.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describing the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "75ca1983",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#making a distribution plot of Frage 1\n",
    "plt.title('Frage 1 Plot')\n",
    "sns.distplot(df['Frage_1'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c6f3c387",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relationship between Alter and Frage 1\n",
    "plt.scatter(df['Alter'], df['Frage_1'], color = 'lightcoral')\n",
    "plt.title('Frage 1')\n",
    "plt.xlabel('Alter')\n",
    "plt.ylabel('Feedback Werte')\n",
    "plt.box(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "afe50a6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting variables\n",
    "X = df.iloc[:,:1]\n",
    "y = df.iloc[:,1:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e305332d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Assigning test, and Train data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "#print(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "912ee5a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Linear Regression Model\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2baf2fd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction result\n",
    "y_pred_test = regressor.predict(X_test)     # predicted value of y_test\n",
    "y_pred_train = regressor.predict(X_train)   # predicted value of y_train\n",
    "\n",
    "#print(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8c1944a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prediction on training set\n",
    "plt.scatter(X_train, y_train, color = 'lightcoral')\n",
    "plt.plot(X_train, y_pred_train, color = 'firebrick')\n",
    "plt.title('Alter vs Frage_1 (Training Set)(Ich hatte Spaß an der Ausstellung)')\n",
    "plt.xlabel('Alter')\n",
    "plt.ylabel('Feedback Werte')\n",
    "plt.legend(['X_train/Pred(y_test)', 'X_train/y_train'], title = 'Sal/Exp', loc='best', facecolor='white')\n",
    "plt.box(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e035b638",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#making a distribution plot of Frage 2\n",
    "plt.title('Frage 2 Plot')\n",
    "sns.distplot(df['Frage_2'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2a20879d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hYWEhnj59qhJnnTp11EpE79y5I9zd3ZX7kFwuF23bthU//vijMmkRQoiwsDABQCxZskSl/YMHD4RcLhdTpkxRlnXu3Fm4uLjkus7g4OBs+9fIkSOFXC5XeU+FyDkR/eyzz4SxsXGeiU/m56BTp04q5Tt37hQAsiXHWWVuk0WLFqmUjxo1SpiZmWWLM1PmPrJ582ZhaGiosk+1atVKABD79u1TaTN8+HBhYGCg3Nbbt28XAMSuXbtU6oWHhwsAKsljTlq0aCHatGmjfF69enUxefJkYWBgoDy4btu2TQAQt27dKlA/IiIiBIA8/xnPTWb/z507p1Jep04d0aFDB+Vzf39/YWBgIMLDw1Xq/fHHHwKA+PPPP4UQBUtEO3ToICpVqiRevXqlUm/MmDHCzMxM2b8uXbqIBg0aFLhvkydPFjKZTFy/fl2lvEOHDirHoIIce7ImMNo6bqn73ZLb8VNd6enpIjU1VdSoUUOMHz9eWV7Yz2bTpk2Fk5OTSEpKUpbFx8cLW1tble/RgnxnZe6bx44dU7t/8+bNEwBEUFCQEEKIu3fvCplMpkwKM6mbiKrz2Vq8eLEAoHIiI6vM74mcPhfqHq+LKhEdNWqUSr1FixYJACI2NlYI8fYfRgBi6tSpKvUyvxNzSkS/+uorYWBgIF6/fp3ttdwU+NL8+vXrIZfL0a9fPwCApaUl+vTpg1OnTuH27dsFXRwA4K+//sLz588xePBgpKenKx8KhQI+Pj4IDw9XuQQEQOUSVl4GDBgAU1NTlUsY27dvR0pKCoYMGaIsa9KkCTZt2oRvvvkGZ8+ezfUyXm42b96M8PBwlce794DmFu+aNWvQqFEjmJmZwcjICMbGxjh27BgiIyOVdUJDQ2FlZZVtAE3//v1Vnt+5cwf//PMPBgwYAAAq72WnTp0QGxtboMug74qKikKXLl1QuXJlrFu3Lt/6maf37e3tNVpfmzZtULZs2Wzlx48fh7e3N2xsbGBoaAhjY2PMmjULz549Q1xcXL7L7dy5MwwNDZXPPTw8AAD379/Pt22DBg3g7OysfG5mZoaaNWuqtA0NDUWbNm1Qrlw5ZZmBgQH69u2b7/IBoFq1arh8+TJCQ0Mxd+5ceHt7Izw8HGPGjIGnp6dyxoIDBw5AJpPh008/VdnODg4OqF+/frZLj3nJ3EbvjnZ89OgRypcvr9aAtEOHDsHLywu1a9fOt263bt1Unhfk/c+tfXJyssq2v3jxIrp16wY7OzvlPjJo0CBkZGTg1q1bKu2trKyyLfOTTz6BQqHAyZMnAbx9r8uUKYOuXbuqvNcNGjSAg4NDvu9127ZtcebMGSQlJeH+/fu4c+cO+vXrhwYNGiA4OBjA28u7zs7OqFGjRoH6Ub16dZQtWxZTp07FmjVrcOPGDbXex0wODg7K223efU/f3R4HDhyAu7s7GjRooNL/Dh065HiZOz/Jyck4duwYevToAXNz82zfU8nJycrbZZo0aYLLly9j1KhROHz4MOLj49VaR2hoKNzd3bPdK571O1OTY09h2uZ2HFDnu6Wg0tPT8d1336FOnTowMTGBkZERTExMcPv2bZXjSyZNPpuJiYkIDw9Hz549YWZmpiy3srJC165dVeoW9DurbNmyaNOmjVp9FUIoL8dn3gpWpUoVtG7dGrt27VJ7v3mXOp+tzMvuffv2xc6dOws0Yrwoj9fqym+bh4aGAkC241fv3r1zHeNib28PhUKBx48fqx1HgRLRO3fu4OTJk+jcuTOEEHj58iVevnyJ3r17A0CO98upI/M+st69e8PY2FjlsXDhQggh8Pz5c5U2ud1Hl5WtrS26deuGzZs3K+/72rRpE5o0aYK6desq6/32228YPHgw1q1bB09PT9ja2mLQoEFqv5m1a9dG48aNVR75xbt06VKMHDkSTZs2xa5du3D27FmEh4fDx8cHSUlJynrPnj1DhQoVsrXPWpb5Pk6aNCnb+zhq1CgA0Gj6m/v378PLywtGRkY4duwYbG1t822TGf+7X04FkdP79ffff6N9+/YAgF9++QVnzpxBeHg4vvrqK5V15sXOzk7luampqcZtM9trsq3yYmBggJYtW2LWrFkIDAzEo0eP8PHHH+P8+fPKz9iTJ08ghECFChWybeuzZ88WaDtnbqN3+5GUlKT2tnv69Knag7sK8/6r0z46OhoffvghYmJisGLFCpw6dQrh4eHKew+zrien7eLg4ADg7bYE3r7XL1++hImJSbb3+vHjx/m+197e3khJScHp06cRHByMcuXKoWHDhvD29lbeg37s2DF4e3sr26jbDxsbG4SGhqJBgwaYMWMG6tatCycnJ8yePVutf6bV2aefPHmCK1euZOu7lZUVhBAF/k559uwZ0tPT8cMPP2RbZqdOnQD873tq+vTpWLx4Mc6ePYuOHTvCzs4Obdu2RURERL7rKMh3ZkGOPYVpm9txS53tUFATJkzA119/je7du2P//v04d+4cwsPDUb9+/RyXq8ln88WLF1AoFMrPzLuylhX0O0vdYzzw9gTFvXv30KdPH8THxytzk759++LNmzfYvn272svKpM5nq2XLlti7dy/S09MxaNAgVKpUCe7u7mqtr6iO1wWR3zbP/A7M+rkxMjLKcZ8Fcj6e5KdAw7Y3bNgAIQT++OOPHOcDDAgIwDfffKNyxkkdmWePfvjhh1xHT2d9Iwoyf9aQIUPw+++/Izg4GM7OzggPD8fq1auzxbB8+XIsX74c0dHRCAwMxLRp0xAXF6cc9V0YOcW7detWtG7dOlssCQkJKs/t7Ozw999/Z2ufNUnOfB+nT5+Onj175hiHm5tbgeK+f/8+WrduDSEEQkJC1E44MmPJ7Us8Pzm9Xzt27ICxsTEOHDigkiTlNQ9mcbOzs8s2QAfIvq0KwsLCAtOnT8dvv/2Ga9euAXj7/spkMpw6dUr55fGunMpyk7mN3j2LW65cOVy4cEGt9uXLl893YExx2bt3LxITE7F79264uLgoy3Obwy+vbZX5RZt5I39u3wNWVlZ5xtS0aVNYWlri6NGjiIqKQtu2bSGTydC2bVssWbIE4eHhiI6OVklEC9KPevXqYceOHRBC4MqVK9i0aRPmzZsHuVyu0XRfWZUrVw5yuTzXEw2Z+03mZzIlJUXl9cyDWaayZcvC0NAQAwcOxOjRo3NcZpUqVQC8PeBNmDABEyZMwMuXL3H06FHMmDEDHTp0wIMHD3IdRa3u51CTY09h2hbnvI9bt27FoEGD8N1336mU//fffyhTpoxW1lG2bFnIZLIcv99yeq8L8p1VkPcqc0Dy0qVLsXTp0hxfHzFiBIDc99Ockj51PlsfffQRPvroI6SkpODs2bPw9/fHJ598AldXV3h6euYac1Ecr01NTbP1C8j+GVRX5nfgkydPVKbBSk9Pz3WZOR1P8qN2IpqRkYGAgABUq1Ytx0uzBw4cwJIlS3Do0KFcJ5LN7T+sDz74AGXKlMGNGzcwZswYtYNXV/v27VGxYkVs3LgRzs7OMDMzy3aJ5l3Ozs4YM2YMjh07hjNnzmg9nkwymSzbh+/KlSsICwtTmfqgVatW2LlzJw4dOoSOHTsqy3fs2KHS1s3NDTVq1MDly5ezffloIjo6Gq1bt0ZGRgZCQkJUDoj5cXFxgVwux7///lvoODJlTnn17j86SUlJ2LJli9bWUVitWrXCn3/+if/++0/5QVQoFPj999/Vah8bG5vjmYDMS2mZc9N26dIFCxYsQExMTL6X/fM7s5I5YvXdy5i1atXC9u3b8erVK9jY2OS5/I4dO2LLli24efNmgb84tS3z4PXu50oIkesozoSEBAQGBqpcovr111+VZ6WBt+/1jh07kJGRodE0dcbGxmjZsiWCg4Px4MED5Q8TfPjhhzAyMsLMmTOViamm/chsU79+fSxbtgybNm1S+x+J/HTp0gXfffcd7OzslAliTipUqAAzMzNcuXJFpXzfvn0qz83NzeHl5YWLFy/Cw8Mj2/y1uSlTpgx69+6NmJgYjBs3DlFRUblO09aqVSssXrwYN27cUKmT9TuzMMeeoj5uZVXQqwc5HV8OHjyImJgYVK9eXSsxWVhYoEmTJti9eze+//57ZZKXkJCA/fv3q9QtyHdWQbx48QJ79uzBBx98gG+++Sbb6+vWrcO2bdtw7do1uLu7K+dgvXLlisr3VeYMOjlR57NlamqKVq1aoUyZMjh8+DAuXrwIT0/PXLebto/XwNvR8Vk/f8ePH8fr1681Wl7md+Bvv/2GRo0aKcv/+OMPpKen59jm7t27sLOzK9BVQLUT0UOHDuHRo0dYuHBhjr9Y4+7ujh9//BHr16/PNRHN/BWTn3/+GVZWVjAzM0OVKlVgZ2eHH374AYMHD8bz58/Ru3dv2Nvb4+nTp7h8+TKePn2a7axhQRgaGmLQoEFYunQprK2t0bNnT5WD66tXr+Dl5YVPPvkEtWrVgpWVFcLDwxEUFJTrfyra0KVLF8yfPx+zZ89Gq1atcPPmTcybNw9VqlRR2ciDBw/GsmXL8Omnn+Kbb75B9erVcejQIRw+fBiA6rQ+a9euRceOHdGhQwf4+vqiYsWKeP78OSIjI3HhwgW1E6K4uDh4eXkhNjYW69evR1xcnMp9ePnNtWliYpLj1EiF0blzZyxduhSffPIJPv/8czx79gyLFy8u0Nm/ovbVV19h//79aNu2Lb766ivI5XKsWbNGea9Y1imYsqpbty7atm2Ljh07olq1akhOTsa5c+ewZMkSVKhQAUOHDgXw9iD4+eefY8iQIYiIiEDLli1hYWGB2NhYnD59GvXq1cPIkSMBvP2vfvfu3Vi9ejXee+89GBgYqNw6cvbsWdjZ2aFevXrKssyz4OfOnVPeDpGbefPm4dChQ2jZsiVmzJiBevXq4eXLlwgKCsKECROy/dBDUWrXrh1MTEzQv39/TJkyBcnJyVi9ejVevHiRY307OzuMHDkS0dHRqFmzJv7880/88ssvGDlypPKevX79+mHbtm3o1KkTvvzySzRp0gTGxsZ4+PAhTpw4gY8++gg9evTIM662bdti4sSJAKA88ymXy9G8eXMcOXIEHh4eKvdTq9uPAwcOYNWqVejevTuqVq0KIQR2796Nly9fFnjatNyMGzcOu3btQsuWLTF+/Hh4eHhAoVAgOjoaR44cwcSJE9G0aVPl/X8bNmxAtWrVUL9+ffz999/49ddfsy1zxYoVaNGiBT788EOMHDkSrq6uSEhIwJ07d7B//34cP34cwNvpYDLnaC5fvjzu37+P5cuXw8XFReV+2pxi3rBhAzp27Ih58+ahQoUK+PXXX/HPP/8A+N/n0NLSUuNjT2HaaiKv42dOunTpgk2bNqFWrVrw8PDA+fPn8f3332t9juT58+fDx8cH7dq1w8SJE5GRkYGFCxfCwsJC5YpYQb6zCmLbtm1ITk7GF198kWNuYmdnh23btmH9+vVYtmwZ3n//fbi5uWHSpElIT09H2bJlsWfPHpw+fVqlnTqfrVmzZuHhw4do27YtKlWqhJcvX2LFihUwNjZGq1atALy9718ul2Pbtm2oXbs2LC0t4eTkBCcnJ60drzMNHDgQX3/9NWbNmoVWrVrhxo0b+PHHH/M9mZCbunXron///liyZAkMDQ3Rpk0bXL9+HUuWLIGNjU2Ox7OzZ8+iVatWBTv7r+6opu7duwsTE5M8p4vo16+fMDIyUk7DgBxGcC1fvlxUqVJFGBoaZhtJFhoaKjp37ixsbW2FsbGxqFixoujcubP4/ffflXUyR4q9OypZHbdu3VKORA4ODlZ5LTk5Wfj5+QkPDw9hbW0t5HK5cHNzE7NnzxaJiYl5Ljdz9FnWEaWZMke7vduHTCkpKWLSpEmiYsWKwszMTDRq1Ejs3btXDB48ONso5+joaNGzZ09haWkprKysRK9evcSff/6Z46jfy5cvK6enMjY2Fg4ODqJNmzbKmQ7UkRl3bo+cRuZltX79emFoaCgePXqUa53cRs2/O33OuzZs2CDc3NyEqampqFq1qvD39xfr16/PNgoyt1Hz7472fnd97/Ynt1HzWePMaT1CCHHq1CnRtGlTYWpqKhwcHMTkyZPFwoUL8x1dKYQQa9euFT179hRVq1YV5ubmwsTERFSrVk34+fkpp/zI+n40bdpUWFhYCLlcLqpVqyYGDRokIiIilHWeP38uevfuLcqUKSNkMplK3xQKhXBxcRFjx45VWW5GRoZwdXXNNqoyt/fxwYMH4rPPPhMODg7C2NhYODk5ib59+4onT54IIXL/HOQ10vpduX3ucxoBu3//flG/fn1hZmYmKlasKCZPniwOHTqUbdRpq1atRN26dUVISIho3LixMDU1FY6OjmLGjBnZRrGmpaWJxYsXK5draWkpatWqJUaMGCFu376dZ+xCvP1MAhA1atRQKf/2228FADFhwoRsbdTpxz///CP69+8vqlWrJuRyubCxsRFNmjTJcTq9rDL7n1VO3z+vX78WM2fOFG5ubsLExEQ59dL48eNVpt159eqVGDZsmKhQoYKwsLAQXbt2FVFRUTl+Z9y7d0989tlnomLFisLY2FiUL19eNG/eXHzzzTfKOkuWLBHNmzcX5cqVEyYmJsLZ2VkMHTpUREVF5du/a9euCW9vb2FmZiZsbW3F0KFDRUBAgEAO08+pc+zJbbR1YY9bBfluyev4mdWLFy/E0KFDhb29vTA3NxctWrQQp06dyrbcwn42hRAiMDBQeHh4KLfRggULcvweFUK976zc9s2cNGjQQNjb24uUlJRc6zRr1kyUK1dOWefWrVuiffv2wtraWpQvX16MHTtWHDx4sMCfrQMHDoiOHTuKihUrChMTE2Fvby86deokTp06pbL+7du3i1q1agljY+NsnwV1jtfqjppPSUkRU6ZMEZUrVxZyuVy0atVKXLp0KddR81nzlpzWk5ycLCZMmCDs7e2FmZmZaNasmQgLCxM2NjYqsy8I8XbWF+Qww0h+Cjx9E+mOb7/9VshkshwTFF2QlJQkypcvn+8UTqVBu3btsiUhuuDo0aPCwMBAOd/nuxYvXizKli2b5zyZRCXJ8OHDhaWlZZ5JCxHl7cyZMwKA2LZtm0r5zJkzhbOzc7Z/5PMjEyLLzw6QTvrxxx8BvL13Ly0tDcePH8fKlSvx8ccfY/PmzRJHl7vVq1djzpw5uHv3bo6/1KSPJkyYgIYNG6Jy5cp4/vw5tm3bht27d2P9+vVq/TpIcfLy8kL16tVzvPcwOTkZtWvXxujRozFp0iQJoiPS3Lx58+Dk5ISqVavi9evXOHDgANatW4eZM2di3rx5UodHVCIEBwcjLCwM7733HuRyOS5fvowFCxbAxsYGV65cUd4X/PLlS1StWhU//PCDckoqdenuj52TCnNzcyxbtgxRUVFISUmBs7Mzpk6dipkzZxZ4WbndZJzJwMAg33sZ1fX555/j5cuXuHv3rso9iPosIyMDs2bNwuPHjyGTyVCnTh1s2bIFn376qdShqXjx4gVatWqlnCokKzMzM2zZsgUXL14s5siICs/Y2Bjff/89Hj58iPT0dNSoUQNLly7Fl19+KXVoRCWGtbU1jhw5guXLlyMhIQHlypVDx44d4e/vrzJ7zb179zB9+nR88sknBV4Hz4iWQvndRDx48OA8f8OYiIiISBt4RrQUCg8Pz/P1gsz/RURERKQpnhElIiIiIklo50ZAIiIiIqICYiJKRERERJJgIkpEREREkmAiSkRERESSYCJKRERERJJgIkpEREREkmAiSkRERESSYCJKRERERJJgIkpEREREkmAiSkRERESSYCJKRERERJJgIkpEREREkmAiSkRERESSYCJKRERERJIwkjoAotIqIzUVKUePQvHsGQzs7GDq7Q1DExOpw9IaRXo60iIikPH8OQxtbWHcuDEMjPTnK0ff+wfo/z6anpyM5L17oXjxAgZly8Kse3cYmZlJHZbWlIZ9VCgUyIiOhiIhAQZWVjB0dobMgOfYShKZEEJIHURxmTNnDubOnatSVqFCBTx+/DjXNqGhoZgwYQKuX78OJycnTJkyBX5+fkUdKum5xB07kH7zZrZyIzc3WPTrJ0FE2pUUHIzUsDDg3a8XmQwmnp6Qt2snXWBaou/9A/R/H0345RcoHj3KVm7g5ASr4cMliEi7SsM+mhYZiaSgIIj4eGWZzNoach8fGNeuLWFkVBCl7t+GunXrIjY2Vvm4evVqrnXv3buHTp064cMPP8TFixcxY8YMfPHFF9i1a1cxRkz6JrcDPACk37yJxB07ijki7UoKDkbqX3+pHgABQAik/vUXkoKDpQlMS/S9f4D+76O5JaEAoHj0CAm//FLMEWlXadhH0yIj8WbnTpUkFABEfDze7NyJtMhIiSKjgip1iaiRkREcHByUj/Lly+dad82aNXB2dsby5ctRu3ZtDBs2DJ999hkWL15cjBGTPslITc31AJ8p/eZNZKSmFlNE2qVIT397FiYPqWFhUKSnF1NE2qXv/QP0fx9NT07ONQnNpHj0COnJycUUkXaVhn1UKBRICgrKs05SUBCEQlFMEVFhlLpE9Pbt23ByckKVKlXQr18/3L17N9e6YWFhaN++vUpZhw4dEBERgbS0tBzbpKSkID4+XuWRkpKi1T5QyZVy9KhW6+matIiI7GdhshLibb0SSN/7B+j/Ppq8d69W6+ma0rCPZkRHZzsTmpWIj0dGdHQxRUSFUaoS0aZNm2Lz5s04fPgwfvnlFzx+/BjNmzfHs2fPcqz/+PFjVKhQQaWsQoUKSE9Px3///ZdjG39/f9jY2Kg8/P39td4XKpkUuexrmtbTNRnPn2u1nq7R9/4B+r+PKl680Go9XVMq9tGEBK3WI2np1/C5fHTs2FH5d7169eDp6Ylq1aohICAAEyZMyLGNTCZTeZ45titreabp06dnW5apqWlhwiY9YmBnh4w8zsK/W68kMrS1Rc7XCrLXK4n0vX+A/u+jBmXLIiMuTq16JVGp2EetrLRaj6RVqs6IZmVhYYF69erh9u3bOb7u4OCQbUR9XFwcjIyMYJfLl7CpqSmsra1VHkxEKZOpt7dW6+ka48aNgVz+SVOSyd7WK4H0vX+A/u+jZt27a7WerikN+6ihszNk1tZ51pFZW8PQ2bmYIqLCKNWJaEpKCiIjI+Ho6Jjj656engjOMrrwyJEjaNy4MYyNjYsjRNIzhiYmMHJzy7OOkZtbiZ2r0cDICCaennnWMfH0LLFzGep7/wD930eNzMxg4OSUZx0DJ6cSO59oadhHZQYGkPv45FlH7uPD+URLiFK1lSZNmoTQ0FDcu3cP586dQ+/evREfH4/BgwcDeHtZfdCgQcr6fn5+uH//PiZMmIDIyEhs2LAB69evx6RJk6TqAukBi379cj3Q68McjfJ27WDSvHn2szIyGUyaNy/xcxjqe/8A/d9HrYYPzzUZ1Yd5REvDPmpcuzbM+/bNdmZUZm0N8759OY9oCVKqJrTv168fTp48if/++w/ly5dHs2bNMH/+fNSpUwcA4Ovri6ioKISEhCjbhIaGYvz48coJ7adOncoJ7Ukr9P1Xa/T9V130vX+A/u+j/GWlko+/rFTylapElIiIiIh0B/9tICIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJlNpE1N/fHzKZDOPGjcu1TkhICGQyWbbHP//8U3yBEhEREekpI6kDkEJ4eDh+/vlneHh4qFX/5s2bsLa2Vj4vX758UYVGREREVGqUujOir1+/xoABA/DLL7+gbNmyarWxt7eHg4OD8mFoaFjEURIRERHpv1KXiI4ePRqdO3eGt7e32m0aNmwIR0dHtG3bFidOnMizbkpKCuLj41UeKSkphQ2biIiIciCEQPqbN1KHQRoqVYnojh07cOHCBfj7+6tV39HRET///DN27dqF3bt3w83NDW3btsXJkydzbePv7w8bGxuVh7rrIyIiotwp0tNxb8sW7K9WTfk4UL06DtWrJ3VopCGZEEJIHURxePDgARo3bowjR46gfv36AIDWrVujQYMGWL58udrL6dq1K2QyGQIDA3N8PSUlJdsZUFNTU5iammocOxERUWmT/vo1bq9ahTtr16pVv+u//xZxRFQUSk0iunfvXvTo0UPl/s6MjAzIZDIYGBggJSVFrXs/v/32W2zduhWRkZFFGS4REVGpkfT4MSIXLULMvn0Fblupe3fUmz8fRubmRRAZFbVSM2q+bdu2uHr1qkrZkCFDUKtWLUydOlXtAUgXL16Eo6NjUYRIRESk9+L/+QfX5s3Ds3PnCty2up8faowcCSNLyyKIjKRQahJRKysruLu7q5RZWFjAzs5OWT59+nTExMRg8+bNAIDly5fD1dUVdevWRWpqKrZu3Ypdu3Zh165dxR4/ERFRSRN38iSuzZmDxPv3C9zWfc4cuPTvDwOjUpOqlErcuu+IjY1FdHS08nlqaiomTZqEmJgYyOVy1K1bFwcPHkSnTp0kjJKIiEi3CIUCD/74A1fnzIGigDPFmDk4oN6cOajg7Q2ZTFZEEZKuKjX3iBIREVHhZSQn487PP+PWihUFblu2YUPU/fprlP3/QcNEPCNKREREOUr57z/8s2QJonfuLHBbx44dUWfaNJhXqlQEkZG+YCJKREREeH33Lq7Nn4+necyVnZsqvr6o+cUXMLGxKYLISJ8xESUiIiplnv39N67Ono2EW7cK3LbOtGlwHTQIhpwfm7SAiSgREZGeEhkZuLdlC26tXIm0V68K1Na4bFnUmz0bTl26cBARFRkmokRERHog5flzHHn/fY3aWteuDfdZs2DXpImWoyLKGxNRIiKiEubllSs41aOHRm3tvbxQd8YMWFatquWoiAqOiSgREZEOi965E5enT9e4vee2bSjXrJkWIyLSHiaiREREOuLChAka/d56Ju8zZyB3cNBiRERFi4koERFRMRNC4FC9eshIStJ4GZ0jI2FgYqLFqIiKHxNRIiKiIpT++jUOFeKXhKzc3ND6zz+1GBGR7mAiSkREpCUJ//6LkPbtNW5f3c8PtSdP1mJERLqNiSgREZEGYg8fRsSoURq3f3/tWjh4e2sxIqKSh4koERFRPq5/9x3url+vcXuvo0dhWaWKFiMi0g9MRImIiN5xon17vP73X43bd7x2DUZyuRYjItJfTESJiKhUSk9MxCEPD43bm5Yvj3ZhYfz5S6JCYCJKRER679X16zjZrZvG7Z379kV9f38tRkREABNRIiLSM3c3bcL1+fM1bt9g0SJU7tVLixERUW6YiBIRUYl1qkcPvLxyReP2HLlOJC0mokREVCLsr1atUO29T5+G3NFRS9EQkTYwESUiIp2iSEvDwVq1CrWMLrduQWZoqKWIiKioMBElIiLJvHnwAMdaty7UMroWYqolIpIWE1EiIioWMfv348K4cRq3L9e8OTy3bNFeQEQkOSaiRESkdee/+AKPDh7UuL3HN9/ApX9/LUZERLqIiSgRERVKYQcRtT58GFbVq2spGiIqSZiIEhGRWoQQOFDIhLHTjRswNDXVUkREVNIxESUiomxSnj3DkSZNCrUMDiIiovwwESUiKuWenjmDs4MGadzewsUFbY4f12JERFRaMBElIipFbvj749916zRu7zZuHGqOHavFiIioNGMiSkSkp4Leew9pL19q3P6DnTth+9572guIiCgLJqJERHqgsCPXfS5ehLG1tZaiISJSDxPREkqRno60iAhkPH8OQ1tbGDduDAMj/dmc+t4/Ik2lJybikIdHoZbR5c4dyGSyfOvp++dQKBTIiI6GIiEBBlZWMHR2hszAQOqwqAC4DUs+mRBCSB2EFPz9/TFjxgx8+eWXWL58ea71QkNDMWHCBFy/fh1OTk6YMmUK/Pz8ii/QHCQFByM1LAx4d9PJZDDx9IS8XTvpAtMSfe8fkbpeXb+Ok926FWoZmo5c1/fPYVpkJJKCgiDi45VlMmtryH18YFy7toSRkbq4DfWD/vxrWwDh4eH4+eef4ZHPWYV79+6hU6dOGD58OLZu3YozZ85g1KhRKF++PHr16lVM0apKCg5G6l9/ZX9BCGV5ST5I6Hv/iHLz74YNuPHttxq3d+7XD/UL0f5d+v45TIuMxJudO7OVi/h4vNm5E+Z9+zKR0XHchvqj1CWir1+/xoABA/DLL7/gm2++ybPumjVr4OzsrDxjWrt2bURERGDx4sWSJKKK9PS3ZyjykBoWBlMvrxJ5+Uzf+0eU6WS3bnh1/brG7Zv88gsqtGmjxYj+R98/h0KhQFJQUJ51koKCYOTmxku8OorbUL+UvG+RQho9ejQ6d+4Mb2/vfBPRsLAwtG/fXqWsQ4cOWL9+PdLS0mBsbJytTUpKClJSUlTKTE1NYaqFXxJJi4hQvUyWEyGQFhEB02bNCr2+4qbv/aPSqbCDiLzPnIHcwUFL0eRP3z+HGdHRKpdycyLi45ERHQ0jV9fiCYoKhNtQv5SqRHTHjh24cOECwsPD1ar/+PFjVKhQQaWsQoUKSE9Px3///QdHR8dsbfz9/TF37lyVstmzZ2POnDkax50p4/lzrdbTNfreP9JvitRUHCzkpcAut25BZmiopYg0o++fQ0VCglbrUfHjNtQvpSYRffDgAb788kscOXIEZmZmarfLOrI0c2xXbiNOp0+fjgkTJqiUaeNsKAAY2toiTc16JZG+94/0R/KTJwhu3rxQy9DVn7/U98+hgZWVVutR8eM21C+lJhE9f/484uLi8N47kzNnZGTg5MmT+PHHH5GSkgLDLGciHBwc8PjxY5WyuLg4GBkZwc7OLsf1aOsyfE6MGzdG8pEjeV82k8lg3Lhxkay/qOl7/6hkKuzPX5b/8EM027RJewEVMX3/HBo6O0NmbZ3npV2ZtTUMnZ2LMSoqCG5D/VJqEtG2bdvi6tWrKmVDhgxBrVq1MHXq1GxJKAB4enpi//79KmVHjhxB48aNc7w/tKgZGBnBxNMz59Gs/8/E07NEDiAA9L9/pPturlyJWytWaNy+vr8/nPv21WJExU/fP4cyAwPIfXxyHHGdSe7jw0EuOozbUL+U2nlEAaB169Zo0KCBclT89OnTERMTg82bNwN4O32Tu7s7RowYgeHDhyMsLAx+fn7Yvn27ZNM3Afo/v5++9490Q2FHrrc9dQrmTk5ajEi36PvnkHNQlnzchvqBieg7iaivry+ioqIQEhKirBMaGorx48crJ7SfOnWq5BPaA/r/iyf63j8qPkIIHKhevVDL6BwZCQMTEy1FVHLo++eQv8pT8nEblnylOhElIv2S+uoVDjdqpHF7AzMzdC7EWVIiIioY/fnXlohKlZdXruBUjx4at6/cpw8aLFigxYiIiKigmIgSkc6L2roVV2fP1rh9w2XLUKmQv9lORETax0SUiHTK359/jifHjmncvvXhw7Aq5D2hRERUPJiIEpFkCvvzlx2vXoWRubmWoiEiouLGRJSIilx6UhIOubsXahld7tzJ9RfNiIioZGIiSkRaFf/PPwjt3Fnj9iXtl4iIiEhzTESJSGP3t2/HlZkzNW5f9+uvUdXXV3sBERFRicJElIjUcm7YMMSdOKFx+xa7dqFsgwbaC4iIiEo8JqJElE1hBxG1P3cOpuXKaSkaIiLSV0xEiUoxRWoqDhbyN5m73L7Nn9QjIiKNMBElKiXePHiAY61ba9zewMQEnSMjtRcQERGVekxEifRQ7OHDiBg1SuP2zn37or6/vxYjIiIiyo6JKFEJd/mrrxC9Y4fG7d/76Sc4+fhoMSIiIiL1MBElKkEO1q4NRWqqxu3bhoTAvHJlLUZERESkOSaiRDpIKBQ4UKNGoZbROTISBiYmWoqIiIhI+5iIEkks5b//cKRp00Ito+u//2opGiIiouLDRJSoGL28cgWnevTQuH35li3RbONGLUZEREQkHSaiREXk3ubNuDZ3rsbt682fD9dPPtFiRERERLqFiSiRFpz77DPEhYZq3L7VwYOwrlVLixERERHpPiaiRAUghMCB6tULtYyO167BSC7XUkREREQlFxNRolykv3mDQ/XqFWoZHERERESUOyaiRAAS/v0XIe3ba9zevnVrNF2/XosRERER6b8Sk4impqbi3r17qFatGoyMSkzYpINi9u/HhXHjNG7vPmcOqgwcqL2AiIiISimdz+jevHmDsWPHIiAgAABw69YtVK1aFV988QWcnJwwbdo0iSMkXXZ5+nRE79ypcfsP9+xBGQ8PLUZEREREmXQ+EZ0+fTouX76MkJAQ+Lzze9je3t6YPXs2E1FS+tPDAxmJiRq373DhAkxsbLQYEREREeVF5xPRvXv34rfffkOzZs0gk8mU5XXq1MG/HAhSKinS0nCwkFMddbl9GzIDAy1FRERERJrQ+UT06dOnsLe3z1aemJiokpiSfkqKjcXRFi00bm9dpw5a7d+vxYiIiIhIW3Q+EX3//fdx8OBBjB07FgCUyecvv/wCT09PKUMjLXt+/jzO9O2rcfuaX3wBty+/1GJEREREVJR0PhH19/eHj48Pbty4gfT0dKxYsQLXr19HWFgYQgvxSzYkrXsBAbg2b57G7Ztt3ozyH3ygxYiIiIiouMmEEELqIPJz9epVLF68GOfPn4dCoUCjRo0wdepU1CvkZONUPML9/PA4OFjj9t5nzkDu4KDFiIiIiEgXlIhElEoGoVDgQI0ahVpG55s3YcB5YomIiEoFnT/iGxoaIjY2NtuApWfPnsHe3h4ZGRlqL2v16tVYvXo1oqKiAAB169bFrFmz0LFjxxzrh4SEwMvLK1t5ZGQkahVy1HZJl5aQgKAGDTRuX/a999CiEPN7EhERUcmn84lobidsU1JSYGJiUqBlVapUCQsWLED16tUBAAEBAfjoo49w8eJF1K1bN9d2N2/ehLW1tfJ5+fLlC7Teki7+5k2EduqkcXu3ceNQ8/8HmxERERFl0tlEdOXKlQDejpJft24dLC0tla9lZGTg5MmTBT4r2bVrV5Xn3377LVavXo2zZ8/mmYja29ujTJkyBVpXSfVw715cnDhR4/ZNN26EfcuWWoyIiIiI9JXOJqLLli0D8PaM6Jo1a2BoaKh8zcTEBK6urlizZo3Gy8/IyMDvv/+OxMTEfKeBatiwIZKTk1GnTh3MnDkzx8v1mVJSUpCSkqJSZmpqClNTU41jLSpXZs7E/e3bNW7f9uRJmFesqMWIiIiIqDTR2UT03r17AAAvLy/s2bNHa2ckr169Ck9PTyQnJ8PS0hJ79uxBnTp1cqzr6OiIn3/+Ge+99x5SUlKwZcsWtG3bFiEhIWiZy1k/f39/zJ07V6Vs9uzZmDNnjlbi14QQAsHNmyMlLk7jZXS6cQOGOphMExERUcml06Pm09LS4ObmhgMHDuSaLBZUamoqoqOj8fLlS+zatQvr1q1DaGio2svv2rUrZDIZAgMDc3xd6jOiGcnJ+DOP2wzyY+7sjLYnTmgxIiIiIqKc6ewZUQAwNjZGSkqKVn/K08TERDlYqXHjxggPD8eKFSuwdu1atdo3a9YMW7duzfX14kw63zx4gGOtW2vc3nXgQNST8EwtERERlW46nYgCwNixY7Fw4UKsW7cORkUwv6QQItsZzLxcvHgRjo6OWo+joAr6y0SNli9HxSyDtYiIiIikpPOJ6Llz53Ds2DEcOXIE9erVg4WFhcrru3fvVntZM2bMQMeOHVG5cmUkJCRgx44dCAkJQVBQEABg+vTpiImJwebNmwEAy5cvh6urK+rWrYvU1FRs3boVu3btwq5du7TXQQ0ZWVnl+lrroCBYFXJieSIiIqKipvOJaJkyZdCrVy+tLOvJkycYOHAgYmNjYWNjAw8PDwQFBaFdu3YAgNjYWERHRyvrp6amYtKkSYiJiYFcLkfdunVx8OBBdCrEnJraUqlHD5Rt0ABmFSrAKEtyTkRERFQS6PRgJSIiIiLSXwZSB6CO9PR0HD16FGvXrkVCQgIA4NGjR3j9+rXEkRERERGRpnT+jOj9+/fh4+OD6OhopKSk4NatW6hatSrGjRuH5OTkQk1qT0RERETS0fkzol9++SUaN26MFy9eQC6XK8t79OiBY8eOSRgZERERERWGzg9WOn36NM6cOQMTExOVchcXF8TExEgUFREREREVls6fEVUoFMjIyMhW/vDhQ1jlMYUREREREek2nU9E27Vrh+XLlyufy2QyvH79GrNnz9aJaZSIiIiISDM6P1jp0aNH8PLygqGhIW7fvo3GjRvj9u3bKFeuHE6ePAl7e3upQyQiIiIiDeh8IgoASUlJ2L59Oy5cuACFQoFGjRphwIABKoOXiIiIiKhk0dlEtGLFimjTpg28vLzQpk0buLq6Sh0SEREREWmRziai8+fPR2hoKMLCwpCcnIzKlSujTZs2aNOmDVq3bo1KlSpJHSIRERERFYLOJqKZ0tLSEBYWhpCQEGVimpKSgqpVq6JNmzZYu3at1CESERERkQZ0PhHN6sWLF1iyZAl++OEHvH79OsepnYiIiIhI9+n8hPbJyck4c+YMQkJCEBISgvDwcLi6uuLjjz9Gq1atpA6PiIiIiDSks2dEZ8+ejRMnTiA8PBxVq1ZFq1atlA8HBwepwyMiIiKiQtLZRNTAwADOzs6YNm0a+vTpAzs7O6lDIiIiIiIt0tlENCgoSHk5/uLFi6hZsyZat26tPCtavnx5qUMkIiIiokLQ2UT0XQkJCTh16hRCQ0Nx4sQJXL58GdWrV4eXlxd+/PFHqcMjIiIiIg2UiEQ0U0ZGBv7++28EBgZi1apVHDVPREREVILp9Kh5hUKBiIgInDhxAiEhIThz5gwSExNRqVIl9OjRA15eXlKHSEREREQa0tkzop06dcKZM2eQkJAAJycntG7dGl5eXvDy8kLVqlWlDo+IiIiICklnz4ja2Njg+++/h5eXF2rUqCF1OERERESkZTp7RpSIiIiI9JuB1AEQERERUenERJSIiIiIJMFElIiIiIgkUaIT0aSkJKlDICIiIiIN6XwiOnr06BzLExMT0bFjx2KOhoiIiIi0RecT0SNHjmDmzJkqZYmJifDx8eGvKhERERGVYDo7j2imI0eOoEWLFrCzs8P48eORkJCADh06wMjICIcOHZI6PCIiIiLSkM4nolWqVMHhw4fRunVrGBgYYMeOHTA1NcXBgwdhYWEhdXhEREREpKESM6H92bNn4e3tjaZNm+LAgQOQy+VShyQpoVAgIzoaioQEGFhZwdDZGTIDnb/Tgt7BbViyKdLTkRYRgYznz2Foawvjxo1hYKTz/9sXiL7vo+wfkfR0MhFt2LAhZDJZtvL79+/D3t5eJQm9cOGC2stdvXo1Vq9ejaioKABA3bp1MWvWrDwHPYWGhmLChAm4fv06nJycMGXKFPj5+anfmSKQFhmJpKAgiPh4ZZnM2hpyHx8Y164tYWSkLm7Dki0pOBipYWHAu1+fMhlMPD0hb9dOusC0SN/3UfaPSDfoZCI6d+5ctevOnj1b7br79++HoaEhqlevDgAICAjA999/j4sXL6Ju3brZ6t+7dw/u7u4YPnw4RowYgTNnzmDUqFHYvn07evXqpfZ6tSktMhJvdu7M9XXzvn35JaPjuA1LtqTgYKT+9Veur5s0b17ik1F930fZv5LdP9IvOpmIFidbW1t8//33GDp0aLbXpk6disDAQERGRirL/Pz8cPnyZYSFhRVnmADeXmZJWLFC5T/crGTW1rD68kteftFR3IYlmyI9HQnffad6JjQrmQxWM2aU2Mv0+r6Psn8lu3+kf3R+LwwPD8e5c+eylZ87dw4REREaLzcjIwM7duxAYmIiPD09c6wTFhaG9u3bq5R16NABERERSEtLy7FNSkoK4uPjVR4pKSkax6kSc3R0nl8uACDi45ERHa2V9ZH2cRuWbGkREXknoQAgxNt6JZS+76PsX8nuH+kfnU9ER48ejQcPHmQrj4mJyXWy+7xcvXoVlpaWMDU1hZ+fH/bs2YM6derkWPfx48eoUKGCSlmFChWQnp6O//77L8c2/v7+sLGxUXn4+/sXOM6cKBIStFqPih+3YcmW8fy5VuvpIn3fR9m/gtUjKmo6f+3oxo0baNSoUbbyhg0b4saNGwVenpubGy5duoSXL19i165dGDx4MEJDQ3NNRrMOmsq8kyGnwVQAMH36dEyYMEGlzNTUtMBx5sTAykqr9aj4cRuWbIa2tsj5Wkj2eiWVvu+j7F/B6hEVNZ0/I2pqaoonT55kK4+NjYWRBvdgmZiYoHr16mjcuDH8/f1Rv359rFixIse6Dg4OePz4sUpZXFwcjIyMYGdnl2u81tbWKg9tJaKGzs6QWVvnWUdmbQ1DZ2etrI+0j9uwZDNu3BjI5Z9QJZnsbb0SSt/3UfavZPeP9I/OJ6Lt2rXD9OnT8erVK2XZy5cvMWPGDLTTwshUIUSu93B6enoiODhYpezIkSNo3LgxjI2NC73ugpIZGEDu45NnHbmPD29A12HchiWbgZERTHK5pzyTiadniR2oBOj/Psr+lez+kf7R+VHzMTExaNmyJZ49e4aGDRsCAC5duoQKFSogODgYlStXVntZM2bMQMeOHVG5cmUkJCRgx44dWLBgAYKCgpQJb0xMDDZv3gzgf9M3jRgxAsOHD0dYWBj8/Pwknb4J4Pxw+oDbsGTjPKIlfx9l/4h0g84nogCQmJiIbdu24fLly5DL5fDw8ED//v0LfFZy6NChOHbsGGJjY2FjYwMPDw9MnTpVeWbV19cXUVFRCAkJUbYJDQ3F+PHjlRPaT506VfIJ7QH+YoY+4DYs2fjLSiUf+0ckvRKRiBIRERGR/ikx/77fuHED0dHRSE1NVSnv1q2bRBERERERUWHofCJ69+5d9OjRA1evXoVMJss2fVJGRoaU4RERERGRhnT+ZpEvv/wSVapUwZMnT2Bubo7r16/j5MmTaNy4scq9nERERERUsuj8PaLlypXD8ePH4eHhARsbG/z9999wc3PD8ePHMXHiRFy8eFHqEImIiIhIAzp/RjQjIwOWlpYA3ialjx49AgC4uLjg5s2bUoZGRERERIWg8/eIuru748qVK6hatSqaNm2KRYsWwcTEBD///DOqVq0qdXhEREREpCGdvzR/+PBhJCYmomfPnrh79y66dOmCf/75B3Z2dvjtt9/Qpk0bqUMkIiIiIg3ofCKak+fPn6Ns2bLKkfNEREREVPLo/KX5dz148AAymQyVKlWSOhQiIiIiKiSdH6yUnp6Or7/+GjY2NnB1dYWLiwtsbGwwc+ZMpKWlSR0eEREREWlI58+IjhkzBnv27MGiRYvg6ekJAAgLC8OcOXPw33//Yc2aNRJHSERERESa0Pl7RG1sbLBjxw507NhRpfzQoUPo168fXr16JVFkRERERFQYOn9p3szMDK6urtnKXV1dYWJiUvwBEREREZFW6HwiOnr0aMyfPx8pKSnKspSUFHz77bcYM2aMhJERERERUWHo5D2iPXv2VHl+9OhRVKpUCfXr1wcAXL58GampqWjbtq0U4RERERGRFuhkImpjY6PyvFevXirPK1euXJzhEBEREVER0PnBSkRERESkn3T+HlEiIiIi0k86eWm+YcOGav9854ULF4o4GiIiIiIqCjqZiHbv3l35d3JyMlatWoU6deooJ7Q/e/Ysrl+/jlGjRkkUIREREREVls7fIzps2DA4Ojpi/vz5KuWzZ8/GgwcPsGHDBokiIyIiIqLC0PlE1MbGBhEREahRo4ZK+e3bt9G4cWP+shIRERFRCaXzg5XkcjlOnz6drfz06dMwMzOTICIiIiIi0gadvEf0XePGjcPIkSNx/vx5NGvWDMDbe0Q3bNiAWbNmSRwdEREREWlK5y/NA8DOnTuxYsUKREZGAgBq166NL7/8En379pU4MiIiIiLSVIlIRImIiIhI/+j8PaIA8PLlS6xbtw4zZszA8+fPAbydPzQmJkbiyIiIiIhIUzp/j+iVK1fg7e0NGxsbREVFYdiwYbC1tcWePXtw//59bN68WeoQiYiIiEgDOn9GdMKECfD19cXt27dVRsl37NgRJ0+elDAyIiIiIioMnU9Ew8PDMWLEiGzlFStWxOPHjyWIiIiIiIi0QecTUTMzM8THx2crv3nzJsqXLy9BRERERESkDTqfiH700UeYN28e0tLSAAAymQzR0dGYNm0aevXqpfZy/P398f7778PKygr29vbo3r07bt68mWebkJAQyGSybI9//vmnUH0iIiIiohKQiC5evBhPnz6Fvb09kpKS0KpVK1SvXh1WVlb49ttv1V5OaGgoRo8ejbNnzyI4OBjp6elo3749EhMT82178+ZNxMbGKh9Zf26UiIiIiAquxMwjevz4cVy4cAEKhQKNGjWCt7d3oZaXmdyGhoaiZcuWOdYJCQmBl5cXXrx4gTJlyhRqfURERESkSuenb8rUpk0bNG/eHKamppDJZIVe3qtXrwAAtra2+dZt2LAhkpOTUadOHcycORNeXl651k1JSUFKSopKmampKUxNTQsXMBEREZGe0flL8wqFAvPnz0fFihVhaWmJe/fuAQC+/vprrF+/XqNlCiEwYcIEtGjRAu7u7rnWc3R0xM8//4xdu3Zh9+7dcHNzQ9u2bfOcNsrf3x82NjYqD39/f43iJCIiItJnOn9pft68eQgICMC8efMwfPhwXLt2DVWrVsXOnTuxbNkyhIWFFXiZo0ePxsGDB3H69GlUqlSpQG27du0KmUyGwMDAHF/nGVEiIiIi9ej8GdHNmzfj559/xoABA2BoaKgs9/Dw0Gj0+tixYxEYGIgTJ04UOAkFgGbNmuH27du5vm5qagpra2uVB5NQIiIioux0/h7RmJgYVK9ePVu5QqFQTumkDiEExo4diz179iAkJARVqlTRKJ6LFy/C0dFRo7ZERERE9D86n4jWrVsXp06dgouLi0r577//joYNG6q9nNGjR+PXX3/Fvn37YGVlpfxVJhsbG8jlcgDA9OnTERMTo/z9+uXLl8PV1RV169ZFamoqtm7dil27dmHXrl1a6h0RERFR6aXziejs2bMxcOBAxMTEQKFQYPfu3bh58yY2b96MAwcOqL2c1atXAwBat26tUr5x40b4+voCAGJjYxEdHa18LTU1FZMmTUJMTAzkcjnq1q2LgwcPolOnToXuFxEREVFpp/ODlQDg8OHD+O6773D+/HnlPKKzZs1C+/btpQ6NiIiIiDSks4no3bt3UaVKFa3MGUpEREREukdnR83XqFEDT58+VT7/+OOP8eTJEwkjIiIiIiJt0tlENOuJ2j///FOt34UnIiIiopJBZxNRIiIiItJvOpuIymSybPeH8n5RIiIiIv2hs9M3CSHg6+ur/FWi5ORk+Pn5wcLCQqXe7t27pQiPiIiIiApJZxPRwYMHqzz/9NNPJYqEiIiIiIqCzk7fRERERET6TWfvESUiIiIi/cZElIiIiIgkwUSUiIiIiCTBRJSIiIiIJMFElIiIiIgkwUSUiIiIiCTBRJSIiIiIJMFElIiIiIgkwUSUiIiIiCTBRJSIiIiIJMFElIiIiIgkwUSUiIiIiCTBRJSIiIiIJMFElIiIiIgkwUSUiIiIiCTBRJSIiIiIJMFElIiIiIgkwUSUiIiIiCTBRJSIiIiIJMFElIiIiIgkwUSUiIiIiCTBRJSIiIiIJGEkdQCkGaFQICM6GoqEBBhYWcHQ2RkyA/5fQURERCVHqclc/P398f7778PKygr29vbo3r07bt68mW+70NBQvPfeezAzM0PVqlWxZs2aYog2b2mRkUhYsQKJAQFI2r0biQEBSFixAmmRkVKHRkRERKS2UpOIhoaGYvTo0Th79iyCg4ORnp6O9u3bIzExMdc29+7dQ6dOnfDhhx/i4sWLmDFjBr744gvs2rWrGCNXlRYZiTc7d0LEx6uUi/h4vNm5k8koERERlRgyIYSQOggpPH36FPb29ggNDUXLli1zrDN16lQEBgYi8p3kzs/PD5cvX0ZYWFhxhaokFAokrFiRLQl9l8zaGlZffsnL9ERERKTzSm228urVKwCAra1trnXCwsLQvn17lbIOHTogIiICaWlpObZJSUlBfHy8yiMlJUUrMWdER+eZhAJvz4xmREdrZX1ERERERalUJqJCCEyYMAEtWrSAu7t7rvUeP36MChUqqJRVqFAB6enp+O+//3Js4+/vDxsbG5WHv7+/VuJWJCRotR4RERGRlErlqPkxY8bgypUrOH36dL51ZTKZyvPMOxmylmeaPn06JkyYoFJmamqqYaSqDKystFqPiIiISEqlLhEdO3YsAgMDcfLkSVSqVCnPug4ODnj8+LFKWVxcHIyMjGBnZ5djG1NTU60lnlkZOjtDZm2d7z2ihs7ORbJ+IiIiIm0qNZfmhRAYM2YMdu/ejePHj6NKlSr5tvH09ERwcLBK2ZEjR9C4cWMYGxsXVai5khkYQO7jk2cduY8PByoRERFRiVBqRs2PGjUKv/76K/bt2wc3NzdluY2NDeRyOYC3l9VjYmKwefNmAG+nb3J3d8eIESMwfPhwhIWFwc/PD9u3b0evXr0k6QfwdgqnpKAglTOjMmtryH18YFy7tmRxERERERVEqUlEc7unc+PGjfD19QUA+Pr6IioqCiEhIcrXQ0NDMX78eFy/fh1OTk6YOnUq/Pz8iiHivPGXlYiIiKikKzWJKBERERHpFp5CIyIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJMBElIiIiIkkwESUiIiIiSRhJHQC9lZGRgbS0NKnDICoSxsbGMDQ0lDoMIiLSMUxEJSaEwOPHj/Hy5UupQyEqUmXKlIGDgwNkMpnUoRARkY5gIiqxzCTU3t4e5ubmPEiT3hFC4M2bN4iLiwMAODo6ShwRERHpCiaiEsrIyFAmoXZ2dlKHQ1Rk5HI5ACAuLg729va8TE9ERAA4WElSmfeEmpubSxwJUdHL3M95LzQREWViIqoDeDmeSgPu50RElBUTUSIiIiKSBBNR0kubNm1CmTJlpA6DiIiI8sBElHRSXFwcRowYAWdnZ5iamsLBwQEdOnRAWFhYoZa7adMmNGvWDADQunVryGSybA8/Pz9tdIGIiIjywVHzpJN69eqFtLQ0BAQEoGrVqnjy5AmOHTuG58+fF2q5gYGB+Oijj5TPhw8fjnnz5qnU4eAxIiKi4sEzoqRzXr58idOnT2PhwoXw8vKCi4sLmjRpgunTp6Nz584AgKVLl6JevXqwsLBA5cqVMWrUKLx+/TrP5SYnJ+PIkSPo1q2bsszc3BwODg4qD2trawDA5s2bYWlpidu3byvrjx07FjVr1kRiYiIAwNXVFfPnz8cnn3wCS0tLODk54YcfftD2W0JERKSXmIiSzrG0tISlpSX27t2LlJSUHOsYGBhg5cqVuHbtGgICAnD8+HFMmTIlz+UeO3YMDg4OqFu3rlpxDBo0CJ06dcKAAQOQnp6OoKAgrF27Ftu2bYOFhYWy3vfffw8PDw9cuHAB06dPx/jx4xEcHKx+h4mIiEopJqKkc4yMjLBp0yYEBASgTJky+OCDDzBjxgxcuXJFWWfcuHHw8vJClSpV0KZNG8yfPx87d+7Mc7n79u1TuSwPAKtWrVImvpmPgIAA5etr165FbGwsvvjiC/j6+mL27Nl4//33VZbxwQcfYNq0aahZsybGjh2L3r17Y9myZVp4J4iIiPRbqUpET548ia5du8LJyQkymQx79+7Ns35ISEiOg1n++eef4gm4FOvVqxcePXqEwMBAdOjQASEhIWjUqBE2bdoEADhx4gTatWuHihUrwsrKCoMGDcKzZ8+Ul8yzEkJg//79KpflAWDAgAG4dOmSyqNHjx7K18uWLYv169dj9erVqFatGqZNm5Zt2Z6entmeR0ZGFvIdICIi0n+lKhFNTExE/fr18eOPPxao3c2bNxEbG6t81KhRo4gipHeZmZmhXbt2mDVrFv766y/lGcn79++jU6dOcHd3x65du3D+/Hn89NNPAHL/1Z6///4bqampaNGihUq5jY0NqlevrvLIvEc008mTJ2FoaIhHjx7lmuhmxcnbiYiI8leqEtGOHTvim2++Qc+ePQvUzt7eXmUwC38nWxp16tRBYmIiIiIikJ6ejiVLlqBZs2aoWbMmHj16lGfbffv2oXPnzgXedn/99RcWLVqE/fv3w9raGmPHjs1W5+zZs9me16pVq0DrISIiKo04fZMaGjZsiOTkZNSpUwczZ86El5dXrnVTUlKyDbAxNTWFqalpUYepN549e4Y+ffrgs88+g4eHB6ysrBAREYFFixbho48+QrVq1ZCeno4ffvgBXbt2xZkzZ7BmzZo8lxkYGIi5c+dmK3/z5g0eP36sUmZqaoqyZcsiISEBAwcOxNixY9GxY0c4OzujcePG6NKlC/r06aOsf+bMGSxatAjdu3dHcHAwfv/9dxw8eFA7bwYREZEeK1VnRAvK0dERP//8M3bt2oXdu3fDzc0Nbdu2xcmTJ3Nt4+/vDxsbG5WHv79/MUZd8llaWqJp06ZYtmwZWrZsCXd3d3z99dcYPnw4fvzxRzRo0ABLly7FwoUL4e7ujm3btuX5Hv/777+4c+cOOnTokO21X375BY6OjiqP/v37AwC+/PJLWFhY4LvvvgMA1K1bFwsXLoSfnx9iYmKUy5g4cSLOnz+Phg0bYv78+ViyZEmO6yIiIiJVMiGEkDoIKchkMuzZswfdu3cvULuuXbtCJpMhMDAwx9cLckY0OTkZ9+7dQ5UqVWBmZlagOEh9S5cuxdGjR/Hnn39qfdmurq4YN24cxo0bp/Vl6xvu70RElBUvzRdQs2bNsHXr1lxf52V43VOpUiVMnz5d6jCIiIgoCyaiBXTx4kU4OjpKHQYVQN++faUOgYiIiHJQqhLR169f486dO8rn9+7dw6VLl2BrawtnZ2dMnz4dMTEx2Lx5MwBg+fLlcHV1Rd26dZGamoqtW7di165d2LVrl1RdIB0TFRUldQhEREQlVqlKRCMiIlRGvE+YMAEAMHjwYGzatAmxsbGIjo5Wvp6amopJkyYhJiYGcrkcdevWxcGDB9GpU6dij52IiIhI35TawUq6gIM3qDTh/k5ERFlx+iYiIiIikgQTUSIiIiKSBBNRIiIiIpIEE1EiIiIikgQTUSrxNm3ahDJlykgdRoG4urpi+fLlKmXHjx9HrVq1oFAopAlKQ5MmTcIXX3whdRhERFQCMRGlAsvIyEDz5s3Rq1cvlfJXr16hcuXKmDlzZp7tQ0JCIJPJ8PLlS63E8/HHH+PWrVsatfX19cW0adMAvP3Z18yHlZUVGjdujN27d2slRnVMmTIFX331FQwMCv+x1PZ7DLydM1Umk+HSpUsq5VOmTMHGjRtx7949ra2LiIhKByaiekIoFEiPikLq1atIj4qCKMKzaoaGhggICEBQUBC2bdumLB87dixsbW0xa9YsrawnNTVVrXpyuRz29vYFXr5CocDBgwfx0UcfKcs2btyI2NhYhIeHo379+ujTpw/CwsIKFZ86/vrrL9y+fRt9+vTR2jKLi729Pdq3b481a9ZIHQoREZUwTET1QFpkJBJWrEBiQACSdu9GYkAAElasQFpkZJGts0aNGvD398fYsWPx6NEj7Nu3Dzt27EBAQABMTExybRcVFaX8UYGyZctCJpPB19cXANC6dWuMGTMGEyZMQLly5dCuXTsAwNKlS1GvXj1YWFigcuXKGDVqFF6/fq1cZtZL83PmzEGDBg2wZcsWuLq6wsbGBv369UNCQoJKLGfOnIGBgQGaNm2qLCtTpgwcHBxQq1YtrFmzBmZmZggMDATw9nL6N998A19fX9jY2GD48OEA3iaRLVu2hFwuR+XKlfHFF18gMTFRucy4uDh07doVcrkcVapUUUneM+3YsQPt27dXzq8ZFRUFAwMDREREqNT74Ycf4OLigrym/83rPRZCYNGiRahatSrkcjnq16+PP/74Q9n2xYsXGDBgAMqXLw+5XI4aNWpg48aNAIAqVaoAABo2bAiZTIbWrVsr23Xr1g3bt2/PNSYiIqKcMBEt4dIiI/Fm506I+HiVchEfjzc7dxZpMjp27FjUr18fgwYNwueff45Zs2ahQYMGebapXLmy8idSb968idjYWKxYsUL5ekBAAIyMjHDmzBmsXbsWAGBgYICVK1fi2rVrCAgIwPHjxzFlypQ81/Pvv/9i7969OHDgAA4cOIDQ0FAsWLBApU5gYCC6du2a66VwY2NjGBkZIS0tTVn2/fffw93dHefPn8fXX3+Nq1evokOHDujZsyeuXLmC3377DadPn8aYMWOUbXx9fREVFYXjx4/jjz/+wKpVqxAXF6eyrpMnT6Jx48bK566urvD29lYmgZk2btwIX19fyGSyXPue13s8c+ZMbNy4EatXr8b169cxfvx4fPrppwgNDQUAfP3117hx4wYOHTqEyMhIrF69GuXKlQMA/P333wCAo0ePIjY2VuW2hSZNmuDBgwe4f/9+rnERERFlVap+4lPfCIUCSUFBedZJCgqCkZsbZFq47zArmUyG1atXo3bt2qhXr57yXsu8GBoawtbWFsDbS7pZBxlVr14dixYtUikbN26c8u8qVapg/vz5GDlyJFatWpXrehQKBTZt2gQrKysAwMCBA3Hs2DF8++23yjqBgYFYvHhxju1TUlLw/fffIz4+Hm3btlWWt2nTBpMmTVI+HzRoED755BNljDVq1MDKlSvRqlUrrF69GtHR0Th06BDOnj2rPPO6fv161K5dW2V9UVFRcHJyUikbNmwY/Pz8sHTpUpiamuLy5cu4dOlSvvet5vYeJyYmYunSpTh+/Dg8PT0BAFWrVsXp06exdu1atGrVCtHR0WjYsKEyKXZ1dVUut3z58gAAOzs7ODg4qKyzYsWKyn64uLjkGR8REVEmJqIlWEZ0dLYzoVmJ+HhkREfD6J2EQps2bNgAc3Nz3Lt3Dw8fPlRJXDTx7lnBTCdOnMB3332HGzduID4+Hunp6UhOTkZiYiIsLCxyXI6rq6syCQUAR0dHlbOQkZGRePjwIby9vVXa9e/fH4aGhkhKSoKNjQ0WL16Mjh075hrf+fPncefOHZXL7UIIKBQK3Lt3D7du3YKRkZFKu1q1amVLwJOSkrL97GX37t0xZswY7NmzB/369cOGDRvg5eWl8Xt848YNJCcnK295yJSamoqGDRsCAEaOHIlevXrhwoULaN++Pbp3747mzZvnu2y5XA4AePPmjUaxERFR6cRL8yWYIss9j4WtV1BhYWFYtmwZ9u3bB09PTwwdOjTPexfVkTWxvH//Pjp16gR3d3fs2rUL58+fx08//QQAKpfMszI2NlZ5LpPJVKZFCgwMRLt27ZQJVKZly5bh0qVLiI2NxfPnzzFx4sQ841MoFBgxYgQuXbqkfFy+fBm3b99GtWrVlO9HXpfSAaBcuXJ48eKFSpmJiQkGDhyIjRs3IjU1Fb/++is+++yzPJeTl8z+Hzx4UCXeGzduKO8T7dixI+7fv49x48bh0aNHaNu2rcoZ4Nw8f/4cwP/OmhIREamDZ0RLMIN3zvhpo15BJCUlYfDgwRgxYgS8vb1Rs2ZNuLu7Y+3atfDz88uzbeZgpoyMjHzXExERgfT0dCxZskR5L+fOnTsLHf++ffswbNiwbOUODg6oXr262stp1KgRrl+/nmub2rVrIz09HREREWjSpAmAt/dtZp1WqWHDhrhx40a29sOGDYO7uztWrVqFtLQ09OzZU624cnqP69SpA1NTU0RHR6NVq1a5ti1fvjx8fX3h6+uLDz/8EJMnT8bixYvz3G7Xrl2DsbEx6tatq1Z8REREAM+IlmiGzs6QWVvnWUdmbQ1DZ2etr3vatGlQKBRYuHAhAMDZ2RlLlizB5MmTERUVlWdbFxcXyGQyHDhwAE+fPlUZAZ9VtWrVkJ6ejh9++AF3797Fli1bCj1NUFxcHMLDw9GlS5dCLQcApk6dirCwMIwePRqXLl3C7du3ERgYiLFjxwIA3Nzc4OPjg+HDh+PcuXM4f/48hg0blu1MbIcOHXD69Olsy69duzaaNWuGqVOnon///tna5San99jKygqTJk3C+PHjERAQgH///RcXL17ETz/9hICAAADArFmzsG/fPty5cwfXr1/HgQMHlPez2tvbQy6XIygoCE+ePMGrV6+U6zt16hQ+/PBDteMjIiICmIiWaDIDA8h9fPKsI/fx0fpApdDQUPz000/YtGmTyqXq4cOHo3nz5vleoq9YsSLmzp2LadOmoUKFCiojzLNq0KABli5dioULF8Ld3R3btm2Dv79/oeLfv38/mjZtqtHco1l5eHggNDQUt2/fxocffoiGDRvi66+/hqOjo7LOxo0bUblyZbRq1Qo9e/bE559/nm3dn376KW7cuIGbN29mW8fQoUORmppaoMvyub3H8+fPx6xZs+Dv74/atWujQ4cO2L9/v3JqJhMTE0yfPh0eHh5o2bIlDA0NsWPHDgCAkZERVq5cibVr18LJyUll/tXt27crp7MiIiJSl0wU9qY+0lhycjLu3buHKlWqZBuoUhBpkZFICgpSGbgks7aG3McHxllGZ9PbOS9btGiR7xRQxW3KlCl49eqVctqqTN9++y127NiBq1evShRZ3g4ePIjJkyfjypUrMDLK/W4fbe3vRESkP3iPqB4wrl0bRm5uyIiOhiIhAQZWVm8v2xfBlE36oEWLFujfv7/UYWTz1Vdf4aeffkJGRgYMDQ3x+vVrREZG4ocffsD8+fOlDi9XiYmJ2LhxY55JKBERUU54RlRC+nqGyM/PD1u3bs3xtU8//ZQ/BakmX19fbN++Hd27d8evv/4KQ0ND5Wsl8T3W1/2diIg0x0RUQvp6YI6Li0N8LvObWltba+XezNKuJL7H+rq/ExGR5ngtjbTO3t5eJxMhfcL3mIiI9AFvIiQiIiIiSTARJSIiIiJJMBElIiIiIkkwESUiIiIiSTARJSIiIiJJMBGlEm/Tpk0oU6aM1GFolUwmw969e6UOg4iIqEgxEaUCy8jIQPPmzdGrVy+V8levXqFy5cqYOXNmnu1DQkIgk8nw8uVLrcTz8ccf49atWxq19fX1xbRp0wodg6urK5YvX17o5WSKjY1Fx44dtbY8IiIiXcRElArM0NAQAQEBCAoKwrZt25TlY8eOha2tLWbNmqWV9aSmpqpVTy6XazSnpkKhwMGDB/HRRx8VuK0mMjIyoFAo1Krr4OAAU1PTIo6IiIhIWkxESSM1atSAv78/xo4di0ePHmHfvn3YsWMHAgICYGJikmu7qKgoeHl5AQDKli0LmUwGX19fAEDr1q0xZswYTJgwAeXKlUO7du0AAEuXLkW9evVgYWGBypUrY9SoUXj9+rVymVkvzc+ZMwcNGjTAli1b4OrqChsbG/Tr1w8JCQkqsZw5cwYGBgZo2rQp2rRpgzFjxqi8/uzZM5iamuL48eN5vhetW7fG/fv3MX78eMhkMshkMpW4Dhw4gDp16sDU1BT3799HeHg42rVrh3LlysHGxgatWrXChQsXVJb57qX5qKgoyGQy7N69G15eXjA3N0f9+vURFhaWZ1xERES6jomojhFCIP3NG0keBf2117Fjx6J+/foYNGgQPv/8c8yaNQsNGjTIs03lypWxa9cuAMDNmzcRGxuLFStWKF8PCAiAkZERzpw5g7Vr1wIADAwMsHLlSly7dg0BAQE4fvw4pkyZkud6/v33X+zduxcHDhzAgQMHEBoaigULFqjUCQwMRNeuXWFgYIBhw4bh119/RUpKivL1bdu2wcnJSZk452b37t2oVKkS5s2bh9jYWMTGxipfe/PmDfz9/bFu3Tpcv34d9vb2SEhIwODBg3Hq1CmcPXsWNWrUQKdOnbIlyll99dVXmDRpEi5duoSaNWuif//+SE9Pz7MNERGRLuNPfOqYjKQkHKpXT5J1d7x6FUbm5mrXl8lkWL16NWrXro169eqpda+loaEhbG1tAbz9mcqsg4yqV6+ORYsWqZSNGzdO+XeVKlUwf/58jBw5EqtWrcp1PQqFAps2bYKVlRUAYODAgTh27Bi+/fZbZZ3AwEAsXrwYANCrVy+MHTsW+/btQ9++fQEAGzduhK+vr/IMZ25sbW1haGgIKysrODg4qLyWlpaGVatWoX79+sqyNm3aqNRZu3YtypYti9DQUHTp0iXX9UyaNAmdO3cGAMydOxd169bFnTt3UKtWrTzjo6IhFApkREdDkZAAAysrGDo7Q2bA/+1LEm5DIumVqk/cyZMn0bVrVzg5Oak9Kjk0NBTvvfcezMzMULVqVaxZs6boAy1BNmzYAHNzc9y7dw8PHz4s9PIaN26crezEiRNo164dKlasCCsrKwwaNAjPnj1DYmJirstxdXVVJqEA4OjoiLi4OOXzyMhIPHz4EN7e3gAAU1NTfPrpp9iwYQMA4NKlS7h8+bLytgFNmZiYwMPDQ6UsLi4Ofn5+qFmzJmxsbGBjY4PXr18jOjo6z2W9uxxHR0flsqj4pUVGImHFCiQGBCBp924kBgQgYcUKpEVGSh0aqYnbkEg3lKozoomJiahfvz6GDBmSbcR3Tu7du4dOnTph+PDh2Lp1K86cOYNRo0ahfPnyarXXhKFcjo5Xr+b6uiIpCYo8RpsblCkDA7lc43UXRFhYGJYtW4ZDhw5h0aJFGDp0KI4ePZrvGcS8WFhYqDy/f/8+OnXqBD8/P8yfPx+2trY4ffo0hg4dirS0tFyXY2xsrPJcJpOpDBQKDAxEu3btIH+nz8OGDUODBg3w8OFDbNiwAW3btoWLi4vGfQHeDqTK+n74+vri6dOnWL58OVxcXGBqagpPT898B2e926fMZao7+Im0Jy0yEm927sxWLuLj8WbnTpj37Qvj2rUliIzUxW1IpDtKVSLasWPHAk2Js2bNGjg7Oyun5alduzYiIiKwePHiIktEZTJZrpfHhRCQJSTkmWjK0tNhkEPyo21JSUkYPHgwRowYAW9vb9SsWRPu7u5Yu3Yt/Pz88mybOZgpIyMj3/VEREQgPT0dS5YsgcH/XzLbmcMBpKD27duHYcOGqZTVq1cPjRs3xi+//IJff/0VP/zwg9rLMzExUas/AHDq1CmsWrUKnTp1AgA8ePAA//33n/rBk2SEQoGkoKA86yQFBcHIzY2XeHUUtyGRbuGnLA9hYWFo3769SlmHDh0QERGR69m4lJQUxMfHqzzeHQBTKKmpEPkkOyIjA1Bz2qPCmDZtGhQKBRYuXAgAcHZ2xpIlSzB58mRERUXl2dbFxQUymQwHDhzA06dPVUbAZ1WtWjWkp6fjhx9+wN27d7Fly5ZC3x4RFxeH8PDwHO/HHDZsGBYsWICMjAz06NFD7WW6urri5MmTiImJyTeprF69OrZs2YLIyEicO3cOAwYMUDkzS7orIzoaIj4+zzoiPh4Z+dxmQdLhNiTSLUxE8/D48WNUqFBBpaxChQpIT0/PNdnw9/dX3veX+fD399dKPPkloQWtp6nQ0FD89NNP2LRpk8ql9OHDh6N58+YYOnRoniPwK1asiLlz52LatGmoUKFCtmmT3tWgQQMsXboUCxcuhLu7O7Zt21bo93P//v1o2rRpjnOP9u/fH0ZGRvjkk09gZmam9jLnzZuHqKgoVKtWDeXLl8+z7oYNG/DixQs0bNgQAwcOxBdffKHRPKhU/BT5zGxQ0HpU/LgNiXSLTBR0zh49IZPJsGfPHnTv3j3XOjVr1sSQIUMwffp0ZdmZM2fQokULxMbGZhshDbw9I5r1DKipqWmOk5MnJyfj3r17qFKlilpJj0hJQcazZ/nWM7Szg4yToeeqW7duaNGiRY5TQD148ACurq4IDw9Ho0aNJIhOfxV0f9dF6VFRSAwIyLeexeDBMHJ1LfqAqMC4DYl0S6m6R7SgHBwc8PjxY5WyuLg4GBkZwc7OLsc2uSWdWmFiApmhYZ5nPGWGhkAeE8oT0KJFC/Tv31+lLC0tDbGxsZg2bRqaNWvGJJRyZOjsDJm1dZ6XdmXW1jB0di7GqKgguA2JdAsvzefB09MTwcHBKmVHjhxB48aNs43KLg4ymQwya+u861hbF/lApfz4+fnB0tIyx0d+A5mKw5QpU1C5cmWVsjNnzsDFxQXnz5/Pdg/qqVOncu2PpaVlcYZOEpMZGEDu45NnHbmPDwe56DBuQyLdUqouzb9+/Rp37twBADRs2BBLly6Fl5cXbG1t4ezsjOnTpyMmJgabN28G8Hb6Jnd3d4wYMQLDhw9HWFgY/Pz8sH37dq2Mmtf0UqUiKQkiPl7lzKjM0BAya2uNp27Spri4OMTncrbB2tq6xN0PmZSUhJiYmFxfr169ejFGU3Lpw6X5TGmRkUgKClI5qyaztobcx4fT/pQQ3IZEuqFUJaIhISE5/lzj4MGDsWnTJvj6+iIqKgohISHK10JDQzF+/Hhcv34dTk5OmDp1qtbO6hXmwCyEUI6iz7wcL/WZUKK86FMiCvBXefQBtyGR9EpVIqpr9O3ATJQX7u9ERJQV//XTAfxfgEoD7udERJQVE1EJZQ54evPmjcSREBW9zP1cioF+RESkmzh9k4QMDQ1RpkwZxMXFAQDMzc15nyfpHSEE3rx5g7i4OJQpUwaGhoZSh0RERDqC94hKTAiBx48f4+XLl1KHQlSkypQpAwcHB/6zRURESkxEdURGRkauv19PVNIZGxvzTCgREWXDRJSIiIiIJMHBSkREREQkCSaiRERERCQJJqJEREREJAkmokREREQkCSaiJVhKSgrmzJmDlJQUqUMpEvreP0D/+8j+lXz63kf2r+QrDX3UZxw1X4LFx8fDxsYGr169grW1tdThaJ2+9w/Q/z6yfyWfvveR/Sv5SkMf9RnPiBIRERGRJJiIEhEREZEkmIgSERERkSSYiJZgpqammD17NkxNTaUOpUjoe/8A/e8j+1fy6Xsf2b+SrzT0UZ9xsBIRERERSYJnRImIiIhIEkxEiYiIiEgSTESJiIiISBJMRImIiIhIEkxES4CTJ0+ia9eucHJygkwmw969e1Ve3717Nzp06IBy5cpBJpPh0qVLksSpqbz6l5aWhqlTp6JevXqwsLCAk5MTBg0ahEePHkkXcAHlt/3mzJmDWrVqwcLCAmXLloW3tzfOnTsnTbAayq+P7xoxYgRkMhmWL19ebPEVVn798/X1hUwmU3k0a9ZMmmA1oM72i4yMRLdu3WBjYwMrKys0a9YM0dHRxR+shvLrY9btl/n4/vvvpQm4gPLr3+vXrzFmzBhUqlQJcrkctWvXxurVq6UJVgP59e/Jkyfw9fWFk5MTzM3N4ePjg9u3b0sTLBUIE9ESIDExEfXr18ePP/6Y6+sffPABFixYUMyRaUde/Xvz5g0uXLiAr7/+GhcuXMDu3btx69YtdOvWTYJINZPf9qtZsyZ+/PFHXL16FadPn4arqyvat2+Pp0+fFnOkmsuvj5n27t2Lc+fOwcnJqZgi0w51+ufj44PY2Fjl488//yzGCAsnv/79+++/aNGiBWrVqoWQkBBcvnwZX3/9NczMzIo5Us3l18d3t11sbCw2bNgAmUyGXr16FXOkmsmvf+PHj0dQUBC2bt2KyMhIjB8/HmPHjsW+ffuKOVLN5NU/IQS6d++Ou3fvYt++fbh48SJcXFzg7e2NxMRECaKlAhFUogAQe/bsyfG1e/fuCQDi4sWLxRqTNuXVv0x///23ACDu379fPEFpkTr9e/XqlQAgjh49WjxBaVlufXz48KGoWLGiuHbtmnBxcRHLli0r9ti0Iaf+DR48WHz00UeSxKNtOfXv448/Fp9++qk0ARUBdT6HH330kWjTpk3xBKRlOfWvbt26Yt68eSpljRo1EjNnzizGyLQja/9u3rwpAIhr164py9LT04Wtra345ZdfJIiQCoJnRKnEefXqFWQyGcqUKSN1KFqXmpqKn3/+GTY2Nqhfv77U4WiNQqHAwIEDMXnyZNStW1fqcIpESEgI7O3tUbNmTQwfPhxxcXFSh6QVCoUCBw8eRM2aNdGhQwfY29ujadOmed5+UdI9efIEBw8exNChQ6UORWtatGiBwMBAxMTEQAiBEydO4NatW+jQoYPUoRVaSkoKAKicoTc0NISJiQlOnz4tVVikJiaiVKIkJydj2rRp+OSTT2BtbS11OFpz4MABWFpawszMDMuWLUNwcDDKlSsndVhas3DhQhgZGeGLL76QOpQi0bFjR2zbtg3Hjx/HkiVLEB4ejjZt2igPkCVZXFwcXr9+jQULFsDHxwdHjhxBjx490LNnT4SGhkodXpEICAiAlZUVevbsKXUoWrNy5UrUqVMHlSpVgomJCXx8fLBq1Sq0aNFC6tAKrVatWnBxccH06dPx4sULpKamYsGCBXj8+DFiY2OlDo/yYSR1AETqSktLQ79+/aBQKLBq1Sqpw9EqLy8vXLp0Cf/99x9++eUX9O3bF+fOnYO9vb3UoRXa+fPnsWLFCly4cAEymUzqcIrExx9/rPzb3d0djRs3houLCw4ePFjikxmFQgEA+OijjzB+/HgAQIMGDfDXX39hzZo1aNWqlZThFYkNGzZgwIABJeoe2PysXLkSZ8+eRWBgIFxcXHDy5EmMGjUKjo6O8Pb2ljq8QjE2NsauXbswdOhQ2NrawtDQEN7e3ujYsaPUoZEaeEaUSoS0tDT07dsX9+7dQ3BwsF6dDQUACwsLVK9eHc2aNcP69ethZGSE9evXSx2WVpw6dQpxcXFwdnaGkZERjIyMcP/+fUycOBGurq5Sh1ckHB0d4eLiohejdsuVKwcjIyPUqVNHpbx27dolatS8uk6dOoWbN29i2LBhUoeiNUlJSZgxYwaWLl2Krl27wsPDA2PGjMHHH3+MxYsXSx2eVrz33nu4dOkSXr58idjYWAQFBeHZs2eoUqWK1KFRPnhGlHReZhJ6+/ZtnDhxAnZ2dlKHVOSEEHpxWRcABg4cmO2MS4cOHTBw4EAMGTJEoqiK1rNnz/DgwQM4OjpKHUqhmZiY4P3338fNmzdVym/dugUXFxeJoio669evx3vvvadX92inpaUhLS0NBgaq554MDQ2VZ7z1hY2NDQDg9u3biIiIwPz58yWOiPLDRLQEeP36Ne7cuaN8fu/ePVy6dAm2trZwdnbG8+fPER0drZxbM/OA4eDgAAcHB0liLoi8+ufk5ITevXvjwoULOHDgADIyMvD48WMAgK2tLUxMTKQKW2159c/Ozg7ffvstunXrBkdHRzx79gyrVq3Cw4cP0adPHwmjLpj89tGs/zwYGxvDwcEBbm5uxR2qRvLqn62tLebMmYNevXrB0dERUVFRmDFjBsqVK4cePXpIGLX68tt+kydPxscff4yWLVvCy8sLQUFB2L9/P0JCQqQLuoDy6yMAxMfH4/fff8eSJUukClNj+fWvVatWmDx5MuRyOVxcXBAaGorNmzdj6dKlEkatvvz69/vvv6N8+fJwdnbG1atX8eWXX6J79+5o3769hFGTWqQetk/5O3HihACQ7TF48GAhhBAbN27M8fXZs2dLGre68upf5pRUOT1OnDghdehqyat/SUlJokePHsLJyUmYmJgIR0dH0a1bN/H3339LHXaB5LePZlXSpm/Kq39v3rwR7du3F+XLlxfGxsbC2dlZDB48WERHR0sdttrU2X7r168X1atXF2ZmZqJ+/fpi79690gWsAXX6uHbtWiGXy8XLly+lC1RD+fUvNjZW+Pr6CicnJ2FmZibc3NzEkiVLhEKhkDZwNeXXvxUrVohKlSopP4MzZ84UKSkp0gZNapEJIYT20loiIiIiIvVwsBIRERERSYKJKBERERFJgokoEREREUmCiSgRERERSYKJKBERERFJgokoEREREUmCiSgRERERSYKJKBFRPkJCQiCTyfDy5UupQyEi0itMRImI/t9ff/0FQ0ND+Pj45Flv06ZNKFOmTPEERUSkx5iIEhH9vw0bNmDs2LE4ffo0oqOji3x9GRkZUCgURb4eIiJdxUSUiAhAYmIidu7ciZEjR6JLly7YtGlTjvVCQkIwZMgQvHr1CjKZDDKZDHPmzAEApKamYsqUKahYsSIsLCzQtGlThISEKNtmnkk9cOAA6tSpA1NTU9y/f7/oO0dEpKOYiBIRAfjtt9/g5uYGNzc3fPrpp9i4cSOEENnqNW/eHMuXL4e1tTViY2MRGxuLSZMmAQCGDBmCM2fOYMeOHbhy5Qr69OkDHx8f3L59W9n+zZs38Pf3x7p163D9+nXY29sXWx+JiHSNkdQBEBHpgvXr1+PTTz8FAPj4+OD169c4duwYvL29VeqZmJjAxsYGMpkMDg4OyvJ///0X27dvx8OHD+Hk5AQAmDRpEoKCgrBx40Z89913AIC0tDSsWrUK9evXL6aeERHpLiaiRFTq3bx5E3///Td2794NADAyMsLHH3+MDRs2ZEtEc3PhwgUIIVCzZk2V8pSUFNjZ2Smfm5iYwMPDQ3vBExGVYExEiajUW79+PdLT01GxYkVlmRACxsbGePHihVrLUCgUMDQ0xPnz52FoaKjymqWlpfJvuVwOmUymncCJiEo4JqJEVKqlp6dj8+bNWLJkCdq3b6/yWq9evbBt2za4u7urlJuYmCAjI0OlrGHDhsjIyEBcXBw+/PDDIo+biEgfMBElolLtwIEDePHiBYYOHQobGxuV13r37o3169dj2bJlKuWurq7Ke0jr168Pc3Nz1KxZEwMGDMCgQYOwZMkSNGzYEP/99x+OHz+OevXqoVOnTsXZLSKiEoGj5omoVFu/fj28vb2zJaHA2zOily5dwoULF1TKmzdvDj8/P3z88ccoX748Fi1aBADYuHEjBg0ahIkTJ8LNzQ3dunXDuXPnULly5WLpCxFRSSMTOc1PQkRERERUxHhGlIiIiIgkwUSUiIiIiCTBRJSIiIiIJMFElIiIiIgkwUSUiIiIiCTBRJSIiIiIJMFElIiIiIgkwUSUiIiIiCTBRJSIiIiIJMFElIiIiIgkwUSUiIiIiCTBRJSIiIiIJPF/FB5tBWac4mYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Linear regression of Frage 2\n",
    "# Splitting variables\n",
    "X = df.iloc[:,:1]\n",
    "y = df.iloc[:,2:3]\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "#print(X_train, y_train)\n",
    "\n",
    "#Linear Regression Model\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# Prediction result\n",
    "y_pred_test = regressor.predict(X_test)     # predicted value of y_test\n",
    "y_pred_train = regressor.predict(X_train)   # predicted value of y_train\n",
    "\n",
    "#print(X_train, y_train)\n",
    "\n",
    "# Prediction on training set\n",
    "plt.scatter(X_train, y_train, color = 'lightcoral')\n",
    "plt.plot(X_train, y_pred_train, color = 'firebrick')\n",
    "plt.title('Alter vs Frage_2 (Training Set)(Ich habe was neues gelernt an der Ausstellung)')\n",
    "plt.xlabel('Alter')\n",
    "plt.ylabel('Feedback Werte')\n",
    "plt.legend(['X_train/Pred(y_test)', 'X_train/y_train'], title = 'Sal/Exp', loc='best', facecolor='white')\n",
    "plt.box(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2b43e1a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution plot Frage 3\n",
    "plt.title('Frage 3 Plot')\n",
    "sns.distplot(df['Frage_3'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0a8a16e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Linear regression of Frage 3\n",
    "# Splitting variables\n",
    "X = df.iloc[:,:1]\n",
    "y = df.iloc[:,3:4]\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "#print(X_train, y_train)\n",
    "\n",
    "#Linear Regression Model\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# Prediction result\n",
    "y_pred_test = regressor.predict(X_test)     # predicted value of y_test\n",
    "y_pred_train = regressor.predict(X_train)   # predicted value of y_train\n",
    "\n",
    "#print(X_train, y_train)\n",
    "\n",
    "# Prediction on training set\n",
    "plt.scatter(X_train, y_train, color = 'lightcoral')\n",
    "plt.plot(X_train, y_pred_train, color = 'firebrick')\n",
    "plt.title('Alter vs Frage_3 (Training Set)(Ich würde nochmal zur Ausstellung kommen)')\n",
    "plt.xlabel('Alter')\n",
    "plt.ylabel('Feedback Werte')\n",
    "plt.legend(['X_train/Pred(y_test)', 'X_train/y_train'], title = 'Sal/Exp', loc='best', facecolor='white')\n",
    "plt.box(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8600bcff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution Polt Frage 4\n",
    "plt.title('Frage 4 Plot')\n",
    "sns.distplot(df['Frage_4'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "98e7dd40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Linear regression of Frage 4\n",
    "# Splitting variables\n",
    "X = df.iloc[:,:1]\n",
    "y = df.iloc[:,4:5]\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "#print(X_train, y_train)\n",
    "\n",
    "#Linear Regression Model\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# Prediction result\n",
    "y_pred_test = regressor.predict(X_test)     # predicted value of y_test\n",
    "y_pred_train = regressor.predict(X_train)   # predicted value of y_train\n",
    "\n",
    "#print(X_train, y_train)\n",
    "\n",
    "# Prediction on training set\n",
    "plt.scatter(X_train, y_train, color = 'lightcoral')\n",
    "plt.plot(X_train, y_pred_train, color = 'firebrick')\n",
    "plt.title('Alter vs Frage_4 (Training Set)(Die Ausstellung hat meine Interesse an Robotik erweckt)')\n",
    "plt.xlabel('Alter')\n",
    "plt.ylabel('Feedback Werte')\n",
    "plt.legend(['X_train/Pred(y_test)', 'X_train/y_train'], title = 'Sal/Exp', loc='best', facecolor='white')\n",
    "plt.box(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "39876ea7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Satisfaction Rate Plot')\n",
    "sns.distplot(df['Sastisfaction_Rate'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "16865772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Linear regression of Satisfacton Rate\n",
    "# Splitting variables\n",
    "X = df.iloc[:,:1]\n",
    "y = df.iloc[:,5:6]\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "#print(X_train, y_train)\n",
    "\n",
    "#Linear Regression Model\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# Prediction result\n",
    "y_pred_test = regressor.predict(X_test)     # predicted value of y_test\n",
    "y_pred_train = regressor.predict(X_train)   # predicted value of y_train\n",
    "\n",
    "#print(X_train, y_train)\n",
    "\n",
    "# Prediction on training set\n",
    "plt.scatter(X_train, y_train, color = 'lightcoral')\n",
    "plt.plot(X_train, y_pred_train, color = 'firebrick')\n",
    "plt.title('Alter vs Satisfaction Rate (Training Set)(Satisfaction Rate)')\n",
    "plt.xlabel('Alter')\n",
    "plt.ylabel('Satisfaction Rate')\n",
    "plt.legend(['X_train/Pred(y_test)', 'X_train/y_train'], title = 'Sal/Exp', loc='best', facecolor='white')\n",
    "plt.box(False)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}